unknown time varying input delay compensation for uncertain nonlinear systems abstract a tracking controller is developed for a class of uncertain nonlinear systems subject to unknown time varying input delay and additive disturbances a novel filtered error signal is designed using the past states in a finite integral over a constant estimated delay interval the maximum tolerable error between unknown time varying delay and a constant estimate of the delay is determined to establish uniformly ultimately bounded convergence of the tracking error to the origin the controller development is based on an approach which uses lyapunov krasovskii functionals to analyze the effects of unknown sufficiently slowly time varying input delays a stability analysis is provided to prove ultimate boundedness of the tracking error signals numerical simulation results illustrate the performance of the developed robust controller

neural network adaptive control for a class of nonlinear uncertain dynamical systems with asymptotic stability guarantees in this paper a neuroadaptive control framework for continuous and discrete time nonlinear uncertain dynamical systems with input to state stable internal dynamics is developed the proposed framework is lyapunov based and unlike standard neural network nn controllers guaranteeing ultimate boundedness the framework guarantees partial asymptotic stability of the closed loop system that is asymptotic stability with respect to part of the closed loop system states associated with the system plant states the neuroadaptive controllers are constructed without requiring explicit knowledge of the system dynamics other than the assumption that the plant dynamics are continuously differentiable and that the approximation error of uncertain system nonlinearities lie in a small gain type norm bounded conic sector this allows us to merge robust control synthesis tools with nn adaptive control tools to guarantee system stability finally two illustrative numerical examples are provided to demonstrate the efficacy of the proposed approach

testing for the constancy of parameters over time abstract tests are proposed for detecting possible changes in parameters when the observations are obtained sequentially in time while deriving the tests the alternative one has in mind specifies the parameter process as a martingale the distribution theory of these tests relies on the large sample results that is only the limiting null distributions are known except in very special cases the main tool in establishing these limiting distributions is weak convergence of stochastic processes suppose that we have vector valued observations x 1 x n obtained sequentially in time or ordered in some other linear fashion their joint distribution is described by determining the initial distribution for x 1 and the conditional distribution for each x k given the past up to x k 1 suppose further that these distributions depend on a p dimensional parameter vector at least locally i e in a short time period this may be more or less legitimate in the long run however the possibility of some ch

criteria for asymptotic stability of linear time delay systems several sufficient conditions which guarantee stability of linear time delay systems are derived each of these results is expressed by a succinct scalar inequality and corresponds to a certain extent to the tradeoff between simplicity and sharpness

universal neural network control of mimo uncertain nonlinear systems in this brief a continuous tracking control law is proposed for a class of high order multi input multi output uncertain nonlinear dynamic systems with external disturbance and unknown varying control direction matrix the proposed controller consists of high gain feedback nussbaum gain matrix selector online approximator ola model and a robust term the ola model is represented by a two layer neural network the continuousness of the control signal is guaranteed to relax the requirement for the actuator bandwidth and avoid the incurred chattering effect asymptotic tracking performance is achieved theoretically by standard lyapunov analysis the control feasibility is also verified in simulation environment

nonlinear decision weights in choice under uncertainty in most real world decisions consequences are tied explicitly to the outcome of events previous studies of decision making under uncertainty have indicated that the psychological weight attached to an event called a decision weight usually differs from the probability of that event we investigate two sources of nonlinearity of decision weights subadditivity of probability judgments and the overweighting of small probabilities and underweighting of medium and large probabilities these two sources of nonli nearity are combined into a two stage model of choice under uncertainty in the first stage events are taken into subjective probability judgments and the second stage takes probability judgments into decision weights we then characterize the curvature of the decision weights by extending a condition employed by wu and gonzalez 1996 in the domain of risk to the domain of uncertainty and show that the nonlinearity of decision weights can be decomposed into subadditivity of probability judgments and the curvature of the probability weighting function empirical tests support the proposed two stage model and indicate that decision weights are concave then convex more specifically our results lend support for a new property of subjective probability ju dgments interior additivity subadditive at the boundaries but additive away from the boundaries and show that the probability weighting function is inverse s shaped as in wu and gonzalez 1996

moving horizon state estimation for nonlinear systems using neural networks moving horizon mh state estimation is addressed for nonlinear discrete time systems affected by bounded noises acting on system and measurement equations by minimizing a sliding window least squares cost function such a problem is solved by searching for suboptimal solutions for which a certain error is allowed in the minimization of the cost function nonlinear parameterized approximating functions such as feedforward neural networks are employed for the purpose of design thanks to the offline optimization of the parameters the resulting mh estimation scheme requires a reduced online computational effort simulation results are presented to show the effectiveness of the proposed approach in comparison with other estimation techniques

testing for measurement invariance with respect to an ordinal variable researchers are often interested in testing for measurement invariance with respect to an ordinal auxiliary variable such as age group income class or school grade in a factor analytic context these tests are traditionally carried out via a likelihood ratio test statistic comparing a model where parameters differ across groups to a model where parameters are equal across groups this test neglects the fact that the auxiliary variable is ordinal and it is also known to be overly sensitive at large sample sizes in this paper we propose test statistics that explicitly account for the ordinality of the auxiliary variable resulting in higher power against monotonic violations of measurement invariance and lower power against non monotonic ones the statistics are derived from a family of tests based on stochastic processes that have recently received attention in the psychometric literature the statistics are illustrated via an application involving real data and their performance is studied via simulation

asymptotic optimal control of uncertain nonlinear euler lagrange systems a sufficient condition to solve an optimal control problem is to solve the hamilton jacobi bellman hjb equation however finding a value function that satisfies the hjb equation for a nonlinear system is challenging for an optimal control problem when a cost function is provided a priori previous efforts have utilized feedback linearization methods which assume exact model knowledge or have developed neural network nn approximations of the hjb value function the result in this paper uses the implicit learning capabilities of the rise control structure to learn the dynamics asymptotically specifically a lyapunov stability analysis is performed to show that the rise feedback term asymptotically identifies the unknown dynamics yielding semi global asymptotic tracking in addition it is shown that the system converges to a state space system that has a quadratic performance index which has been optimized by an additional control element an extension is included to illustrate how a nn can be combined with the previous results experimental results are given to demonstrate the proposed controllers

