an improved adaptive control for robust adaptation an adaptive control law consisting of an integral and proportional adaptation is suggested it is shown that the addition of the proportional adaptation term to the control law improves the performance of the adaptive system although this is done in the context of a first order system it is believed that this may also be the case for higher order systems the scalar case is only discussed simulation results are presented to complement the theoretical developments

robustness of adaptive control for stochastic systems robustness of the stochastic adaptive systems with unmodelled dynamics and external disturbances is discussed in this paper an adaptive controller is designed using the els algorithm except for the finite switches it is shown that the closed loop system is globally stable both the estimation error and the tracking error approach their minima when the unmodelled dynamics diminish that is the asymptotically adaptive system operating with the els algorithm has a good robustness with respect to unmodelled dynamics and external disturbances

optimal robustness via constrained controller in siso discrete time feedback systems a new sufficient condition for robust stabilization of siso discrete time feedback systems with both non linear or linear time varying uncertainties and constrained actuators is derived in this paper neither the plant nor the controller has to be restricted to the stable or minimum phase case the necessary and sufficient condition for solvability of a stabilizer satisfying such a robust constraint is also introduced the optimal h norm theory is employed to achieve the maximum robustness to tolerate the uncertainties via constrained control finally a design algorithm is proposed to specify adequate parameters in the compensator to satisfy the criterion of optimal robustness and an example with computer simulation is given to illustrate the results

robustness analysis of identification and adaptive control for stochastic systems abstract this paper gives a preliminary robustness analysis for stochastic systems with unmodelled dynamics the extended least squares algorithm and stochastic adaptive control for tracking are defined on the basis of the modelled part of the system and it is shown that the parameter estimate is closed to the true one and the tracking error is small if the unmodelled dynamics is small

a robust stochastic adaptive controller the adaptive tracking problem is considered for discrete time stochastic systems consisting of a modeled part being a stable armax process and unmodeled dynamics dominated by a small constant epsilon multiplied by a quantity independent of epsilon but tending to infinity as the past input output and noise grow the adaptive control law proposed is switched at stopping times and is disturbed by a sequence of random vectors bounded by an arbitrary small but fixed constant sigma it is shown that the closed loop system is globally stable the estimation errors for parameters contained in the modeled part of the order epsilon and the tracking error differs from the minimum tracking error for systems without unmodeled dynamics by value of o epsilon sup 2 o sigma sup 2

optimal control of pdes in a complex space setting application to the schrodinger equation in this paper we discuss optimality conditions for abstract optimization problems over complex spaces we then apply these results to optimal control problems with a semigroup structure as an application we detail the case when the state equation is the schrodinger one with pointwise constraints on the bilinear control we derive first and second order optimality conditions and address in particular the case that the control enters the state equation and cost function linearly

clustering with evolution strategies tbe applicability of evolution strategies ess population based stochastic optimization techniques to optimize clustering objective functions is explored clustering objective functions are categorized into centroid and non centroid type of functions optimization of the centroid type of objective functions is accomplished by formulating them as functions of real valued parameters using ess both hard and fuzzy clustering objective functions are considered in this study applicability of ess to discrete optimization problems is extended to optimize the non centroid type of objective functions as ess are amenable to parallelization a parallel model master slave model is described in the context of the clustering problem results obtained for selected data sets substantiate the utility of ess in clustering

a new clustering algorithm with multiple runs of iterative procedures abstract the k means clustering algorithm is well known and widely used recently an alternative algorithm based on an earlier idea duda and hart pattern classification and scene analysis 1973 ismail and kamel pattern recognition 22 75 89 1989 has been proposed in comprehensive experiments we verify that this alternative is superior to k means both techniques suffer from the complexity of the cost function surface they are descending and a consequent inclination to fall into poor local minima a popular solution to this problem is to rerun the chosen iterative procedure from many randomly chosen starting positions saving the best we introduce here an alternative strategy to form the starting position for such a multiple run which consists of perturbing an existing result in an attempt to improve its poor aspects this controlled configuration change has comparable complexity with random initialisation but usually produces superior clustering results comprehensive experiments confirm this result

a tabu search based algorithm for the fuzzy clustering problem the fuzzy clustering problem fcp is a mathematical program which is difficult to solve since it is nonconvex which implies possession of many local minima the fuzzy c means heuristic is the widely known approach to this problem but it is guaranteed only to yield local minima in this paper we propose a new approach to this problem which is based on tabu search technique and aims at finding a global solution of fcp we compare the performance of the algorithm with the fuzzy c means algorithm

an adaptive control algorithm for linear systems having unknown time delay abstract this paper presents an algorithm for adaptive control of linear systems having unknown time delay a rational approximation is used for the delay and it is argued that within this framework the analysis of the properties of the corresponding adaptive control algorithm essentially reduces to the issue of robustness to unmodelled dynamics a global convergence theory is presented using recent methods for analyzing robustness properties of adaptive control algorithms the analysis is supported by simulation results

analysis and design of i o based variable structure adaptive control this paper presents an overview of a technique for the design of variable structure model reference adaptive control vs mrac systems using only input output data the main ideas underlying the design and the analysis as well as the main properties of the controller are described having as point of departure a well known parameter adaptive mrac scheme then asymptotic properties of the vs mrac system are established taking into account the effect of the averaging filters necessary to implement some equivalent control signals in particular global exponential stability of the associated error system with respect to a small residual set is demonstrated the effect of linear zones in the relay functions is assessed showing that they can alleviate the chattering phenomena this is related with the fact that the vs mrac has a high gain stability property a procedure for noise sensitivity reduction is also proposed simulations illustrate the very satisfactory performance of the vs mrac in adverse operating conditions

