critical behavior of the one dimensional diffusive pair contact process the phase transition of the one dimensional diffusive pair contact process is investigated by n cluster mean field approximations and high precision simulations the n 3 4 cluster approximations exhibit smooth transition line to absorbing state by varying the diffusion rate d with beta 2 2 mean field order parameter exponent of the pair density this contradicts with former n 2 results where two different mean field behavior was found along the transition line extensive dynamical simulations on l 10 5 lattices give estimates for the order parameter exponents of the particles for 0 05 or d or 0 7 these data may support former two distinct class findings however the gap between low and high d exponents is narrower than previously estimated and the possibility for interpreting numerical data as a single class behavior with exponents alpha 0 21 1 beta 0 40 1 assuming logarithmic corrections is shown finite size scaling results are also presented

approximate probabilistic cellular automata for the dynamics of single species populations under discrete logisticlike growth with and without weak allee effects we investigate one dimensional elementary probabilistic cellular automata pca whose dynamics in first order mean field approximation yields discrete logisticlike growth models for a single species unstructured population with nonoverlapping generations beginning with a general six parameter model we find constraints on the transition probabilities of the pca that guarantee that the ensuing approximations make sense in terms of population dynamics and classify the valid combinations thereof several possible models display a negative cubic term that can be interpreted as a weak allee factor we also investigate the conditions under which a one parameter pca derived from the more general six parameter model can generate valid population growth dynamics numerical simulations illustrate the behavior of some of the pca found

the attractor basin portrait of a cellular automaton local space time structures such as domains and the intervening dislocations dominate a wide class of cellular automaton ca behavior for such spatially extended dynamics regular domains vicinities and attractors are introduced as organizing principles to identify the discretized analogs of attractors basins and separatrices structures used in classifying dissipative continuous state dynamical systems we describe the attractor basin portrait of nonlinear elementary ca rule 18 whose global dynamics is largely determined by a single regular attracting domain the latter s basin is analyzed in terms of subbasin and portal structures associated with particle annihilation the conclusion is that the computational complexity of such ca is more apparent than real transducer machines are constructed that automatically identify domain and dislocation structures in space time count the number of dislocations in a spatial pattern and implement an isomorphism between rule 18 and rule 90 we use a transducer to trace dislocation trajectories and confirm that in rule 18 isolated dislocation trajectories as well as a dislocation gas agree extremely well with the classical model of annihilating diffusive particles the ca efficiently transforms randomness of an initial pattern ensemble into a random walk of dislocations in space time

reduced order generalized h_ infty filtering for linear discrete time systems with application to channel equalization this paper investigates the problem of reduced order generalized h infty filtering for linear discrete time systems generalized h infty filtering covers standard h infty filtering as a special case and can make use of the frequency domain characteristics of practical signals moreover reduced order filters are more attractive than full order ones regarding implementability by virtue of the generalized bounded real lemma a new necessary and sufficient condition is proposed for analyzing the generalized h infty performance of the filtering error system to compute reduced order generalized h infty filters a necessary and sufficient condition is then derived in terms of matrix inequalities to solve which iterative linear matrix inequality algorithms are further developed in addition the proposed filter design method is applied to cope with the channel equalization problem examples are finally presented for illustrating the proposed method

filtering on sampled data systems with parametric uncertainty the paper is concerned with the problem of robust h sub spl infin filtering for a class of systems with parametric uncertainties and unknown time delays under sampled measurements the parameter uncertainties considered are real time varying and norm bounded appearing in the state equation an approach has been proposed for the designing of h sub spl infin filters using sampled measurements which would guarantee a prescribed h sub spl infin performance in the continuous time context irrespective of the parameter uncertainties and unknown time delays both cases of finite and infinite horizon filtering are studied it has been shown that the above robust h sub spl infin filtering problem can be solved in terms of differential riccati inequalities with finite discrete jumps

reduced order h_ infty filter design for delta operator systems over multiple frequency intervals in this article a reduced order h infty filter is designed for a delta operator system over multiple frequency intervals by nonsmooth optimization techniques subdifferential analysis is generalized in delta domain for minimizing the maximum value of the h infty performance over the multiple frequency intervals based on the subdifferential analysis an efficient descent algorithm is developed to obtain the reduced order h infty filter compared with some existing algorithms the proposed descent algorithm not only reduces computational complexities but also improves the h infty performance numerical examples are presented to demonstrate effectiveness of the proposed descent algorithm