interior additivity and subjective probability assessment of continuous variables one of the goals of psychological research on subjective probability judgment is to develop prescriptive procedures that can improve such judgments in this paper our aim is to reduce partition dependence a judgmental bias that arises from the particular way in which a state space is partitioned for the purposes of making probability judgments we explore a property of subjective probabilities called interior additivity ia our story begins with a psychological model of subjective probability judgment that exhibits ia the model is a linear combination of underlying support for the event in question and a term that reflects a prior belief that all elements in the state space partition are equally likely the model is consistent with known properties of subjective probabilities such as binary complementarity subadditivity and partition dependence and has several additional properties related to ia we present experimental evidence to support our model the model further suggests a simple prescriptive method based on ia that decision and risk analysts can use to reduce partition dependence and we present preliminary empirical evidence demonstrating the effectiveness of the method

lyapunov based tracking control in the presence of uncertain nonlinear parameterizable friction modeling and compensation for friction effects has been a topic of considerable mainstream interest in motion control research this interest is spawned from the fact that modeling nonlinear friction effects is a theoretically challenging problem and compensating for the effects of friction in a controller has practical ramifications if the friction effects in the system can be accurately modeled there is an improved potential to design controllers that can cancel the effects whereas excessive steady state tracking errors oscillations and limit cycles can result from controllers that do not accurately compensate for friction a tracking controller is developed in this paper for a general euler lagrange system that contains a new continuously differentiable friction model with uncertain nonlinear parameterizable terms to achieve the semi global asymptotic tracking result a recently developed integral feedback compensation strategy is used to identify the friction effects online assuming exact model knowledge of the remaining dynamics a lyapunov based stability analysis is provided to conclude the tracking and friction identification results experimental results illustrate the tracking and friction identification performance of the developed controller

robust adaptive asymptotic tracking of nonlinear systems with additive disturbance this note deals with the tracking control of multiple input multiple output mimo nonlinear parametric strict feedback systems in the presence of additive disturbances and parametric uncertainties for such systems c sup 0 robust adaptive controllers usually cannot ensure asymptotic tracking or even regulation in this work under the assumption the disturbances are c sup 2 with bounded time derivatives we present a c sup 0 robust adaptive control construction that guarantees the tracking error is asymptotically driven to zero

sliding mode variable structure control for parameter uncertain stochastic systems with time varying delay in this paper the variable structure control problem for a class uncertain of stochastic system with time varying delay is investigated firstly a new concept of the subordinated reachability of the sliding motion is introduced to approach approximately the specified sliding surface the variable structure control law is then proposed to ensure that the sliding motion is subordinated reachable furthermore a sufficient condition for mean square asymptotical stability of the sliding motion is given finally a numerical example is presented to demonstrate the effectiveness of the obtained results

sliding mode control of retarded nonlinear systems via finite spectrum assignment approach in the present study a sliding mode control design method based on the finite spectrum assignment procedure is proposed the finite spectrum assignment for retarded nonlinear systems can transform retarded nonlinear systems into delay free linear systems via a variable transformation and a feedback which contain the past values of the state this method can be considered to be an extension of both the finite spectrum assignment for retarded linear systems with controllability over polynomial rings of the delay operator and the exact linearization for finite dimensional nonlinear systems the proposed method is to design a sliding surface via the variable transformation used in the finite spectrum assignment and to derive a switching feedback law the obtained surface contains not only the current values of the state variables but also the past values of the state variables in the original coordinates the effectiveness of the proposed method is tested by an illustrative example

regular paper model matching for linear systems with delays and 2d systems model matching problem for linear systems with delays is considered via a transfer matrix approach we deal with a general class of neutral delay differential systems which is equivalent to 2d systems from a realizability point of view the solution of the problem is based on the decomposition of the ring of the causal rational functions which is not a principal ideal domain as the intersection of two suitable principal ideal domains

adaptive neural pd control with semiglobal asymptotic stabilization guarantee this paper proves that adaptive neural plus proportional derivative pd control can lead to semiglobal asymptotic stabilization rather than uniform ultimate boundedness for a class of uncertain affine nonlinear systems an integral lyapunov function based ideal control law is introduced to avoid the control singularity problem a variable gain pd control term without the knowledge of plant bounds is presented to semiglobally stabilize the closed loop system based on a linearly parameterized raised cosine radial basis function neural network a key property of optimal approximation is exploited to facilitate stability analysis it is proved that the closed loop system achieves semiglobal asymptotic stability by the appropriate choice of control parameters compared with previous adaptive approximation based semiglobal or asymptotic stabilization approaches our approach not only significantly simplifies control design but also relaxes constraint conditions on the plant two illustrative examples have been provided to verify the theoretical results

robust adaptive control for a class of mimo nonlinear systems by state and output feedback in this technical note high dimensional integral lyapunov functions are introduced for a class of mimo nonlinear systems with unknown nonlinearities first adaptive state feedback control is presented based on the integral lyapunov function when only the output is measurable by using a high gain observer to estimate the derivative of the system output adaptive output feedback control is also derived the proposed control scheme provides a general approach to stabilize the mimo plant without any restrictive assumptions the control is continuous and ensures closed loop stability and convergence of the tracking error to a small residual set the size of the tracking error at steady state can be specified a priori and guaranteed by choosing the design parameters