a monotonic method for nonlinear optimal control problems with concave dependence on the state initially introduced in the framework of quantum control the so called monotonic algorithms have demonstrated very good numerical performance when dealing with bilinear optimal control problems this article presents a unified formulation that can be applied to more general nonlinear settings compatible with the hypothesis detailed below in this framework we show that the well posedness of the general algorithm is related to a nonlinear evolution equation we prove the existence of the solution to the evolution equation and give important properties of the optimal control functional finally we show how the algorithm works for selected models from the literature we also compare the algorithm with the gradient algorithm

optimal bilinear control of gross pitaevskii equations with coulombian potentials in this paper we consider an optimal bilinear control problem for the gross pitaevskii equations with coulombian potentials we show the well posedness of the problem and the existence of an optimal control in addition the first order optimality system is rigorously derived in particular we prove the frechet differentiability of the unconstrained functional we extend the study of hintermuller et al 2013 15 to more general power nonlinearities and unbounded potentials c 2015 elsevier inc all rights reserved

applications of singular perturbation techniques to control problems this paper discusses typical applications of singular perturbation techniques to control problems in the last fifteen years the first three sections are devoted to the standard model and its time scale stability and controllability properties the next two sections deal with linear quadratic optimal control and one with cheap near singular control then the composite control and trajectory optimization are considered in two sections and stochastic control in one section the last section returns to the problem of modeling this time in the context of large scale systems the bibliography contains more than 250 titles

a tabu search based heuristic for clustering abstract this paper considers a clustering problem where a given data set is partitioned into a certain number of natural and homogeneous subsets such that each subset is composed of elements similar to one another but different from those of any other subset for the clustering problem a heuristic algorithm is exploited by combining the tabu search heuristic with two complementary functional procedures called packing and releasing procedures the algorithm is numerically tested for its effectiveness in comparison with reference works including the tabu search algorithm the k means algorithm and the simulated annealing algorithm

controllability for distributed bilinear systems this paper studies controllability of systems of the form dw dt mathcal a w p t mathcal b w where mathcal a is the infinitesimal generator of a c 0 semigroup of bounded linear operators e mathcal a t on a banach space x mathcal b x to x is a c 1 map and p in l 1 0 t mathbb r is a control the paper i gives conditions for elements of x to be accessible from a given initial state w 0 and ii shows that controllability to a full neighborhood in x of w 0 is impossible for dim x infty examples of hyperbolic partial differential equations are provided

a tabu search approach for the minimum sum of squares clustering problem in this paper a tabu search based clustering approach called ts clustering is proposed to deal with the minimum sum of squares clustering problem in the ts clustering algorithm five improvement operations and three neighborhood modes are given the improvement operation is used to enhance the clustering solution obtained in the process of iterations and the neighborhood mode is used to create the neighborhood of tabu search the superiority of the proposed method over some known clustering techniques is demonstrated for artificial and real life data sets

monotonic time discretized schemes in quantum control most of the numerical simulations in quantum bilinear control have used monotonically convergent algorithms of krotov introduced by tannor et al 15 of zhu rabitz 16 or their unified formulation in 17 however the properties of the discrete version of these procedures have not been yet tackled with we present in this paper a stable time and space discretization which preserves the monotonic properties of the monotonic algorithms numerical results show that the newly derived algorithms are stable and enable various experimentations

an evolutionary technique based on k means algorithm for optimal clustering in r n a genetic algorithm based efficient clustering technique that utilizes the principles of k means algorithm is described in this paper the algorithm called kga clustering while exploiting the searching capability of k means avoids its major limitation of getting stuck at locally optimal values its superiority over the k means algorithm and another genetic algorithm based clustering method is extensively demonstrated for several artificial and real life data sets a real life application of the kga clustering in classifying the pixels of a satellite image of a part of the city of mumbai is provided

data clustering by minimizing disconnectivity identifying clusters of arbitrary shapes remains a challenge in the field of data clustering we propose a new measure of cluster quality based on minimizing the penalty of disconnection between objects that would be ideally clustered together this disconnectivity is based on analysis of nearest neighbors and the principle that an object should be in the same cluster as its nearest neighbors an algorithm called mindisconnect is proposed that heuristically minimizes disconnectivity and numerical results are presented that indicate that the new algorithm can effectively identify clusters of complex shapes and is robust in finding clusters of arbitrary shapes

local minimization algorithms for dynamic programming equations the numerical realization of the dynamic programming principle for continuous time optimal control leads to nonlinear hamilton jacobi bellman equations which require the minimization of a nonlinear mapping over the set of admissible controls this minimization is often performed by comparison over a finite number of elements of the control set in this paper we demonstrate the importance of an accurate realization of these minimization problems and propose algorithms by which this can be achieved effectively the considered class of equations includes nonsmooth control problems with ell 1 penalization which lead to sparse controls

new class of control laws for robotic manipulators part 1 non adaptive case a new class of exponentially stabilizing control laws for joint level control of robot arms is introduced it has recently been recognized that the non linear dynamics associated with robotic manip

