breaking the symmetry between interaction and replacement in evolutionary dynamics on graphs we study the evolution of cooperation modeled as symmetric 2 2 games in a population whose structure is split into an interaction graph defining who plays with whom and a replacement graph specifying evolutionary competition we find it is always harder for cooperators to evolve whenever the two graphs do not coincide in the thermodynamic limit the dynamics on both graphs is given by a replicator equation with a rescaled payoff matrix in a rescaled time analytical results are obtained in the pair approximation and for weak selection their validity is confirmed by computer simulations

brief estimation and detection of unknown inputs using optimal fir filter the optimal finite impulse response fir filter is applied to systems with both unknown inputs and noise in order to obtain noise suppressed estimates of the unknown inputs the unknown inputs are modeled as random walk processes and estimated together with the state using the optimal fir filtering algorithm the estimate for each unknown input is shown to be independent of the window initial state and also of other unknown inputs a detection scheme for an unknown input is developed utilizing a test variable independently of other unknown inputs finally numerical examples are given to show the performance of the proposed estimation and detection method

unbiased fir filtering of discrete time polynomial state space models we address an unbiased finite impulse response fir filter for discrete time state space models with polynomial representation of the states the unique l degree polynomial fir filter gain and the estimate variance are found for a general case the noise power gain ng is derived for white gaussian noises in the model and in the measurement the filter does not involve any knowledge about noise in the algorithm it is unstable at short horizons 2 les n les l and inefficient ng exceeds unity in the narrow range l 1 the estimate noise becomes negligible and the filter thus optimal in the sense of zero bias and zero noise having such properties the proposed unbiased fir filter fits well slowly changing with time models an example is given for a two state system

deterministic evolutionary game dynamics in finite populations evolutionary game dynamics describes the spreading of successful strategies in a population of reproducing individuals typically the microscopic definition of strategy spreading is stochastic such that the dynamics becomes deterministic only in infinitely large populations here we present a microscopic birth death process that has a fully deterministic strong selection limit in well mixed populations of any size additionally under weak selection from this process the frequency dependent moran process is recovered this makes it a natural extension of the usual evolutionary dynamics under weak selection we find simple expressions for the fixation probabilities and average fixation times of the process in evolutionary games with two players and two strategies for cyclic games with two players and three strategies we show that the resulting deterministic dynamics crucially depends on the initial condition in a nontrivial way

fast kalman like optimal unbiased fir filtering with applications in this paper an optimal unbiased finite impulse response oufir filter is proposed as a linking solution between the unbiased fir ufir filter and the kalman filter kf we first derive the batch oufir estimator to minimize the mean square error mse subject to the unbiasedness constraint and then find its fast iterative form it is shown that the oufir filter is full horizon fh and that its estimate converges to the kf estimate by increasing the horizon length n as a special feature we note that the fh oufir filter operates almost as fast as the kf several other critical properties of the oufir filter are illustrated based on simulations and practical applications similar to the ufir filter and contrary to the kf the oufir filter is highly insensitive to the initial conditions it has much better robustness than kf against temporary model uncertainties finally the oufir filter allows for ignoring system noise which is typically not well known to the engineer

stochastic dynamics of invasion and fixation we study evolutionary game dynamics in finite populations we analyze an evolutionary process which we call pairwise comparison for which we adopt the ubiquitous fermi distribution function from statistical mechanics the inverse temperature in this process controls the intensity of selection leading to a unified framework for evolutionary dynamics at all intensities of selection from random drift to imitation dynamics we derive a simple closed formula that determines the feasibility of cooperation in finite populations whenever cooperation is modeled in terms of any symmetric two person game in contrast with previous results the present formula is valid at all intensities of selection and for any initial condition we investigate the evolutionary dynamics of cooperators in finite populations and study the interplay between intensity of selection and the remnants of interior fixed points in infinite populations as a function of a given initial number of cooperators showing how this interplay strongly affects the approach to fixation of a given trait in finite populations leading to counterintuitive results at different intensities of selection