time varying input and state delay compensation for uncertain nonlinear systems a robust controller is developed for uncertain second order nonlinear systems subject to simultaneous unknown time varying state delays and known sufficiently small time varying input delays in addition to additive sufficiently smooth disturbances an integral term composed of previous control values facilitates a delay free open loop error system and the development of the feedback control structure a stability analysis based on lyapunov krasovskii lk functionals guarantees uniformly ultimately bounded tracking under the assumption that the delays are bounded and slowly varying numerical simulations illustrate robustness of the developed method to various combinations of simultaneous input and state delays

the bounded rationality of probability distortion in decision making under risk dmr participants choices are based on probability values systematically different from those that are objectively correct similar systematic distortions are found in tasks involving relative frequency judgments jrf these distortions limit performance in a wide variety of tasks and an evident question is why do we systematically fail in our use of probability and relative frequency information we propose a bounded log odds model blo of probability and relative frequency distortion based on three assumptions 1 log odds probability and relative frequency are mapped to an internal log odds scale 2 boundedness the range of representations of probability and relative frequency are bounded and the bounds change dynamically with task and 3 variance compensation the mapping compensates in part for uncertainty in probability and relative frequency values we compared human performance in both dmr and jrf tasks to the predictions of the blo model as well as 11 alternative models each missing one or more of the underlying blo assumptions factorial model comparison the blo model and its assumptions proved to be superior to any of the alternatives in a separate analysis we found that blo accounts for individual participants data better than any previous model in the dmr literature we also found that subject to the boundedness limitation participants choice of distortion approximately maximized the mutual information between objective task relevant values and internal values a form of bounded rationality

robust exponential stability of uncertain systems with time varying delays focuses on the problem of robust exponential stability of a class of uncertain systems described by functional differential equations with time varying delays the uncertainties are assumed to be continuous time varying nonlinear and norm bounded sufficient conditions for robust exponential stability are given for both single and multiple delays cases

on the determination of strength of belief for decision support under uncertainty part ii fusing strengths of belief the problem of decision making under uncertainty is described and the difficulty of comparing actions with uncertain outcomes is noted we discuss the role of valuation functions which convert an uncertain outcome into a single representative value and hence aid in the comparison of uncertain alternatives the need for information about the strength of belief that a given outcome will occur is pointed out we then look at various methods for obtaining this information we consider in particular detail the situation in which the strength of belief is dependent on the outcome payoff

robust observer design for ito stochastic time delay systems via sliding mode control this paper deals with the output feedback sliding mode control for ito stochastic time delay systems the system states are unmeasured and the uncertainties are unmatched a sliding mode control scheme is proposed based on the state estimates by utilizing a novel switching function the derivative of the switching function is ensured to be finite variation it is shown that the sliding mode in the estimation space can be attained in finite time the sufficient condition for the asymptotic stability in probability of the overall closed loop stochastic system is derived finally a simulation example is shown to illustrate the proposed method

covariate free and covariate dependent reliability classical test theory reliability coefficients are said to be population specific reliability generalization a meta analysis method is the main procedure for evaluating the stability of reliability coefficients across populations a new approach is developed to evaluate the degree of invariance of reliability coefficients to population characteristics factor or common variance of a reliability measure is partitioned into parts that are and are not influenced by control variables resulting in a partition of reliability into a covariate dependent and a covariate free part the approach can be implemented in a single sample and can be applied to a variety of reliability coefficients

tests for parameter instability and structural change with unknown change point this paper considers tests for parameter instability and structural change with unknown change point the results apply to a wide class of parametric models that are suitable for estimation by generalized method of moments procedures the asymptotic distributions of the test statistics considered here are nonstandard because the change point parameter only appears under the alternative hypothesis and not under the null the tests considered here are shown to have nontrivial asymptotic local power against all alternatives for which the parameters are nonconstant the tests are found to perform quite well in a monte carlo experiment reported elsewhere copyright 1993 by the econometric society

tests of measurement invariance without subgroups a generalization of classical methods the issue of measurement invariance commonly arises in factor analytic contexts with methods for assessment including likelihood ratio tests lagrange multiplier tests and wald tests these tests all require advance definition of the number of groups group membership and offending model parameters in this paper we study tests of measurement invariance based on stochastic processes of casewise derivatives of the likelihood function these tests can be viewed as generalizations of the lagrange multiplier test and they are especially useful for i identifying subgroups of individuals that violate measurement invariance along a continuous auxiliary variable without prespecified thresholds and ii identifying specific parameters impacted by measurement invariance violations the tests are presented and illustrated in detail including an application to a study of stereotype threat and simulations examining the tests abilities in controlled conditions

parameter free elicitation of utility and probability weighting functions this paper proposes a two step method to successively elicit utility functions and decision weights under rank dependent expected utility theory and its more descriptive version cumulative prospect theory the novelty of the method is that it is parameter free and thus elicits the whole individual preference functional without imposing any prior restriction this method is used in an experimental study to elicit individual utility and probability weighting functions for monetary outcomes in the gain and loss domains concave utility functions are obtained for gains and convex utility functions for losses the elicited weighting functions satisfy upper and lower subadditivity and are consistent with previous parametric estimations the data also show that the probability weighting function for losses is more elevated than for gains