invertible cellular automata a review abstract in the light of recent developments in the theory of invertible cellular automata we attempt to give a unified presentation of the subject and discuss its relevance to computer science and mathematical physics

mean field theory of avalanches in self organized critical states abstract a new mean field approximation is proposed for the sandpile model of bak tang and wiesenfeld and the distribution of heights in the self organized critical state is calculated we treat several series of successive topples to approximate various avalanches in contrast to the previous mean field theory given by tang and bak which assumes the stationary condition among topples in an avalanche out theory takes into account long term behavior of the system the results are in good agreement with the values estimated by computer simulations even in low dimensions d 2 and 3 higher approximations obtained by including local correlations or by imposing a reducibility condition which is used in the cluster variation method are also shown

h _ infty filtering for discrete time state delayed systems with finite frequency specifications this article researches the problem of finite frequency ff h filtering for linear discrete time state delayed systems the disturbance is assumed to reside in low middle high frequency ranges to reduce the conservatism of the results delay partitioning idea is used to derive a new ff bounded real lemma brl by applying the generalized kalman yakubovich popov lemma two equivalent approaches to the proof of the proposed ff brl are given respectively starting from transfer function and lyapunov krasovskii functional a new ff h filter design method is proposed in terms of solving a set of linear matrix inequalities finally a numerical example clearly demonstrates the merits and effectiveness of the proposed method

presentation functions fixed points and a theory of scaling function dynamics presentation functions provide the time ordered points of the forward dynamics of a system as successive inverse images they generally determine objects constructed on trees regular or otherwise and immediately determine a functional form of the transfer matrix of these systems presentation functions for regular binary trees determine the associated forward dynamics to be that of a period doubling fixed point they are generally parametrized by the trajectory scaling function of the dynamics in a natural way the requirement that the forward dynamics be smooth with a critical point determines a complete set of equations whose solution is the scaling function these equations are compatible with a dynamics in the space of scalings which is conjectured with numerical and intuitive support to possess its solution as a unique globally attracting fixed point it is argued that such dynamics is to be sought as a program for the solution of chaotic dynamics in the course of the exposition new information pertaining to universal mode locking is presented

robust h sub 2 h sub spl infin filtering for linear systems with error variance constraints in this correspondence we consider the robust h sub 2 h sub spl infin filtering problem for linear perturbed systems with steady state error variance constraints the purpose of this multiobjective problem is to design a linear filter that does not depend on the parameter perturbations such that the following three performance requirements are simultaneously satisfied 1 the filtering process is asymptotically stable 2 the steady state variance of the estimation error of each state is not more than the individual prespecified value 3 the transfer function from exogenous noise inputs to error state outputs meets the prespecified h sub spl infin norm upper bound constraint we show that in both continuous and discrete time cases the addressed filtering problem can effectively be solved in terms of the solutions of a couple of algebraic riccati like equations inequalities we present both the existence conditions and the explicit expression of desired robust filters an illustrative numerical example is provided to demonstrate the flexibility of the proposed design approach

criticality in cellular automata abstract using recent results obtained for the transition to turbulence via spatiotemporal intermittency in extended dynamical systems critical cellular automata rules are built thanks to a systematic procedure the continuous phase transition observed in a coupled map lattice is translated into a sequence of cellular automata rules for which the critical properties of the original system are reproduced as precisely as desired it is shown that criticality as understood from the point of view of statistical mechanics is intimately related to the various characteristics of wolfram s class iv rules this suggests in turn the picture of a critical surface in the space of rules and provides the basis for a discussion of the problem of classification schemes we review recent cellular automata studies where questions linked to criticality arise and argue that they are unified in light of our results leaving the relationship between computational and statistical characterizations of critical rules as the central problem for future studies on this subject

robust spl hscr sub spl infin filtering for uncertain discrete time state delayed systems this paper addresses the problem of robust spl hscr sub spl infin filtering for linear discrete time systems subject to parameter uncertainties in the system state space model and with multiple time delays in the state variables the uncertain parameters are supposed to belong to a given convex bounded polyhedral domain a methodology is developed to design a stable linear filter that assures asymptotic stability and a prescribed spl hscr sub spl infin performance for the filtering error irrespective of the uncertainty and the time delays the proposed design is given in terms of linear matrix inequalities which has the advantage in that it can be implemented numerically very efficiently