k harmonic means data clustering with simulated annealing heuristic abstract clustering procedures partition a set of objects into clusters such that objects in the same cluster are more similar to each other than objects in different clusters according to some predefined criteria clustering is a popular data analysis and data mining technique since clustering problem have np complete nature the larger the size of the problem the harder to find the optimal solution and furthermore the longer to reach a reasonable results one of the most used techniques for clustering is based on k means such that the data is partitioned into k clusters although k means algorithm is easy to implement and works fast in most situations it suffers from two major drawbacks one is sensitivity to initialization and the other is convergence to local optima it is seen from the studies k harmonic means clustering solves the problem of initialization but since its greedy search nature the second problem convergence to local optima still remains in this paper we develop a new algorithm for solving this problem based on a simulated annealing technique simulated annealing k harmonic means clustering sakhmc the experiment results on the iris and the other well known data illustrate the robustness of the sakhmc clustering algorithm

a new heuristic approach for non convex optimization problems in this work a new optimization method called the heuristic kalman algorithm hka is presented this new algorithm is proposed as an alternative approach for solving continuous non convex optimization problems the principle of hka is to explicitly consider the optimization problem as a measurement process designed to give an estimate of the optimum a specific procedure based on the kalman estimator was developed to improve the quality of the estimate obtained through the measurement process the main advantage of hka compared to other metaheuristics lies in the small number of parameters that need to be set by the user further it is shown that hka converges almost surely to a near optimal solution the efficiency of hka was evaluated in detail using several non convex test problems both in the unconstrained and constrained cases the results were then compared to those obtained via other metaheuristics the numerical experiments show that hka is a promising approach for solving non convex optimization problems particularly in terms of computation time and success ratio

kgmo a swarm optimization algorithm based on the kinetic energy of gas molecules swarm based algorithms have acquired an important role in solving real world optimization problems in this paper kinetic gas molecule optimization kgmo an optimization algorithm that is based on the kinetic energy of gas molecules is introduced the agents are gas molecules that are moving in the search space they are subject to the kinetic theory of gases which defines the rules for gas molecule interactions in the model the performance of the proposed algorithm in terms of its ability to find the global minima of 23 nonlinear benchmark functions is evaluated against the corresponding results of two well known benchmark algorithms namely particle swarm optimization pso and the recently developed high performance gravitational search algorithm gsa the simulations that were undertaken indicate that kgmo achieves better results in decreasing the mean square error mse significant improvements of up to 107 and 1020 times were achieved by kgmo against pso and gsa respectively in solving unimodal benchmark functions within 150 iterations improvements of at least tenfold were achieved in solving the multimodal benchmark functions the proposed algorithm is more accurate and converges faster than does the benchmark algorithms which makes this algorithm especially useful in solving complex optimization problems

a hybrid method combining continuous tabu search and nelder mead simplex algorithms for the global optimization of multiminima functions abstract tabu search ts is a metaheuristic which proved efficient to solve various combinatorial optimization problems however few works deal with its application to the global minimization of functions depending on continuous variables to perform this task we propose an hybrid method combining tabu search and simplex search ss ts allows to cover widely the solution space to stimulate the search towards solutions far from the current solution and to avoid the risk of trapping into a local minimum ss is used to accelerate the convergence towards a minimum the nelder mead simplex algorithm is a classical very powerful local descent algorithm making no use of the objective function derivatives a simplex is a geometrical figure consisting in n dimensions of n 1 points if any point of a simplex is taken as the origin the n other points define vector directions that span the n dimension vector space through a sequence of elementary geometric transformations reflection contraction and extension the initial simplex moves expands or contracts to select the appropriate transformation the method only uses the values of the function to be optimized at the vertices of the simplex considered after each transformation the current worst vertex is replaced by a better one our algorithm called continuous tabu simplex search ctss implemented in two different forms ctsssingle ctssmultiple is made up of two steps first an adaptation of ts to continuous optimization problems allowing to localize a promising area then intensification within this promising area involving ss the efficiency of ctss is extensively tested by using analytical test functions of which global and local minima are known a comparison is proposed with several variants of tabu search genetic algorithms and simulated annealing ctss is applied to the design of a eddy current sensor aimed at non destructive control

application of the noising method to the travelling salesman problem abstract in this paper we study the application of the noising method a recent combinatorial optimization metaheuristic to the travelling salesman problem tsp we first detail the features of the noising method in order to adapt it to the tsp then we experimentally compare it to the simulated annealing method and we study its sensitiveness to different parameters involved in its design two types of tsps have been considered the randomly weighted tsp for which the weights of the edges are randomly chosen and the euclidean tsp for which the vertices belong to the euclidean plane and where the weights of the edges are given by the euclidean distances between the vertices

an agglomerative clustering algorithm using a dynamic k nearest neighbor list in this paper a new algorithm is developed to reduce the computational complexity of ward s method the proposed approach uses a dynamic k nearest neighbor list to avoid the determination of a cluster s nearest neighbor at some steps of the cluster merge double linked algorithm dla can significantly reduce the computing time of the fast pairwise nearest neighbor fpnn algorithm by obtaining an approximate solution of hierarchical agglomerative clustering in this paper we propose a method to resolve the problem of a non optimal solution for dla while keeping the corresponding advantage of low computational complexity the computational complexity of the proposed method dknna fs dynamic k nearest neighbor algorithm with a fast search in terms of the number of distance calculations is o n 2 where n is the number of data points compared to fpnn with a fast search fpnn fs the proposed method using the same fast search algorithm dknna fs can reduce the computing time by a factor of 1 90 2 18 for the data set from a real image in comparison with fpnn fs dknna fs can reduce the computing time by a factor of 1 92 2 02 using the data set generated from three images compared to dla with a fast search dla fs dknna fs can decrease the average mean square error by 1 26 for the same data set