the probability weighting function a probability weighting finction w p is a prominent feature of several non expected utility theories including prospect theoiy and rank dependent models empirical esti mates indicate that w p is regressive first w p p then iv p p s shaped first concave then convex and asymmetrical intersecting the diagonal at about 1 3 the paper states axioms for several w p forms including the compound invariant w p exp ln p 1 0 a 1 which is regressive s shaped and with an invariant fixed point and inflection point at i e 37 the common ratio effect allais 1953 refers to the obselvation that the more risky of two simple prospects becomes relatively more attractive when the probability of winning is reduced by equal proportion in both prospects thus a person who prefers a sure gain of 100 000 over a coin toss for 300 000 or nothing might also prefer a one in a million lottery ticket for 300 000 over a two in a million lottery ticket for 100 000 this contradicts expected utility but not common sense there is a world of difference between certainty and a 50 50 shot the difference between one or two chances in a million is negligible the example has all the force and simplicity of classical arguments for nonlinear utility where the utility interval from say 10 and 20 is taken as self evidently greater than the utility interval from 1 000 010 and 1 000 020 both arguments appeal directly to our intuitions about numbers a major point of difference is that the money argument involves a constant money intelval and the probability argument a constant probability ratio a minor difference is that in the money domain a constant interval has less impact as the numbers get larger while in the probability domain a constant ratio has less impact as the numbers get smaller putting the two demonstrations side by side like this raises an interesting question namely whether an account of nonlinearity in probabilities can be somehow adapted from classical utility theory modulo a log transformation converting statements about probability ratios into statements about utility intervals indeed in their seminal paper on prospect theory kahneman and tversky 1979 explained the common ratio effect by means of a nonlinear transformation of probabilities into decision weights p w p with log w

brief paper composite adaptive control for euler lagrange systems with additive disturbances in a typical adaptive update law the rate of adaptation is generally a function of the state feedback error ideally the adaptive update law would also include some feedback of the parameter estimation error the desire to include some measurable form of the parameter estimation error in the adaptation law resulted in the development of composite adaptive update laws that are functions of a prediction error and the state feedback in all previous composite adaptive controllers the formulation of the prediction error is predicated on the critical assumption that the system uncertainty is linear in the uncertain parameters lp uncertainty the presence of additive disturbances that are not lp would destroy the prediction error formulation and stability analysis arguments in previous results in this paper a new prediction error formulation is constructed through the use of a recently developed robust integral of the sign of the error rise technique the contribution of this design and associated stability analysis is that the prediction error can be developed even with disturbances that do not satisfy the lp assumption e g additive bounded disturbances a composite adaptive controller is developed for a general mimo euler lagrange system with mixed structured i e lp and unstructured uncertainties a lyapunov based stability analysis is used to derive sufficient gain conditions under which the proposed controller yields semi global asymptotic tracking experimental results are presented to illustrate the approach

a continuous asymptotic tracking control strategy for uncertain nonlinear systems in this note we present a new continuous control mechanism that compensates for uncertainty in a class of high order multiple input multiple output nonlinear systems the control strategy is based on limited assumptions on the structure of the system nonlinearities a new lyapunov based stability argument is employed to prove semiglobal asymptotic tracking

the rich domain of ambiguity explored ellsberg and others suggested that decision under ambiguity is a rich empirical domain with many phenomena to be investigated beyond the ellsberg urns we provide a systematic empirical investigation of this richness by varying the uncertain events the outcomes and combinations of both although ambiguity aversion is prevailing we also find systematic ambiguity seeking confirming insensitivity we find that ambiguity attitudes depend on the kind of uncertainty the source but not on the kind of outcomes ambiguity attitudes are closer to rationality ambiguity neutrality for natural uncertainties than for artificial ellsberg urn uncertainties this also appears from the reductions of monotonicity violations and of insensitivity ambiguity attitudes have predictive power across different outcomes and sources of uncertainty with individual specific components our rich domain serves well to test families of weighting functions for fitting ambiguity attitudes we find that two parameter families capt

testing and characterizing properties of nonadditive measures through violations of the sure thing principle in expected utility theory risk attitudes are modeled entirely in terms of utility in the rank dependent theories a new dimension is added chance attitude modeled in terms of nonadditive measures or nonlinear probability transformations that are independent of utility most empirical studies of chance attitude assume probabilities given and adopt parametric fitting for estimating the probability transformation only a few qualitative conditions have been proposed or tested as yet usually quasi concavity or quasi convexity in the case of given probabilities this paper presents a general method of studying qualitative properties of chance attitude such as optimism pessimism and the inverse s shape pattern both for risk and for uncertainty these qualitative properties can be characterized by permitting appropriate relatively simple violations of the sure thing principle in particular this paper solves a hitherto open problem the preference axiomatization of convex pessimistic or uncertainty averse nonadditive measures under uncertainty the axioms of this paper preserve the central feature of rank dependent theories i e the separation of chance attitude and utility

fitting multivariage normal finite mixtures subject to structural equation modeling this paper is about fitting multivariate normal mixture distributions subject to structural equation modeling the general model comprises common factor and structural regression models the introduction of covariance and mean structure models reduces the number of parameters to be estimated in fitting the mixture and enables one to investigate a variety of substantive hypotheses concerning the differences between the components in the mixture within the general model individual parameters can be subjected to equality nonlinear and simple bounds constraints confidence intervals are based on the inverse of the hessian and on the likelihood profile several illustrations are given and results of a simulation study concerning the confidence intervals are reported

rise based adaptive control of a control affine uncertain nonlinear system with unknown state delays a continuous robust adaptive control method is designed for a class of uncertain nonlinear systems with unknown constant time delays in the states specifically a robust adaptive control method and a delay free gradient based desired compensation adaptation law dcal are utilized to compensate for unknown time delays linearly parameterizable uncertainties and additive bounded disturbances for a general nonlinear system despite these disturbances a lyapunov krasovskii based analysis is used to conclude that the system output asymptotically tracks a desired time varying bounded trajectory

modular adaptive control of uncertain euler lagrange systems with additive disturbances a novel adaptive nonlinear control design is developed which achieves modularity between the controller and the adaptive update law modularity between the controller update law design provides flexibility in the selection of different update laws that could potentially be easier to implement or used to obtain faster parameter convergence and or better tracking performance for a general class of linear in the parameters lp uncertain euler lagrange systems subject to additive bounded non lp disturbances the developed controller uses a model based feedforward adaptive term in conjunction with the recently developed robust integral of the sign of the error rise feedback term modularity in the adaptive feedforward term is made possible by considering a generic form of the adaptive update law and its corresponding parameter estimate this generic form of the update law is used to develop a new closed loop error system and stability analysis that does not depend on nonlinear damping to yield the modular adaptive control result