stochasticity and evolutionary stability in stochastic dynamical systems different concepts of stability can be obtained in different limits a particularly interesting example is evolutionary game theory which is traditionally based on infinite populations where strict nash equilibria correspond to stable fixed points that are always evolutionarily stable however in finite populations stochastic effects can drive the system away from strict nash equilibria which gives rise to a new concept for evolutionary stability the conventional and the new stability concepts may apparently contradict each other leading to conflicting predictions in large yet finite populations we show that the two concepts can be derived from the frequency dependent moran process in different limits our results help to determine the appropriate stability concept in large finite populations the general validity of our findings is demonstrated showing that the same results are valid employing vastly different co evolutionary processes

a thresholded fuzzy c means algorithm for semi fuzzy clustering abstract in this paper the problem of achieving semi fuzzy or soft clustering of multidimensional data is discussed a technique based on thresholding the results of the fuzzy c means algorithm is introduced the proposed approach is analysed and contrasted with the soft clustering method see s z selim and m a ismail pattern recognition 17 559 568 showing the merits of the new method separation of clusters in the semi fuzzy clustering context is introduced and the use of the proposed technique to measure the degree of separation is explained

evolutionary dynamics intrinsic noise and cycles of cooperation we use analytical techniques based on an expansion in the inverse system size to study the stochastic evolutionary dynamics of finite populations of players interacting in a repeated prisoner s dilemma game we show that a mechanism of amplification of demographic noise can give rise to coherent oscillations in parameter regimes where deterministic descriptions converge to fixed points with complex eigenvalues these quasicycles between cooperation and defection have previously been observed in computer simulations here we provide a systematic and comprehensive analytical characterization of their properties we are able to predict their power spectra as a function of the mutation rate and other model parameters and to compare the relative magnitude of the cycles induced by different types of underlying microscopic dynamics we also extend our analysis to the iterated prisoner s dilemma game with a win stay lose shift strategy appropriate in situations where players are subject to errors of the trembling hand type

coexistence versus extinction in the stochastic cyclic lotka volterra model cyclic dominance of species has been identified as a potential mechanism to maintain biodiversity see e g b kerr m a riley m w feldman and b j m bohannan nature 418 171 2002 and b kirkup and m a riley nature 428 412 2004 through analytical methods supported by numerical simulations we address this issue by studying the properties of a paradigmatic non spatial three species stochastic system namely the rock paper scissors or cyclic lotka volterra model while the deterministic approach rate equations predicts the coexistence of the species resulting in regular yet neutrally stable oscillations of the population densities we demonstrate that fluctuations arising in the system with a finite number of agents drastically alter this picture and are responsible for extinction after long enough time two of the three species die out as main findings we provide analytic estimates and numerical computation of the extinction probability at a given time we also discuss the implications of our results for a broad class of competing population systems

a soft double regularization approach to parametric blind image deconvolution this paper proposes a blind image deconvolution scheme based on soft integration of parametric blur structures conventional blind image deconvolution methods encounter a difficult dilemma of either imposing stringent and inflexible preconditions on the problem formulation or experiencing poor restoration results due to lack of information this paper attempts to address this issue by assessing the relevance of parametric blur information and incorporating the knowledge into the parametric double regularization pdr scheme the pdr method assumes that the actual blur satisfies up to a certain degree of parametric structure as there are many well known parametric blurs in practical applications further it can be tailored flexibly to include other blur types if some prior parametric knowledge of the blur is available a manifold soft parametric modeling technique is proposed to generate the blur manifolds and estimate the fuzzy blur structure the pdr scheme involves the development of the meaningful cost function the estimation of blur support and structure and the optimization of the cost function experimental results show that it is effective in restoring degraded images under different environments

a validity measure for fuzzy clustering the authors present a fuzzy validity criterion based on a validity function which identifies compact and separate fuzzy c partitions without assumptions as to the number of substructures inherent in the data this function depends on the data set geometric distance measure distance between cluster centroids and more importantly on the fuzzy partition generated by any fuzzy algorithm used the function is mathematically justified via its relationship to a well defined hard clustering validity function the separation index for which the condition of uniqueness has already been established the performance of this validity function compares favorably to that of several others the application of this validity function to color image segmentation in a computer color vision system for recognition of ic wafer defects which are otherwise impossible to detect using gray scale image processing is discussed