asymptotic properties of feedback solutions for a class of quantum control problems control of quantum systems described by the schrodinger equation is considered feedback control laws are developed for orbit tracking via controlled hamiltonians and their asymptotic properties are analyzed numerical integration via time splitting is also investigated and used to demonstrate the feasibility of the proposed feedback laws

a robust approach to adaptive control algorithms in this note a robust approach is proposed for various modified adaptive control algorithms to counteract the presence of bounded disturbances a key feature of the approach is that when using it neither the knowledge of the upper bound on the disturbance nor the knowledge of the upper bound on the norm of the matching controller parameters is required for time implementation of those adaptive control algorithms modified with dead zone constrained estimation and spl sigma modification the stability and convergence results of the resulting algorithms are provided

adaptive control with discontinuous σ factor and saturation for improved robustness an adaptive control law is proposed to improve the robustness of the adaptive system with respect to fast unmodelled dynamics represented by a singular perturbation the new law is based on saturation of the regressor and control parameter vector with inclusion of a discontinuous factor the analysis of the resulting system is presented and the conclusions are illustrated by simulations the introduction of a discontinuous factor represents an extension of an earlier work the advantages of this extension include global stability with less restrictive assumptions and better transient behaviour

on the controllability of quantum transport in an electronic nanostructure we investigate the controllability of quantum electrons trapped in a two dimensional device typically a metal oxide semiconductor mos field effect transistor the problem is modeled by the schrodinger equation in a bounded domain coupled to the poisson equation for the electrical potential the controller acts on the system through the boundary condition on the potential on a part of the boundary modeling the gate we prove that generically with respect to the shape of the domain and boundary conditions on the gate the device is controllable we also consider control properties of a more realistic nonlinear version of the device taking into account the self consistent electrostatic poisson potential

analysis of a new algorithm for continuous time robust adaptive control analysis of a robust indirect adaptive control scheme is presented it is shown that a new dead zone technique can be used so that the knowledge of the parameters of the upper bounding function on the unmodeled dynamics and the disturbance is not necessarily required furthermore the allowable unmodeled dynamics could be enlarged if the knowledge of the upper bound of the unmodeled dynamics is given

adaptive control systems with robustness optimization to non linear time varying unmodelled dynamics in this paper the robustness properties of adaptive controllers to non linear time varying unmodelled dynamics are presented a new robust stability criterion will be derived for analysing the robustness of adaptive control in deterministic systems under non linear time varying model errors the concept of the excess robustness of adaptive systems is introduced and the theory of the minimum h norm will be employed to solve the problem of robustness optimization in adaptive control systems the results of simulations which display the good performance of the robust adaptive controller according to our design algorithm are also presented

regional quadratic control problem for distributed bilinear systems with bounded controls the aim of this paper is to study regional quadratic control problem for a distributed bilinear system evolving in a spatial domain the question is to obtain a bounded feedback control that drives such a system from an initial state to a desired one in finite time only on a subregion of and minimises a quadratic functional cost our purpose is to prove that an optimal control exists and characterised as solution of an optimality system the approach is successfully illustrated by simulations

a new robust mrac using variable structure design for relative degree two plants abstract variable structure design concept has been shown not only useful in dealing with uncertain systems but also useful in establishing controllers in the adaptive context continuing the work this research proposed a new robust model reference adaptive control for single input single output linear plants with relative degree two a class which encompasses a large number of linearized mechanical systems when compared with the conventional mrac schemes the current approach drastically improves the transient behavior and the tracking performance of the closed loop system from several numerical simulations furthermore if the reference input satisfies the persistently exciting condition or if the prior knowledge about the interval in which the true parameter vector lies is available the magnitude of the input force can be considerably reduced into a more realistic level in the presence of mild unmodelled dynamics and bounded output disturbances the proposed scheme possesses similar well behaved properties it can be shown that the tracking error will in fact fall into a residual set whose size can be directly related to the size of unmodelled dynamics and of output disturbances

regional optimal control problem of a class of infinite dimensional bi linear systems abstractthe purpose of this paper is to study regional optimal control of an infinite dimensional bi linear system excited by unbounded or bounded controls then we prove the existence of an optimal control that minimises a functional cost and we give the characterisation of such a control also we give the condition that ensures the uniqueness of the optimal control the obtained results lead to an algorithm that we illustrate by simulations for a bi dimensional diffusion system excited by a zone actuator

the noising methods a generalization of some metaheuristics abstract in this paper an exhaustive review of the principles and of the applications of the noising methods recent combinatorial optimization metaheuristics is attempted the features and the variants of the noising methods are detailed and the tunings of their parameters when applied to different combinatorial optimization problems are summarized the links between the noising methods and two other metaheuristics namely the simulated annealing method and the threshold accepting algorithm are also studied and that the noising methods can be considered as generalizations of these metaheuristics when their components are properly chosen is shown

a stability instability boundary for disturbance free slow adaptation with unmodeled dynamics the instability of adaptive schemes for small values of parameter adjustment gain has the form of a slow drift of adjustable parameters with an averaging analysis we derive not only a sufficient condition for stability of this drift but also a sufficient condition for it to be unstable a sharp boundary between stability and instability is signal dependent and much less demanding than the usual strict positive realness property the new positivity condition has a simple signal energy interpretation