robust stability criterion for uncertain stochastic systems with time varying delay via sliding mode control abstract this paper is focused on sliding mode control smc for uncertain stochastic systems with time varying delay both time varying parameter uncertainties and stochastic perturbations may appear in the controlled system a new integral type switching surface function is first constructed then a sufficient condition for the existence of sliding surface is established in terms of linear matrix inequalities lmis by employing some free weighting matrices which guarantees the robustly stochastically stable of the sliding mode dynamic system with time varying delay for all admissible uncertainties moreover a smc law is synthesized to drive the system state trajectories onto the predefined switching surface in a finite time finally a simulation example is provided to illustrate the effectiveness of the proposed theory

memoryless state feedback controller design for time delay systems with matched uncertain nonlinearities the robust control problem for a class of uncertain time delay systems is investigated the systems include multiple time delays and uncertain nonlinear functions to our best knowledge no controller design method for the systems is reported in the literature except for sliding mode control smc however the sliding mode controller must use the delayed state of the system so that the precise time delays have to be known and also a large memory is needed for controller implementation to overcome the shortcomings we employ the backstepping method to design a memoryless state feedback controller by constructing a novel lyapunov krasovskii functional we show that the resulting closed loop system is asymptotically stable simulations are done to verify the effectiveness of the proposed method

integrated design of a linear neuro adaptive controller in the presence of norm bounded uncertainties adaptive neural controllers are often criticised for the lack of clear and easy design methodologies that relate adaptive neural network nn design parameters to performance requirements this study proposes a methodology for the design of an integrated linear adaptive model reference controller that guarantees component wise boundedness of the tracking error within an a priori specified compact domain the approach is based on the design of a robust invariant ellipsoidal set where both the nn reconstruction error and the neuro adaptive control are considered as bounded persistent uncertainties we show that all the performance and control requirements for the closed loop system can be expressed as linear matrix inequality constraints this brings the advantage that feasibility and optimal design parameters can be effectively computed while solving a linear optimisation problem an advantage of the method is that it allows a systematic and quantitative evaluation of the interplay between the design param

the midweight method to measure attitudes toward risk and ambiguity this paper introduces a parameter free method for measuring the weighting functions of prospect theory and rank dependent utility these weighting functions capture risk attitudes subjective beliefs and ambiguity attitudes our method called the midweight method is based on a convenient way to obtain midpoints in the weighting function scale it can be used both for risk known probabilities and for uncertainty unknown probabilities the resulting integrated treatment of risk and uncertainty is particularly useful for measuring ambiguity i e the difference between uncertainty and risk compared to existing methods to measure weighting functions and attitudes toward uncertainty and ambiguity our method is more efficient and can accommodate violations of expected utility under risk an experiment demonstrates the tractability of our method yielding plausible results such as ambiguity aversion for moderate and high likelihoods but ambiguity seeking for low likelihoods as predicted by ellsberg this paper was accepted by george wu decision analysis

adaptive neural control for a class of nonlinearly parametric time delay systems in this paper an adaptive neural controller for a class of time delay nonlinear systems with unknown nonlinearities is proposed based on a wavelet neural network wnn online approximation model a state feedback adaptive controller is obtained by constructing a novel integral type lyapunov krasovskii functional which also efficiently overcomes the controller singularity problem it is shown that the proposed method guarantees the semiglobal boundedness of all signals in the adaptive closed loop systems an example is provided to illustrate the application of the approach

a sufficiently large time delay in feedback loop must destroy exponential stability of any decay rate a linear time invariant system with time delays is considered it is assumed that the delays are nontrivial in the sense that they affect the characteristic equation i e the characteristic quasi polynomial is not a polynomial of one complex variable it is proved that if any of such delays grows to infinity then either the system becomes unstable or at least some of its eigenvalues approach the imaginary axis in the latter case of course the system may become extremely sensitive to changes of other parameters which means practical instability the theoretical results of this paper support well known real world observations concerning the stability of systems with large delays in the feedback loop

the cusum test with ols residuals we show that the cusum test of the stability over time of the coefficients of a linear regression model which is usually based on recursive residuals can also be applied to ordinary least squares residuals we derive the limiting null distribution of the resulting test and compare its local power to that of the standard procedure it turns out that neither version is uniformly superior to the other henceforth bde on the cusum and cusum of squares tests for the constancy over time of the coefficients of a linear regression model both tests are based on recursive residuals which are independent n o o 2 under ho and therefore ideal ingredients for all types of tests while the cusum of squares test has been generalized to ordinary least squares ols residuals mccabe and harrison 1980 a generalization of the cusum test to ols residuals which are dependent and heteroskedastic even under ho has not yet been made except for a very special case macneill 1978 in fact both bde 1975 p 151 and mccabe and harrison 1980 p 142 argue that a least squares variant of the cusum test poses intractable problems due to an alleged difficulty in assessing the significance of the departure of the cumulated ols residuals from their mean value zero below we show that it is no more difficult to derive the limiting distribution for a cusum test based on ols residuals than for a cusum test based on recursive residuals while the cusums of the recursive residuals properly standardized tend in distribution to a standard wiener process sen 1982 krimer ploberger and alt 1988 henceforth kpa we show that the ols based cusums tend in distribution to a brownian bridge or tied down brownian motion see billingsley 1968 p 64 this generalizes macneill s 1978 polynomial regression result to arbitrary regressor sequences we also demonstrate that the resulting cusum test has higher local power for certain types of structural change than the one based on recursive residuals though neither variant is uniformly superior to the other