unsupervised optimal fuzzy clustering this study reports on a method for carrying out fuzzy classification without a priori assumptions on the number of clusters in the data set assessment of cluster validity is based on performance measures using hypervolume and density criteria an algorithm is derived from a combination of the fuzzy k means algorithm and fuzzy maximum likelihood estimation the unsupervised fuzzy partition optimal number of classes algorithm performs well in situations of large variability of cluster shapes densities and number of data points in each cluster the algorithm was tested on different classes of simulated data and on a real data set derived from sleep eeg signal

bootstrap technique in cluster analysis abstract we define a method to estimate the number of clusters in a data set e using the bootstrap technique this approach involves the generation of several fake data sets by sampling patterns with replacement in e bootstrapping for each number k of clusters a measure of stability of the k cluster partitions over the bootstrap samples is used to characterize the significance of the k cluster partition for the original data set the value of k which provides the most stable partitions is the estimate of the number of clusters in e the performance of this new technique is demonstrated on both synthetic and real data and is applied to the segmentation of range images

adaptive algorithm based on least mean p power error criterion for fourier analysis in additive noise this abstract presents a novel adaptive algorithm for the estimation of discrete fourier coefficients dfc of sinusoidal and or quasiperiodic signals in additive noise the algorithm is derived using a least mean p power error criterion it reduces to the conventional lms algorithm when p takes on 2 it is revealed by both analytical results and extensive simulations that the new algorithm for p 3 4 generates much improved dfc estimates in moderate and high snr environments compared with the lms algorithm whereas both have similar degrees of complexity assuming the gaussian property of the estimation error the proposed algorithm including the lms algorithm is analyzed in detail elegant dynamic equations and closed form noise misadjustment expressions are derived and clarified

fir filters and recursive forms for continuous time invariant state space models an fir finite impulse response filter and an fir smoother are introduced for continuous time invariant state space models it is shown in this note that finite impulse responses of the fir filter and smoother can be easily determined by solving a simple riccati type matrix differential equation on a finite interval especially for systems with stationary processes finite impulse responses of the fir filter and smoother become time invariant and can be computed from simpler equations for fast computational purposes recursive forms of the fir filter and smoother are derived by using adjoint variables in this case all gains for recursive forms are shown to be constant

fuzzy joint points based clustering algorithms for large data sets the fuzzy joint points fjp method is one of the successful fuzzy approaches to density based clustering besides the basic fjp method there are other methods based on the fjp approach such as noise robust fjp nrfjp and fuzzy neighborhood dbscan fn dbscan these fjp based methods suffer from the low speed of the fjp algorithm thus applications that deal with large databases cannot benefit from them the modified fjp mfjp method addresses this issue and achieves an improvement in speed but it is not satisfactory from the point of applicability in this work we integrate various methods with fjp to establish an optimal time algorithm an even faster algorithm which uses the fjp approach in a somewhat supervised fashion is also proposed along with theoretic comparison experimental results are presented to show the significant speed improvement which will allow the fjp based methods to be used on large data sets

a new unsupervised approach for fuzzy clustering in this paper a new level based hierarchical approach to the fuzzy clustering problem for spatial data is proposed in this approach each point of the initial set is handled as a fuzzy point of the multidimensional space fuzzy point conical form fuzzy neighbor points fuzzy joint points are defined and their properties are explored it is known that in classical fuzzy clustering the matter of fuzziness is usually a possibility of membership of each element into different classes with different positive degrees from 0 1 in this study the fuzziness of clustering is evaluated as how much in detail the properties of classified elements are investigated in this extent a new fuzzy joint points fjp method which is robust through noises is proposed algorithm of fjp method is developed and some properties of the algorithm are explored also sufficient condition to recognize a hidden optimal structure of clusters is proven the main advantage of the fjp algorithm is that it combines determination of initial clusters cluster validity and direct clustering which are the fundamental stages of a clustering process it is possible to handle the fuzzy properties with various level degrees of details and to recognize individual outlier elements as independent classes by the fjp method this method could be important in biological medical geographical information mapping etc problems