modelling and linear model following control of singularly perturbed discrete systems a new method for order reduction of singularly perturbed discrete systems is derived the discrete system is reduced to its dominant and parasitic parts this method can exactly exhibit the responses of the original system for control law design the linear model following control lmfc method is applied the order of the model is chosen the same as that of the dominant part of the plant i e we apply to the plant with and without parasitics the same lmfc law the conditions for reduced order lmfc systems are derived and some simulation results are illustrated

k means type algorithms a generalized convergence theorem and characterization of local optimality the k means algorithm is a commonly used technique in cluster analysis in this paper several questions about the algorithm are addressed the clustering problem is first cast as a nonconvex mathematical program then a rigorous proof of the finite convergence of the k means type algorithm is given for any metric it is shown that under certain conditions the algorithm may fail to converge to a local minimum and that it converges under differentiability conditions to a kuhn tucker point finally a method for obtaining a local minimum solution is given

on reduced order adaptive output error identification and adaptive iir filtering the reduced order application of landau s adaptive output error identifier results in a perturbed error system where the perturbation signal is a moving average of the unmodeled portion of the unknown plant output or desired signal in adaptive filter parlance it is proven in this paper that if this perturbation signal is sufficiently small and a reduced order dimension model is sufficiently excited then the output and parameter estimates of this adaptive identifier filter remain bounded the influence of various operating conditions on this quantitatively defined bound are noted this robustness property is crucial in all real applications which due to nonlinearities and distributed effects are subject to reduced order modeling

on the robustness of output feedback control methods to modeling errors singular perturbation analysis is employed to study the robustness of output feedback control schemes for linear time invariant systems the effect of designing the control strategy using a model that neglects unknown parasitic elements is considered it is shown that such a strategy may in general be destabilizing even when the neglected modes are asymptotically stable special structures that will not permit this situation and will guarantee asymptotic stability are illustrated

constructive solution of a bilinear optimal control problem for a schrodinger equation abstract often considered in numerical simulations related to the control of quantum systems the so called monotonic schemes have not been so far much studied from the functional analysis point of view yet these procedures provide an efficient constructive method for solving a certain class of optimal control problems this paper aims both at extending the results already available about these algorithms in the finite dimensional case i e the time discretized case and at completing those of the continuous case this paper starts with some results about the regularity of a functional related to a wide class of models in quantum chemistry these enable us to extend an inequality due to ojasiewicz to the infinite dimensional case finally some inequalities proving the cauchy character of the monotonic sequence are obtained followed by an estimation of the rate of convergence

on adaptive state regulation an indirect adaptive state regulator consisting of an adaptive observer and an asymptotic feedback matrix synthesis is discussed in a rigorous analysis lyapunov stability of the resulting closed loop system without the need for external excitation possible global instabilities as well as global convergence subject to sufficient excitation are shown moreover the behavior of the closed loop system is analyzed for cases in which the true plant differs from the assumed linear time invariant model in that it is of higher order contains slight nonlinearities and time variation effects and is subject to plant and measurement disturbances

a simulated annealing algorithm for the clustering problem abstract in this paper we discuss the solution of the clustering problem usually solved by the k means algorithm the problem is known to have local minimum solutions which are usually what the k means algorithm obtains the simulated annealing approach for solving optimization problems is described and is proposed for solving the clustering problem the parameters of the algorithm are discussed in detail and it is shown that the algorithm converges to a global solution of the clustering problem we also find optimal parameters values for a specific class of data sets and give recommendations on the choice of parameters for general data sets finally advantages and disadvantages of the approach are presented

a time efficient pattern reduction algorithm for k means clustering this paper presents an efficient algorithm called pattern reduction pr for reducing the computation time of k means and k means based clustering algorithms the proposed algorithm works by compressing and removing at each iteration patterns that are unlikely to change their membership thereafter not only is the proposed algorithm simple and easy to implement but it can also be applied to many other iterative clustering algorithms such as kernel based and population based clustering algorithms our experiments from 2 to 1000 dimensions and 150 to 10 000 000 patterns indicate that with a small loss of quality the proposed algorithm can significantly reduce the computation time of all state of the art clustering algorithms evaluated in this paper especially for large and high dimensional data sets

regularity for a schrodinger equation with singular potentials and application to bilinear optimal control abstract we study the schrodinger equation i t u u v 0 u v 1 u 0 on r 3 0 t where v 0 x t x a t 1 with a w 2 1 0 t r 3 is a coulombian potential singular at finite distance and v 1 is an electric potential possibly unbounded the initial condition u 0 h 2 r 3 is such that r 3 1 x 2 2 u 0 x 2 dx the potential v 1 is also real valued and may depend on space and time variables we prove that if v 1 is regular enough and at most quadratic at infinity this problem is well posed and the regularity of the initial data is conserved for the solution we also give an application to the bilinear optimal control of the solution through the electric potential

decentralized adaptive control of interconnected systems the adaptive control of a class of large scale systems formed of an arbitrary interconnection of subsystems with unknown parameters nonlinearities and bounded disturbances is investigated it is first shown that no matter how weak the interconnections are a decentralized adaptive control scheme can become unstable approaches are then developed for stabilization and tracking using new decentralized adaptive controllers in the case where the relative degree n of the transfer function of each decoupled subsystem is less than or equal to two sufficient conditions are established which guarantee boundedness and exponential convergence of the state and parameter errors to bounded residual sets in the absence of disturbances and interconnections the decentralized adaptive control schemes guarantee exact convergence of the tracking errors to zero the effectiveness of the proposed adaptive schemes is demonstrated using a simple example