comments on adaptive neural control for a class of nonlinearly parametric time delay systems in this comment we point out an error in 1 which will show that the main result of the paper cannot be generalized for nonlinearly parametric time delay systems considered in 1 in 1 the authors considered the problem of the adaptive control for a class of nonlinearly parametric time delay systems and applied successfully the proposed approach to second order nonlinear time delay systems with the term x1 t tau x2 t tau used as simulation examples in section vi however we show in this comment that the control approach presented in 1 cannot be generalized although it is realizable for nonlinear second order time delay systems with the term x1 t tau x2 t tau in addition we illustrate this error by one detail example for simplicity all the symbols in this comment are the same as those in 1

robust stability of uncertain time delay systems and its stabilization by variable structure control in this paper two main results for uncertain time delay systems are derived the first result is the presentation of a new robust stability criterion for uncertain time delay systems the second result is the successful application of the concept of variable structure control to the stabilization problem of uncertain time delay systems the robustness and stability degree of perturbed systems in the sliding mode are also discussed last some examples are included to illustrate our results

stabilization of uncertain fuzzy time delay systems via variable structure control approach in view of a recent new application of variable structure control vsc to the stabilization problem for takagi sugeno t s fuzzy models this paper aims to study the stabilization of uncertain fuzzy time delay systems in t s fuzzy model via vsc approach there are mainly two features in this paper one lies in the incorporation of time delays both smooth and nonsmooth delays in which case lyapunov functionals and razumikhin theorem are required to solve the stabilization problem the other feature is that not only matched uncertainties but also mismatched uncertainties in the state variables are considered as a sequence the contribution of this paper consists of various control schemes proposed for the vsc design and the present results are in terms of linear matrix inequalities lmis an illustrative example is given to show the effectiveness of our various results

delay dependent robust stability and stabilization of uncertain linear delay systems a linear matrix inequality approach this paper considers the problems of robust stability analysis and robust control design for a class of uncertain linear systems with a constant time delay the uncertainty is assumed to be norm bounded and appears in all the matrices of the state space model we develop methods for robust stability analysis and robust stabilization the proposed methods are dependent on the size of the delay and are given in terms of linear matrix inequalities

saturated rise feedback control for a class of second order nonlinear systems a saturated controller is developed for a class of uncertain second order nonlinear systems which includes time varying and nonlinearly parameterized functions with bounded disturbances using a continuous control law with smooth saturation functions based on the robust integral of the sign of the error rise control methodology the developed controller is able to utilize the benefits of high gain control strategies while guaranteeing saturation limits are not surpassed the bounds on the control are known a priori and can be adjusted by changing the feedback gains the saturated controller yields asymptotic tracking despite model uncertainty and added disturbances in the dynamics experimental results using a two link robot manipulator demonstrate the performance of the developed controller

range identification for perspective vision systems in this note a new observer is developed to determine range information and hence the three dimensional 3 d coordinates of an object feature moving with affine motion dynamics or the more general ricatti motion dynamics with known motion parameters the unmeasurable range information is determined from a single camera provided an observability condition is satisfied that has physical significance to develop the observer the perspective system is expressed in terms of the nonlinear feature dynamics the structure of the proposed observer is inspired by recent disturbance observer results the proposed technique facilitates a lyapunov based analysis that is less complex than the sliding mode based analysis derived for recent observer designs the analysis demonstrates that the 3 d task space coordinates of the feature point can be asymptotically identified simulation results are provided that illustrate the performance of the observer in the presence of noise

measurement invariance factor analysis and factorial invariance several concepts are introduced and defined measurement invariance structural bias weak measurement invariance strong factorial invariance and strict factorial invariance it is shown that factorial invariance has implications for weak measurement invariance definitions of fairness in employment admissions testing and salary equity are provided and it is argued that strict factorial invariance is required for fairness equity to exist implications for item and test bias are developed and it is argued that item or test bias probably depends on the existence of latent variables that are irrelevant to the primary goal of test constructers

a novel actor critic identifier architecture for approximate optimal control of uncertain nonlinear systems an online adaptive reinforcement learning based solution is developed for the infinite horizon optimal control problem for continuous time uncertain nonlinear systems a novel actor critic identifier aci is proposed to approximate the hamilton jacobi bellman equation using three neural network nn structures actor and critic nns approximate the optimal control and the optimal value function respectively and a robust dynamic neural network identifier asymptotically approximates the uncertain system dynamics an advantage of using the aci architecture is that learning by the actor critic and identifier is continuous and simultaneous without requiring knowledge of system drift dynamics convergence of the algorithm is analyzed using lyapunov based adaptive control methods a persistence of excitation condition is required to guarantee exponential convergence to a bounded region in the neighborhood of the optimal control and uniformly ultimately bounded uub stability of the closed loop system simulation results demonstrate the performance of the actor critic identifier method for approximate optimal control

approximate n player nonzero sum game solution for an uncertain continuous nonlinear system an approximate online equilibrium solution is developed for an n player nonzero sum game subject to continuous time nonlinear unknown dynamics and an infinite horizon quadratic cost a novel actor critic identifier structure is used wherein a robust dynamic neural network is used to asymptotically identify the uncertain system with additive disturbances and a set of critic and actor nns are used to approximate the value functions and equilibrium policies respectively the weight update laws for the actor neural networks nns are generated using a gradient descent method and the critic nns are generated by least square regression which are both based on the modified bellman error that is independent of the system dynamics a lyapunov based stability analysis shows that uniformly ultimately bounded tracking is achieved and a convergence analysis demonstrates that the approximate control policies converge to a neighborhood of the optimal solutions the actor critic and identifier structures are implemented in real time continuously and simultaneously simulations on two and three player games illustrate the performance of the developed method