short time fourier analysis via optimal harmonic fir filters the fourier coefficients fcs of quasiperiodic signals are assumed to be in random walk motion in order to represent a broader class a state model for such quasiperiodic signals is derived the optimal short time estimate of the fourier coefficients is obtained via the suggested optimal harmonic fir filter ohff based on this state space signal model the optimal harmonic fir filter can be considered to be a generalization of the discrete fourier transform dft in the sense that it becomes the same as the dft when the state model is for periodic signals and the filter length is equal to the order of the state model the optimal harmonic fir filter derived from the model even with nonzero state noise and measurement noise gives an exact harmonic estimate when an incoming signal is periodic and noiseless it is shown by examples that the ability to suppress noise and the ability to resolve changes of the fourier coefficients can be adjusted by the filter length and the noise covariance of the state model finally the suggested scheme is compared with existing short time fourier analysis methods in a test signal that has time varying fourier coefficients

an exact recursion for the composite nearest neighbor degeneracy for a 2 n lattice space a set theoretic argument is utilized to develop a recursion relation that yields exactly the composite nearest neighbor degeneracy for simple indistinguishable particles distributed on a 2 n lattice space the associated generating functions as well as the expectation of the resulting statistics are also treated

h_2 output feedback control with finite multiple measurement information this paper proposes a new h 2 output feedback control with finite multiple measurement information for discrete time systems with disturbances this control is called an h 2 finite multiple measurement output feedback control h2fmmofc the new control ascertains the h 2 performance for disturbances and the equivalence to the h 2 state feedback control without disturbances on a finite measurement horizon the boundedness and exponential stability of the proposed h2fmmofc are also investigated the gain matrices of the h2fmmofc are obtained by solving a convex problem utilizing the linear matrix inequality tool via a numerical example the h2fmmofc is shown to ensure the h 2 performance as well as the excellent robustness property against unexpected temporary model uncertainty although the uncertainty is not considered in its design

stochastic and boltzmann like models for behavioral changes and their relation to game theory abstract in the last decade stochastic models have shown to be very useful for quantitative modelling of social processes here a configurational master equation for the description of behavioral changes by pair interactions of individuals is developed three kinds of social pair interactions are distinguished avoidance processes compromising processes and imitative processes computational results are presented for a special case of imitative processes the competition of two equivalent strategies they show a phase transition that describes the self organization of a behavioral convention this phase transition is further analyzed by examining the equations for the most probable behavioral distribution which are boltzmann like equations special cases of boltzmann like equations do not obey the h theorem and have oscillatory or even chaotic solutions a suitable taylor approximation leads to the so called game dynamical equations also known as selection mutation equations in the theory of evolution

fir smoothing of discrete time polynomial signals in state space we address a smoothing finite impulse response fir filtering solution for deterministic discrete time signals represented in state space with finite degree polynomials the optimal smoothing fir filter is derived in an exact matrix form requiring the initial state and the measurement noise covariance function the relevant unbiased solution is represented both in the matrix and polynomial forms that do not involve any knowledge about measurement noise and initial state the unique l degree unbiased gain and the noise power gain are derived for a general case the widely used low degree gains are investigated in detail as an example the best linear fit is provided for a two state clock error model

evolutionary prisoner s dilemma game on a square lattice a simplified prisoner s game is studied on a square lattice when the players interacting with their neighbors can follow two strategies to cooperate c or to defect d unconditionally the players updated in random sequence have a chance to adopt one of the neighboring strategies with a probability depending on the payoff difference using monte carlo simulations and dynamical cluster techniques we study the density c of cooperators in the stationary state this system exhibits a continuous transition between the two absorbing states when varying the value of temptation to defect in the limits stackrel ensuremath rightarrow c 0 and 1 we have observed critical transitions belonging to the universality class of directed percolation

coevolutionary dynamics from finite to infinite populations traditionally frequency dependent evolutionary dynamics is described by deterministic replicator dynamics assuming implicitly infinite population sizes only recently have stochastic processes been introduced to study evolutionary dynamics in finite populations however the relationship between deterministic and stochastic approaches remained unclear here we solve this problem by explicitly considering large populations in particular we identify different microscopic stochastic processes that lead to the standard or the adjusted replicator dynamics moreover differences on the individual level can lead to qualitatively different dynamics in asymmetric conflicts and depending on the population size can even invert the direction of the evolutionary process