an asymptotic error analysis of identifiers and adaptive observers in the presence of parasitics the behavior of identifiers and adaptive observers in the presence of parasitics is analyzed bounds for the state parameter error are established the adaptive schemes considered are shown to be robust provided the input signal is rich for the dominant modes but does not contain high frequencies in the parasitic range the bounds indicate possibilities for reducing the error by a proper choice of the input signal

decentralized adaptive robust control for a class of large scale systems with uncertainties in the interconnections the problem of decentralized control is considered for a class of time varying large scale systems with uncertainties and external disturbances in the interconnections in this paper the upper bounds of the uncertainties and external disturbances are assumed to be unknown the adaptation laws are proposed to estimate such unknown bounds and by making use of the updated values of these unknown bounds a class of decentralized linear and non linear state feedback controllers are constructed it is shown that by employing the proposed decentralized non linear state feedback controllers the solutions of the resulting adaptive closed loop large scale system can be guaranteed to be uniformly bounded and the states are uniformly asymptotically stable by using the decentralized linear state feedback controllers one can guarantee the uniform ultimate boundedness of the resulting adaptive closed loop large scale system finally a numerical example is given to demonstrate the validity of the results

a stable adaptive implementation of the internal model principle an adaptive implementation of the internal model principle for linear time invariant systems that allows for rejection of unknown deterministically modeled disturbances is provided the minimal representation of the system model is used for parameter estimation and the global convergence and stability of the algorithm are investigated with unmodeled dynamics with proper modification of the parameter estimation algorithm global convergence and stability for the algorithm are obtained without the requirement of persistent excitation some simulation results are given to support the analysis

optimal bilinear control of stochastic nonlinear schrodinger equations mass sub critical case we study optimal control problems for stochastic nonlinear schrodinger equations in both the mass subcritical and critical case for general initial data of the minimal l 2 regularity we prove the existence and first order lagrange condition of an open loop control in particular these results apply to the stochastic nonlinear schrodinger equations with the critical quintic and cubic nonlinearities in dimensions one and two respectively furthermore we obtain uniform estimates of backward stochastic solutions in new spaces of type u 2 and v 2 adapted to evolution operators related to linear schrodinger equations with lower order perturbations these estimates yield a new temporal regularity of backward stochastic solutions which is crucial for the tightness of approximating controls induced by ekeland s variational principle

a tabu search approach to the clustering problem in this paper we consider the problem of clustering m objects into c clusters the objects are represented by points in an n dimensional euclidean space and the objective is to classify these m points into c clusters such that the distance between points within a cluster and its center which is to be found is minimized the problem is a nonconvex program that has many local minima it has been studied by many researchers and the most well known algorithm for solving it is the k means algorithm in this paper we develop a new algorithm for solving this problem based on a tabu search technique preliminary computational experience on the developed algorithm are encouraging and compare favorably with both the k means and the simulated annealing algorithms

optimal quantum control by an adapted coordinate ascent algorithm this paper explores an adaption of the coordinate ascent approach to quantum control problems it was motivated by the observation that several of the existing monotone converging schemes for quantum control may be viewed as approximations of the well known coordinate ascent method our implementation employs line searches in coordinate directions in control space using only evaluations of the performance functional without invoking its derivatives it is based on recasting the performance functional as a local tracking function which gives the future quality of a control at each moment in time back propagation of a basis of the target space enables these performance functional i e tracking function evaluations to be done efficiently the performance functional may include or not include regularization terms as needed convergence of the resulting algorithm and its relation to previous schemes are discussed and numerical examples are provided in our tests the coordinate ascent algorithm exhib

a global algorithm for the fuzzy clustering problem abstract the fuzzy clustering fc problem is a non convex mathematical program which usually possesses several local minima the global minimum solution of the problem is found using a simulated annealing based algorithm some preliminary computational experiments are reported and the solution is compared with that generated by the fuzzy c means algorithm

clustering categorical data sets using tabu search techniques clustering methods partition a set of objects into clusters such that objects in the same cluster are more similar to each other than objects in different clusters according to some defined criteria the fuzzy k means type algorithm is best suited for implementing this clustering operation because of its effectiveness in clustering data sets however working only on numeric values limits its use because data sets often contain categorical values in this paper we present a tabu search based clustering algorithm to extend the k means paradigm to categorical domains and domains with both numeric and categorical values using tabu search based techniques our algorithm can explore the solution space beyond local optimality in order to aim at finding a global solution of the fuzzy clustering problem it is found that the clustering results produced by the proposed algorithm are very high in accuracy

a robust direct adaptive controller this paper proposes a new direct adaptive control algorithm which is robust with respect to additive and multiplicative plant unmodeled dynamics the algorithm is designed based on the reduced order plant which is assumed to be minimum phase and of known order and relative degree but is analyzed with respect to the overall plant which due to the unmodeled dynamics may be nonminimum phase and of unknown order and relative degree it is shown that if the unmodeled dynamics are sufficiently small in the low frequency range then the algorithm guarantees boundedness of all signals in the adaptive loop and small residual tracking errors for any bounded initial conditions in the absence of unmodeled dynamics the residual tracking error is shown to be zero