optimal sliding mode control for linear time delay systems with sinusoidal disturbances this paper develops a successive approximation approach saa of optimal sliding mode control smc for linear time delay systems with sinusoidal disturbances a sequence of two point boundary value tpbv problems with both time delay and time advance terms is derived from the optimal sliding mode design according to the saa the original tpbv problems are transformed into a sequence of linear tpbv problems without delay and advance terms the obtained smc ensures that the state trajectories reach the sliding surface in finite time and remain on it thereafter the stability of the sliding mode is proved a numerical simulation is employed to verify the effectiveness of the proposed approach

judgment extremity and accuracy under epistemic vs aleatory uncertainty people view uncertain events as knowable in principle epistemic uncertainty as fundamentally random aleatory uncertainty or as some mixture of the two we show that people make more extreme probability judgments i e closer to 0 or 1 for events they view as entailing more epistemic uncertainty and less aleatory uncertainty we demonstrate this pattern in a domain where there is agreement concerning the balance of evidence pairings of teams according to their seed in a basketball tournament but individual differences in the perception of the epistemicness aleatoriness of that domain study 1 across a range of domains that vary in their perceived epistemicness aleatoriness study 2 in a single judgment task for which we only vary the degree of randomness with which events are selected study 3 and when we prime participants to see events as more epistemic or aleatory study 4 decomposition of accuracy scores suggests that the greater judgment extremity of more epistemic events can manifes

brief paper smc design for robust h control of uncertain stochastic delay systems recently sliding mode control method has been extended to accommodate stochastic systems however the existing results employ an assumption that may be too restrictive for many stochastic systems this paper aims to remove this assumption and present in terms of lmis a sliding mode control design method for stochastic systems with state delay in some cases the proposed method provides a control scheme for finite time stabilization of stochastic delay systems

asymptotic tracking for uncertain dynamic systems via a multilayer neural network feedforward and rise feedback control structure the use of a neural network nn as a feedforward control element to compensate for nonlinear system uncertainties has been investigated for over a decade typical nn based controllers yield uniformly ultimately bounded uub stability results due to residual functional reconstruction inaccuracies and an inability to compensate for some system disturbances several researchers have proposed discontinuous feedback controllers e g variable structure or sliding mode controllers to reject the residual errors and yield asymptotic results the research in this paper describes how a recently developed continuous robust integral of the sign of the error rise feedback term can be incorporated with a nn based feedforward term to achieve semi global asymptotic tracking to achieve this result the typical stability analysis for the rise method is modified to enable the incorporation of the nn based feedforward terms and a projection algorithm is developed to guarantee bounded nn weight estimates

delay identification for a class of nonlinear systems abstractthe problem of state estimation for nonlinear systems with unknown state delays is still an open problem in this paper we propose to add a delay identifier to suitable high gain observers in order to achieve simultaneous estimation of state and delay in the case of one constant delay in the state we provide sufficient conditions to guarantee the exponential convergence to zero of the errors globally with respect to the initial choice of the system state variables and locally with respect to the initial delay estimation we validate the method through an example concerning chaotic oscillators

h sub spl infin memoryless control with an spl alpha stability constraint for time delay systems an lmi approach focuses on the design of h sub spl infin memoryless state feedback controllers satisfying some spl alpha stability constraints on the closed loop poles for a class of linear systems with delayed state subject to disturbance inputs a sufficient condition for feasibility is derived in terms of linear matrix inequalities furthermore we also consider a particular optimization problem the maximal bound allowed on time delay such that the prescribed level on disturbance attenuation and the spl alpha stability constraints are still preserved this problem is converted into a linear matrix inequality lmi optimization problem and we give a suboptimal bound on the delay

the disturbance decoupling problem for systems over a ring up to now the use of geometric methods in the study of disturbance decoupling problems ddps for systems over a ring has provided only necessary conditions for the existence of solutions in this paper we study such problems considering separately the case in which only static feedback solutions are allowed and the one in which dynamic feedback solutions are admitted in the first case we give a complete geometric characterization of the solvability conditions of such problems for injective systems with coefficients in a commutative ring practical procedures for testing the solvability conditions and for constructing solutions if any exist are given in the case of systems with coefficients in a principal ideal domain pid in the second case we give a complete geometric characterization of the solvability conditions for systems with coefficients in a pid

robust identification based state derivative estimation for nonlinear systems a robust identification based state derivative estimation method for uncertain nonlinear systems is developed the identifier architecture consists of a recurrent multilayer dynamic neural network which approximates the system dynamics online and a continuous robust feedback robust integral of the sign of the error rise term which accounts for modeling errors and exogenous disturbances numerical simulations provide comparisons with existing robust derivative estimation methods including a high gain observer a 2 sliding mode robust exact differentiator and numerical differentiation methods such as backward difference and central difference

robust control of delay systems a sliding mode control design via lmi this paper considers the sliding mode control of uncertain systems with single or multiple constant or time varying state delays submitted to additive perturbations the sliding surface is designed so to maximize the calculable set of admissible delays the conditions for the existence of the sliding regime are studied by using lyapunov krasovskii functionals and lyapunov razumikhin functions lmis are used for the optimization procedure two examples illustrate the proposed method

quantitative measures of robustness for systems including delayed perturbations asymptotically stable linear systems subject to delayed time varying and nonlinear perturbations are considered razumikhin type theorems are used to obtain easy to compute bounds on the perturbations so that the systems remain stable results indicate that if delayed perturbations are included then the bound is reduced as compared to the one for nondelayed perturbations however in certain cases previously obtained bounds for the nondelayed perturbations guarantee stability even when delayed perturbations are in effect

a belief based account of decision under uncertainty we develop a belief based account of decision under uncertainty this model predicts decisions under uncertainty from i judgments of probability which are assumed to satisfy support theory and ii decisions under risk which are assumed to satisfy prospect theory in two experiments subjects evaluated uncertain prospects and assessed the probability of the respective events study 1 involved the 1995 professional basketball playoffs study 2 involved the movement of economic indicators in a simulated economy the results of both studies are consistent with the belief based account but violate the partition inequality implied by the classical theory of decision under uncertainty