intrinsically bayesian robust kalman filter an innovation process approach in many contemporary engineering problems model uncertainty is inherent because accurate system identification is virtually impossible owing to system complexity or lack of data on account of availability time or cost the situation can be treated by assuming that the true model belongs to an uncertainty class of models in this context an intrinsically bayesian robust ibr filter is one that is optimal relative to the cost function in the classical sense and the prior distribution over the uncertainty class in the bayesian sense ibr filters have previously been found for both wiener and granulometric morphological filtering in this paper we derive the ibr kalman filter that performs optimally relative to an uncertainty class of state space models introducing the notion of bayesian innovation process and the bayesian orthogonality principle we show how the problem of designing an ibr kalman filter can be reduced to a recursive system similar to the classical kalman recursive equations except with effective counterparts such as the effective kalman gain matrix after deriving the recursive ibr kalman equations for discrete time we use the limiting method to obtain the ibr kalman bucy equations for continuous time finally we demonstrate the utility of the proposed framework for two real world problems sensor networks and gene regulatory network inference

receding horizon recursive state estimation describes a receding horizon discrete time state observer using the deterministic least squares framework the state estimation horizon which determines the number of past measurement samples used to reconstruct the state vector is introduced as a tuning parameter for the proposed state observer a stability result concerning the choice of the state estimation horizon is established it is also shown that the fixed memory receding horizon state observer can be related to the standard dynamic observer by using an appropriate end point state weighting on the estimator cost function

minimum variance unbiased fir filter for discrete time variant systems this paper is concerned with the minimum variance unbiased mvu finite impulse response fir filtering problem for linear system described by discrete time variant state space models an mvu fir filter is derived by minimizing the variance from the unbiased fir ufir filter the relationship between the filter gains of mvu fir ufir and optimal fir ofir filters is derived analytically and the mean square errors mses of different fir filters are compared to provide an insight into the estimation performance simulations provided verify that errors in the mvu fir filter are in between the ufir and ofir filters it is also shown that the mvu fir filter can offer optimal estimates without a prior knowledge of the initial state and exhibits better robustness against temporary modeling uncertainties than the kalman filter

brief a receding horizon unbiased fir filter for discrete time state space models this paper concerns with a new linear finite impulse response fir filter called the receding horizon unbiased fir rhuf filter for the state estimation in discrete time state space models to obtain the rhuf filter linearity unbiasedness and fir structure will be required beforehand in addition to a performance criteria of minimum variance the rhuf filter is obtained by directly solving an optimization problem with the unbiasedness constraint the rhuf filter has time invariance and deadbeat properties the rhuf filter is represented in both a batch form and an iterative form it is shown that the rhuf filter is equivalent to the existing receding horizon kalman fir rhkf filter whose optimality is not clear to understand the former is more systematic and logical while the latter is heuristic due to handling of infinite covariance of the initial state information

fuzzy clustering based on k nearest neighbours rule abstract in cluster analysis several algorithms have been made for partitioning a set of objects into c natural clusters in general this problem is formulated as being an objective function optimization approach however it is known that the function being minimized is nonconvex and hence it may lead to convergence to many local minima i e to different partitions thus the clustering is repeated with different initializations hoping that some runs will lead to the global minimum therefore the performance of these algorithms depends largely on good choice of these initializations the most widely used algorithm using this function is called fuzzy c means algorithm fcma in this paper a new algorithm is proposed to carry out fuzzy clustering without a priori assumptions on initial guesses this algorithm is based on two layer clustering strategy during the first layer the k nearest neighbours decision rule is used then to achieve an optimal partition the second layer involves one iteration of fcma the performance of the proposed algorithm and that of fcma have been tested on six data sets the results obtained show that the new algorithm possesses a number of advantages over the fcma

three and four state rock paper scissors games with diffusion cyclic dominance of three species is a commonly occurring interaction dynamics often denoted the rock paper scissors rps game such a type of interactions is known to promote species coexistence here we generalize recent results of reichenbach et al nature london 448 1046 2007 of a four state variant of the rps game we show that spiral formation takes place only without a conservation law for the total density nevertheless in general fast diffusion can destroy species coexistence we also generalize the four state model to slightly varying reaction rates this is shown both analytically and numerically not to change pattern formation or the effective wavelength of the spirals and therefore not to alter the qualitative properties of the crossover to extinction