robust redesign of adaptive control effects of unmodeled high frequency dynamics on stability and performance of adaptive control schemes are analyzed in the regulation problem global stability properties are no longer guaranteed but a region of attraction exists for exact adaptive regulation the dependence of the region of attraction on unmodeled parasitics is examined first then the general case of model reference adaptive control is considered in which parasitics can destroy stability and boundedness properties a modified adaptive law is proposed guaranteeing the existence of a region of attraction from which all signals converge to a residual set which contains the equilibrium for exact tracking the size of this set depends on design parameters the frequency range of parasitics and the reference input signal characteristics

stability and exponential stability of an adaptive control scheme for plants of any relative degree relying on a new adaptive pole assignment control scheme for plants with unknown relative degree and uncertain order which can be made minimum phase by means of a relative degree one first order parallel compensator a new adaptation mechanism is proposed in this note in order to tackle with the problem of the attainment of an exponentially stable behavior of the output error in particular the connection between exponential stability and persistent excitation is outlined and a complete stability analysis is performed

unmodeled dynamics in adaptive control systems revisited an alternative approach to analyze the robustness of adaptive systems with respect to unmodeled dynamics and external disturbances is presented this approach shows that certain restrictive assumptions needed earlier can in fact be discarded it also shows that an adaptation algorithm based on a time varying factor equivalent to a constant factor together with projection can provide a globally asymptotically stable system with respect to a compact residual set under mild requirements

genetic and nelder mead algorithms hybridized for a more accurate global optimization of continuous multiminima functions abstract a hybrid method combining two algorithms is proposed for the global optimization of multiminima functions to localize a promising area likely to contain a global minimum it is necessary to well explore the whole search domain when a promising area is detected the appropriate tools must be used to exploit this area and obtain the optimum as accurately and quickly as possible both tasks are hardly performed through only one method we propose an algorithm using two processes each one devoted to one task global metaheuristics such as simulated annealing tabu search and genetic algorithms gas are efficient to localize the best areas on the other hand local search methods are classically available in particular the hill climbing e g the quasi newton method and the nelder mead simplex search ss therefore we worked out an hybrid method called continuous hybrid algorithm cha performing the exploration with a ga and the exploitation with a nelder mead ss to evaluate the efficiency of cha we implemented a set of benchmark functions and compared our results to the ones supplied by other competitive methods

a comparative study of suboptimal branch and bound algorithms abstract the branch and bound algorithm is widely known as an efficient approach for selecting optimal feature subsets if the optimality of the solution is allowed to be compromised it is possible to further improve the search speed of the branch and bound algorithm this paper studies the look ahead search strategy which can eliminate many solutions deemed to be suboptimal early in the branch and bound search we propose ways to incorporate the look ahead search scheme into four major branch and bound algorithms namely the basic branch and bound algorithm the ordered branch and bound algorithm the fast branch and bound algorithm and the adaptive branch and bound algorithm a comparative study of the look ahead scheme in terms of the computational cost and the solution quality on these suboptimal branch and bound algorithms is carried out on real data sets furthermore we test the feasible use of suboptimal branch and bound algorithms on a high dimensional data set and compare its performance with other well known suboptimal feature selection algorithms

formulation and numerical solution of finite level quantum optimal control problems optimal control of finite level quantum systems is investigated and iterative solution schemes for the optimization of a control representing laser pulses are developed the purpose of this external field is to channel the system s wavefunction between given states in its most efficient way physically motivated constraints such as limited laser resources or population suppression of certain states are accounted for through an appropriately chosen cost functional first order necessary optimality conditions and second order sufficient optimality conditions are investigated for solving the optimal control problems a cascadic non linear conjugate gradient scheme and a monotonic scheme are discussed results of numerical experiments with a representative finite level quantum system demonstrate the effectiveness of the optimal control formulation and efficiency and robustness of the proposed approaches

robust static output feedback controller synthesis using kharitonov s theorem and evolutionary algorithms this paper presents a simple but effective tuning strategy for robust static output feedback sof controllers with minimal quadratic cost in the context of multiple parametric uncertainties finding this type of controller is known to be computationally intractable using conventional techniques this is mainly due to the non convexity of the resulting control problem which has a fixed structure to solve this kind of control problem easily and directly without using any complicated mathematical manipulations we utilize kharitonov s theorem and an evolutionary algorithm ea for the resolution of the underlying constrained optimization problem using kharitonov s theorem a family of bounded robustly stable static output feedback controllers can be defined and ea is used to select the controller that ensures a minimal quadratic cost within this family the resulting tuning strategy is applicable to both stable and unstable systems without any limitations on the order of the process to be controlled a numerical study was conducted to demonstrate the validity of the proposed tuning procedure

robustness of adaptive control without deadzones data normalization or persistence of excitation abstract robustness properties have been established for modified adaptive control algorithms the major modifications involve various combinations of normalization relative deadzones persistence of excitation parameter projection and or so called modification results showing global boundedness have used some form of data normalization many results on local properties have depended on persistence of excitation however use of these modifications involve some a priori knowledge of the plant for good performance this may lead to complications in practice here it is shown that some useful local robustness properties hold if only parameter projection is used as a modification of the basic gradient estimator