variance constrained cal h _ infty filtering for a class of nonlinear time varying systems with multiple missing measurements the finite horizon case this paper is concerned with the robust h finite horizon filtering problem for a class of uncertain nonlinear discrete time varying stochastic systems with multiple missing measurements and error variance constraints all the system parameters are time varying and the uncertainty enters into the state matrix the measurement missing phenomenon occurs in a random way and the missing probability for each sensor is governed by an individual random variable satisfying a certain probabilistic distribution in the interval the stochastic nonlinearities under consideration here are described by statistical means which can cover several classes of well studied nonlinearities sufficient conditions are derived for a finite horizon filter to satisfy both the estimation error variance constraints and the prescribed h performance requirement these conditions are expressed in terms of the feasibility of a series of recursive linear matrix inequalities rlmis simulation results demonstrate the effectiveness of the developed filter design scheme

new approach to mixed h sub 2 h sub spl infin filtering for polytopic discrete time systems this paper revisits the problem of mixed h sub 2 h sub spl infin filtering for polytopic discrete time systems differing from previous results in the quadratic framework the filter design makes full use of the parameter dependent stability idea not only is the filter dependent of the parameters which are assumed to reside in a polytope and be measurable online but in addition the lyapunov matrices are different for the entire polytope domain as well as for different channels with respect to the mixed performances these ideas are realized by introducing additional slack variables to the well established performance conditions and by employing new bounding techniques which results in a much less conservative filter design method a numerical example is presented to illustrate the effectiveness and advantage of the developed filter design method

a hierarchical classification of cellular automata abstract a central issue in the theory of cellular automata is classification a classification is a structure imposed on the space of automata which groups together cellular automata with related properties in this paper a classification based on the action of cellular automata on n step markov measures is presented this classification is hierarchical and parametric an algorithm is described which efficiently constructs all rules in a given class at a given level of the hierarchy the utility of the classification is explored in the study of the invariant statistical properties of next nearest neighbor cellular automata on two states per cell

crossovers from parity conserving to directed percolation universality the crossover behavior of various models exhibiting phase transition to absorbing phase with parity conserving class has been investigated by numerical simulations and cluster mean field method in case of models exhibiting z 2 symmetric absorbing phases the cellular automaton version of the nonequilibrium kinetic ising model nekimca and a stochastic cellular automaton invented by grassberger krause and von der twer j phys a 17 l105 1984 the introduction of an external symmetry breaking field causes a crossover to kink parity conserving models characterized by dynamical scaling of the directed percolation dp and the crossover exponent 1 phi approximately equal to 0 53 2 in the case of a branching and annihilating random walk model with an even number of offspring dual to nekimca the introduction of spontaneous particle decay destroys the parity conservation and results in a crossover to the dp class characterized by the crossover exponent 1 phi approximately equal to 0 205 5 the two different kinds of crossover operators cannot be mapped onto each other and the resulting models show a diversity within the dp universality class in one dimension these subclasses differ in cluster scaling exponents

state estimation for discrete time markov jump linear systems with time correlated and mode dependent measurement noise abstract the state estimation problem for discrete time markov jump linear systems corrupted by time correlated and mode dependent measurement noise is considered where the time correlated and mode dependent measurement noise is described via a discrete time stochastic system with markov parameter and kronecker delta function by defining the measurement noise in this manner both time correlation and periodic step change caused by the change of system environment or structure can be embodied in the measurement noise a novel distributed measurement differencing method is applied to the problem of state estimation under consideration so that two algorithms are obtained using some results presented in this paper the first algorithm is optimal in the sense of minimum mean square error which can exactly compute the minimum mean square error estimate of system state the second algorithm is suboptimal and the suboptimality of the algorithm is caused by using some gaussian hypotheses the two proposed algorithms are recursive and the proposed suboptimal algorithm has a time independent complexity the performance of the proposed suboptimal algorithm is illustrated using computer simulations

robust h filter design with past output measurements for uncertain discrete time systems this paper is concerned with the design of robust filters for uncertain discrete time linear systems the uncertainty is assumed to be time invariant belonging to a polytope an upper bound to the h norm of the transfer function from the system input to the filtering error is used as performance criterion the novelty is that the resulting robust filter has order greater than the order of the system being filtered by incorporating past output measurements the design conditions are expressed in terms of convex problems constrained by linear matrix inequalities performance is assessed through parameter dependent lyapunov functions the results generalize existing robust filtering procedures without bringing in any additional conservatism simple numerical examples illustrate the procedure