three state potts model in combination with the rock scissors paper game we study a three state potts model extended by allowing cyclic dominance between the states as exemplified in the rock scissors paper game monte carlo simulations are performed on a square lattice while varying the temperature and the strength of cyclic dominance it is shown that the critical phase transition from the disordered state to the ordered one is destroyed by the cyclic dominance which yields a self organizing pattern even at low temperatures the differences and similarities are discussed between the present model and half filled driven lattice gases with repulsive interaction

robustness of density based clustering methods with various neighborhood relations cluster analysis is one of the most crucial techniques in statistical data analysis among the clustering methods density based methods have great importance due to their ability to recognize clusters with arbitrary shape in this paper robustness of the clustering methods is handled these methods use distance based neighborhood relations between points in particular dbscan density based spatial clustering of applications with noise algorithm and fn dbscan fuzzy neighborhood dbscan algorithm are analyzed fn dbscan algorithm uses fuzzy neighborhood relation whereas dbscan uses crisp neighborhood relation the main characteristic of the fn dbscan algorithm is that it combines the speed of the dbscan and robustness of the nrfjp noise robust fuzzy joint points algorithms it is observed that the fn dbscan algorithm is more robust than the dbscan algorithm to datasets with various shapes and densities

a continuous time recursive fixed lag smoother converging in finite time in this technical note we propose a new fixed lag smoother that estimates the fixed delayed state for a stochastic continuous time system the estimation error variance of the proposed smoother is minimized under the constraint that the estimated state converges to the real state exactly in finite time after noises or uncertainties disappear for numerical computing the proposed smoother is represented in a recursive form unlike other approaches any additional processes such as batch processing and sampling data through discrete time techniques are not required to achieve the finite time convergence a numerical example is presented to illustrate the finite time convergence of the proposed smoother in comparison with the asymptotic convergence of an optimal smoother

linear optimal fir estimation of discrete time invariant state space models this paper addresses a general p shift linear optimal finite impulse response fir estimator intended for solving universally the problems of filtering p 0 smoothing p 0 of discrete time invariant models in state space an optimal solution is found in the batch form with the initial mean square state function self determined by solving the discrete algebraic riccati equation an unbiased solution represented both in the batch and recursive forms does not involve any knowledge about noise and initial state the mean square errors in both the optimal and unbiased estimates are found via the noise power gain npg and a recursive algorithm for fast computation of the npg is supplied applications are given for fir filtering with fixed receding and full averaging horizons

fir filters and recursive forms for discrete time state space models abstract an fir finite impulse response filter and an fir smoother are introduced for discrete time state space models with system noise the fir structure not only guarantees both the bibo stability and the robustness to some parameter changes but also improves the filter divergence problem it is shown in this paper that impulse responses of both the fir filter and the fir smoother are easily obtained from riccati type difference equations especially for time invariant state space models they are also to be time invariant and finally determined by simple equations on a finite interval for the purpose of implementation recursive forms of the fir filter and smoother are derived using each other as adjoint variables improved characteristics of the fir filter in the divergence problem are demonstrated via a simulation

approaches to the representations and logic operations of fuzzy concepts in the framework of axiomatic fuzzy set theory ii in this paper the representations of fuzzy concepts based on raw data have been investigated within the framework of afs axiomatic fuzzy set theory first a brief review of afs theory is presented and a completely distributive lattice the e i algebra is proposed secondly two kinds of e i algebra representations of fuzzy concepts are derived in detail in order to represent the membership functions of fuzzy concepts in the interval 0 1 the norm of afs algebra is defined and studied finally the relationships of various representations with their advantages and drawbacks are analyzed

recursive limited memory filtering and scattering theory corresp redheffer scattering theory is reviewed in a generalized setting as a method to derive recursive solutions of linear two point boundary value problems tpbvp over arbitrarily varying intervals the results can be used to derive a complete solution for the problem of limited memory or sliding window estimation when a usual state space model for the signal is available recursive limited memory filters are derived for both continuous and discrete time signals