an evolution based tabu search approach to codebook design this paper presents an evolution based tabu search approach etsa to design codebooks with smaller distortion values in vector quantization in the etsa there is no need for users to determine the size of a tabu memory and to specifically define a set of tabu restrictions and a set of aspiration criteria during iterations only the best solution visited is memorized as a tabu point in the search space and the distance from each trial solution to the tabu point is an important factor in the fitness evaluation in population competition the new fitness function plays the roles of the tabu restrictions and the aspiration criteria based on the new fitness function and a parallel evolutionary mechanism the etsa can prevent premature convergence and eventually find a good solution seven grayscale images are used to test the performance of the etsa experimental results show that the etsa performs better than several existing algorithms in terms of the distortion and robustness measures

optimal bilinear control of nonlinear schrodinger equations with singular potentials abstract we consider an optimal bilinear control problem for the nonlinear schrodinger equations with singular potentials we show the well posedness of the problem and the existence of an optimal control in addition the first order optimality system is rigorously derived our results give an improvement for some recent results

paper asymptotic stability of nonlinear singularly perturbed systems using higher order corrections this paper examines a methodology for investigating the asymptotic stability properties of nonlinear singularly perturbed systems this is achieved by constructing the so called zeroth order model uncorrected model based on this model a weighted scalar sum of the lyapunov functions for two lower order subsystems is obtained the estimates of the region of attraction and the upper bound on the perturbed parameter are established it is shown that by extending this methodology to higher order corrected models less conservative results are obtained these considerable improvements are facilitated by introducing a new fast variable in both uncorrected and corrected models

a distance based point reassignment heuristic for the k hyperplane clustering problem abstract we consider the k hyperplane clustering problem where given a set of m points in r n we have to partition the set into k subsets clusters and determine a hyperplane for each of them so as to minimize the sum of the squares of the euclidean distances between the points and the hyperplane of the corresponding clusters we give a nonconvex mixed integer quadratically constrained quadratic programming formulation for the problem since even very small size instances are challenging for state of the art spatial branch and bound solvers like couenne we propose a heuristic in which many critical points are reassigned at each iteration such points which are likely to be ill assigned in the current solution are identified using a distance based criterion and their number is progressively decreased to zero our algorithm outperforms the best available one proposed by bradley and mangasarian on a set of real world and structured randomly generated instances for the largest instances we obtain an average improvement in the solution quality of 54

adaptive robust tracking and model following of uncertain dynamical systems with multiple time delays the problem of robust tracking and model following is considered for a class of linear systems with multiple delayed state perturbations time varying uncertain parameters and disturbance in this note it is assumed that the upper bounds of the delayed state perturbations uncertainties and external disturbances are unknown an improved adaptation law with spl sigma modification is proposed to estimate such unknown bounds and on the basis of the updated values of these unknown bounds a class of continuous memoryless state feedback controllers is constructed for robust tracking of dynamical signals the proposed adaptive robust tracking controller can guarantee that the tracking error decreases asymptotically to zero in the presence of multiple delayed state perturbations time varying uncertain parameters and disturbance in addition it is also shown that this improved adaptation law with spl sigma modification can be applied to the general adaptive control problems to obtain some more exact control results finally a numerical example is given to demonstrate the validity of the results

design issues in adaptive control an integrated approach to the design of practical adaptive control algorithms is presented many existing ideas are brought together and the effect of various design parameters available to a user is explored the theory is extended by showing how the problem of stabilizability of the estimated model can be overcome by running parallel estimators it is shown how asymptotic tracking of deterministic set points can be achieved in the presence of unmodeled dynamics

tabu search for fuzzy optimization and applications in this paper we present a tabu search technique to approximately solve fuzzy optimization problems we demonstrate the performance of the proposed method by applying it to an elementary fuzzy optimization problem other applications of the method to fuzzy linear programming fuzzy regression and the training of fuzzy neural networks are also presented

robustness of absolute stability the stability of a class of single input single output singularly perturbed systems formed by a linear time invariant feedforward block with a sector bounded time varying feedback is considered it is shown that if the reduced order slow subsystem is absolutely stable and the parasitics are asymptotically stable and sufficiently fast then the full system is absolutely stable bounds on the singular perturbation parameter for uniform asymptotic stability and absolute stability are obtained

paper instability analysis and improvement of robustness of adaptive control the effects of unmodeled high frequency dynamics and bounded disturbances on stability and performance of adaptive control schemes are analyzed five possible types of instability mechanisms parameter drift linear instability fast adaptation instability high frequency instability and throughput instability are analyzed using simple examples a procedure is used to construct lyapunov like functions for a modified adaptive controller applied to a dominant plant of relative degree one in the presence of parasitics and disturbances and obtain sufficient conditions under which none of the five types of instability can occur the modified scheme is robust in the sense that it guarantees the existence of a large region of attraction from which all the trajectories remain bounded and the state errors converge exponentially to a much smaller residual set the size of the region of attraction depends on the speed of parasitics in such a way that as the parasitics become infinitely fast the region of attraction becomes the whole space

optimal bilinear control of an abstract schro dinger equation well posedness of abstract quantum mechanical systems is considered and the existence of optimal control of such systems is proved first order optimality systems are derived convergence of the monotone scheme for the solution of the optimality system is proved

a new model reference robust adaptive controller in the presence of unmodeled dynamics and bounded disturbances abstract this paper presents a new model reference robust adaptive controller which does not require a priori knowledge of the norm desired controller parameter vector the algorithm is designed according to a reduced order plant which is assumed to be of minimum phase and of an arbitrary relative degree in the adaptive law the output error plays a dual role in the adjustment of the control parameter vector in the absence of unmodeled dynamics and disturbances the tracking error is shown to be zero when reference input is persistently exciting