robust integral of neural network and error sign control of mimo nonlinear systems this paper presents a novel state feedback control scheme for the tracking control of a class of multi input multioutput continuous time nonlinear systems with unknown dynamics and bounded disturbances first the control law consisting of the robust integral of a neural network nn output plus sign of the tracking error feedback multiplied with an adaptive gain is introduced the nn in the control law learns the system dynamics in an online manner while the nn residual reconstruction errors and the bounded disturbances are overcome by the error sign signal since both of the nn output and the error sign signal are included in the integral the continuity of the control input is ensured the controller structure and the nn weight update law are novel in contrast with the previous effort and the semiglobal asymptotic tracking performance is still guaranteed by using the lyapunov analysis in addition the nn weights and all other signals are proved to be bounded simultaneously the proposed approach also relaxes the need for the upper bounds of certain terms which are usually required in the previous designs finally the theoretical results are substantiated with simulations

what determines the shape of the probability weighting function under uncertainty decision weights are an important component in recent theories of decision making under uncertainty to better explain these decision weights a two stage approach has been proposed first the probability of an event is judged and then this probability is transformed by the probability weighting function known from decision making under risk we extend the two stage approach by allowing the probability weighting function to depend on the type of uncertainty using this more general approach properties of decision weights can be attributed to properties of probability judgments and or to properties of probability weighting we present an empirical study that shows that it is indeed necessary to allow the probability weighting function to be source dependent the analysis includes an examination of properties of the probability weighting function under uncertainty that have not been considered yet

curvature of the probability weighting function when individuals choose among risky alternatives the psychological weight attached to an outcome may not correspond to the probability of that outcome in rank dependent utility theories including prospect theory the probability weighting function permits probabilities to be weighted nonlinearly previous empirical studies of the weighting function have suggested an inverse s shaped function first concave and then convex however these studies suffer from a methodological shortcoming estimation procedures have required assumptions about the functional form of the value and or weighting functions we propose two preference conditions that are necessary and sufficient for concavity and convexity of the weighting function empirical tests of these conditions are independent of the form of the value function we test these conditions using preference ladders a series of questions that differ only by a common consequence the concavity convexity ladders validate previous findings of an s shaped weighting function concave up to p 0 40 and convex beyond that probability the tests also show significant nonlinearity away from the boundaries 0 and 1 finally we fit the ladder data with weighting functions proposed by tversky and kahneman tversky amos daniel kahneman 1992 advances in prospect theory cumulative representation of uncertainty j risk and uncertainty5 297 323 and prelec prelec dra en 1995 the probability weighting function unpublished paper

choice based elicitation and decomposition of decision weights for gains and losses under uncertainty this paper reports the results of an experimental parameter free elicitation and decomposition of decision weights under uncertainty assuming cumulative prospect theory utility functions were elicited for gains and losses at an individual level using the tradeoff method subsequently decision weights were elicited through certainty equivalents of uncertain two outcome prospects furthermore decision weights were decomposed using observable choice instead of invoking other empirical primitives as in previous experimental studies the choice based elicitation of decision weights allows for a quantitative study of their characteristics and also allows among other things for the examination of the sign dependence hypothesis for observed choice under uncertainty our results confirm concavity of the utility function in the gain domain and bounded subadditivity of decision weights and choice based subjective probabilities we also find evidence for sign dependence of decision weights

alternative test criteria in covariance structure analysis a unified approach in the context of covariance structure analysis a unified approach to the asymptotic theory of alternative test criteria for testing parametric restrictions is provided the discussion develops within a general framework that distinguishes whether or not the fitting function is asymptotically optimal and allows the null and alternative hypothesis to be only approximations of the true model also the equivalent of the information matrix and the asymptotic covariance matrix of the vector of summary statistics are allowed to be singular when the fitting function is not asymptotically optimal test statistics which have asymptotically a chi square distribution are developed as a natural generalization of more classical ones issues relevant for power analysis and the asymptotic theory of a testing related statistic are also investigated

robust sliding mode control for uncertain time delay systems an lmi approach this note is devoted to robust sliding mode control for time delay systems with mismatched parametric uncertainties a delay independent sufficient condition for the existence of linear sliding surfaces is given in terms of linear matrix inequalities based on which the corresponding reaching motion controller is also developed the results are illustrated by an example

risk attitudes and decision weights to accommodate the observed pattern of risk aversion and risk seeking as well as common violations of expected utility e g the certainty effect we introduce and characterize a weighting function according to which an event has greater impact when it turns impossibility into possibility or possibility into certainty than when it merely makes a possibility more or less likely we show how to compare such weighting functions of different individuals with respect to the degree of departure from expected utility and we present a method for comparing an individual s weighting functions for risk and for uncertainty

lyapunov stability theory of nonsmooth systems this paper develops nonsmooth lyapunov stability theory and lasalle s invariance principle for a class of nonsmooth lipschitz continuous lyapunov functions and absolutely continuous state trajectories computable tests based on filipov s differential inclusion and clarke s generalized gradient are derived the primary use of these results is in analyzing the stability of equilibria of differential equations with discontinuous right hand side such as in nonsmooth dynamic systems or variable structure control

projection based adaptive neurocontrol with switching logic deadzone tuning in this brief an adaptive neural network nn controller is proposed for multiple input multiple output mimo nonlinear systems with triangular control structure and unknown control directions deadzones are employed in the projection based nn weight learning laws and the nussbaum parameter update laws with levels tuned by an innovative switching logic tuning mechanism detailed analysis using a family of lyapunov like integral functions and the function approximation capability of nns proves that all the tracking errors are semiglobal uniform ultimate bounded in small neighborhoods of the origin while the closed loop system variables state vector nn weights nussbaum parameters and the control law remain bounded a simulation study confirms the theoretical results and verifies the effectiveness of the proposed design