fast design of 2 d linear phase complex fir digital filters by analytical least squares method two dimensional full plane and half plane filters are more general and much better frequency responses can be obtained than the quarter plane filters in this correspondence the analytical least squares method is generalized and extended for designing 2 d full plane and half plane linear phase complex fir digital filters the 2 d filter s coefficients can be effectively determined by use of a closed form transformation matrix and some simple element functions the unique advantage of this technique is that it is very fast without employing iterative optimization procedures and matrix inversions design examples are presented to illustrate the simplicity and efficiency of the proposed method

a receding horizon kalman fir filter for discrete time invariant systems a receding horizon kalman fir filter is presented that combines the kalman filter and the receding horizon strategy when the horizon initial state is assumed to be unknown the suggested filter is a fir filter form which has many good inherent properties it can always be defined irrespective of singularity problems caused by unknown information about the horizon initial state the suggested filter can be represented in either an iterative form or a standard fir form it is also shown that the suggested filter possesses the unbiasedness property and the remarkable deadbeat property irrespective of any horizon initial condition the validity of the suggested filter is illustrated by numerical examples

a kalman filtering approach to short time fourier analysis the problem of estimating time varying harmonic components of a signal measured in noise is considered the approach used is via state estimation two methods are proposed one involving pole placement of a state observer the other using quadratic optimization techniques the result is the development of a new class of filters akin to recursive frequency sampling filters for inclusion in a parallel bank to produce sliding harmonic estimates kalman filtering theory is applied to effect the good performance in noise and the class of filters is parameterized by the design tradeoff between noise rejection and convergence rate these filters can be seen as generalizing the dft

a receding horizon kalman fir filter for linear continuous time systems a receding horizon kalman finite impulse response fir filter is suggested for continuous time systems combining the kalman filter with the receding horizon strategy in the suggested filter the horizon initial state is assumed to be unknown it can always be obtained irrespective of unknown information on the horizon initial state the filter may be the first stochastic fir form for continuous time systems that may have many good inherent properties the suggested filter can be represented in an iterative form and also in a standard fir form the suggested filter turns out to be a remarkable deadbeat observer the validity of the suggested filter is illustrated by numerical examples

fast algorithms for optimal fir filter and smoother of discrete time state space models abstract in this paper fast algorithms are suggested for the optimal fir filter and smoother of discrete time state space models with system noise while the computation order of the previous algorithms for the optimal fir filter and smoother is o n2 with filter length n it is shown in this paper that it can be reduced to o n by modifying the computation sequences and by using the property of the transition matrix once the state space model is given the suggested algorithms become more efficient than multichannel levinson algorithm in addition the fir smoother is extended to a fixed lag fir smoother so that it can estimate the state in the filter window

coevolutionary games on networks we study agents on a network playing an iterated prisoner s dilemma against their neighbors the resulting spatially extended coevolutionary game exhibits stationary states which are nash equilibria after perturbation of these equilibria avalanches of mutations reestablish a stationary state scale free avalanche distributions are observed that are in accordance with calculations from the nash equilibria and a confined branching process the transition from subcritical to critical avalanche dynamics can be traced to a change in the degeneracy of the cooperative macrostate and is observed for many variants of this game

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

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

stochastic differential equations for evolutionary dynamics with demographic noise and mutations we present a general framework to describe the evolutionary dynamics of an arbitrary number of types in finite populations based on stochastic differential equations sdes for large but finite populations this allows us to include demographic noise without requiring explicit simulations instead the population size only rescales the amplitude of the noise moreover this framework admits the inclusion of mutations between different types provided that mutation rates are not too small compared to the inverse population size 1 n this ensures that all types are almost always represented in the population and that the occasional extinction of one type does not result in an extended absence of that type for n 1 this limits the use of sdes but in this case there are well established alternative approximations based on time scale separation we illustrate our approach by a rock scissors paper game with mutations where we demonstrate excellent agreement with simulation based results for sufficiently large populations in the absence of mutations the excellent agreement extends to small population sizes

on recursive discrete fourier transformation it is shown that the recent deadbeat state observer approach to discrete fourier transform evaluation is exactly equivalent to the nonrecursive frequency sampling filters of finite impulse response digital filter design the approach is then extended to consider the kalman filter derived from the signal model and this is demonstrated to be nonrobust and pathological the application of state observers to this problem is then brought into question and it is shown that they have a novel application to signal processing between the extremes of deadbeat and kalman observers