brief paper robust finite frequency h filtering for uncertain 2 d roesser systems this paper investigates the problem of robust finite frequency ff h filtering for two dimensional 2 d roesser models with polytopic uncertainties our attention is focused on designing filters guaranteeing the robustly asymptotic stability and ff h disturbance attenuation level of the filtering error system by the parameter dependent idea and the generalized kalman yakubovich popov lemma for 2 d roesser models the existence conditions of robust ff h filters are obtained in terms of solving an optimization problem which is more general than the existing results an example is given to validate the proposed methods the contribution of the paper is twofold 1 systematic methods are proposed for designing ff h filters for roesser models 2 an improved strategy has been presented to deal with the robust h filter design for roesser models

fixed order robust h sub spl infin filter design for markovian jump systems with uncertain switching probabilities this paper discusses the fixed order robust h sub spl infin filtering problem for a class of markovian jump linear systems with uncertain switching probabilities the uncertainties under consideration are assumed to be norm bounded in the system matrices and to be elementwise bounded in the mode transition rate matrix respectively first a criterion based on linear matrix inequalities is provided for testing the h sub spl infin filtering level of a filter over all the admissible uncertainties then a sufficient condition for the existence of the fixed order robust h sub spl infin filters is established in terms of the solvability of a set of linear matrix inequalities with equality constraints to determine the filter a globally convergent algorithm involving convex optimization is suggested finally a numerical example is used to illustrate that the developed theory is more effective than the existing results

a note on the rule classification for square homogeneous cellular automata abstract we give the dynamical sense for the qualitative ideas of wolfram s and stauffer s classifications for the elementary two dimensional cellular automata with four nearest neighbours the new sense arises from the attracting role of the so called subshifts

h filtering for descriptor systems with strict lmi conditions in this paper the problem of h filtering for descriptor systems with strict linear matrix inequalities lmis is investigated the necessary and sufficient conditions for the solvability and the expression of the solution are obtained for both continuous time and discrete time descriptor systems the proposed continuous time filters extend the results presented by xu and lam 2006 by removing semi definite conditions and introducing new formulation for the filter parameters which does not require determination of intermediate variables for discrete time descriptors differing from existing methods that provide sufficient conditions only two necessary and sufficient conditions are introduced new proposed design methods propose less conservative results comparing with existing works two numerical examples are presented to demonstrate the applicability of the proposed methods

robust h sub spl infin filtering for stochastic time delay systems with missing measurements in this paper the robust hinfin filtering problem is studied for stochastic uncertain discrete time delay systems with missing measurements the missing measurements are described by a binary switching sequence satisfying a conditional probability distribution we aim to design filters such that for all possible missing observations and all admissible parameter uncertainties the filtering error system is exponentially mean square stable and the prescribed hinfin performance constraint is met in terms of certain linear matrix inequalities lmis sufficient conditions for the solvability of the addressed problem are obtained when these lmis are feasible an explicit expression of a desired robust hinfin filter is also given an optimization problem is subsequently formulated by optimizing the hinfin filtering performances finally a numerical example is provided to demonstrate the effectiveness and applicability of the proposed design approach

design of delta sigma modulators via generalized kalman yakubovich popov lemma this paper is concerned with the design of delta sigma modulators via the generalized kalman yakubovich popov lemma the shaped noise transfer function ntf is assumed to have infinite impulse response and the optimization objective is minimizing the maximum magnitude of the ntf over the signal frequency band by virtue of the gkyp lemma the optimization of an ntf is converted into a minimization problem subject to quadratic matrix inequalities and then an iterative algorithm is proposed to solve this alternative minimization problem each iteration of the algorithm contains linear matrix inequality constraints only and can be effectively solved by the existing numerical software packages moreover specifications on the ntf zeros are also integrated in the design method a design example demonstrates that the proposed design method has an advantage over the benchmark one in improving the signal to noise ratio

is there a sharp phase transition for deterministic cellular automata abstract previous work has suggested that there is a kind of phase transition between deterministic automata exhibiting periodic behavior and those exhibiting chaotic behavior however unlike the usual phase transitions of physics this transition takes place over a range of values of the parameter rather than at a specific value the present paper asks whether the transition can be made sharp either by taking the limit of an infinitely large rule table or by changing the parameter in terms of which the space of automata is explored we find strong evidence that for the class of automata we consider the transition does become sharp in the limit of an infinite number of symbols the size of the neighborhood being held fixed our work also suggests an alternative parameter in terms of which it is likely that the transition will become fairly sharp even if one does not increase the number of symbols in the course of our analysis we find that mean field theory which is our main tool gives surprisingly good predictions of the statistical properties of the class of automata we consider

numerical methods on shishkin meshes for linear convection diffusion problems abstract on the unit square we consider a model singularly perturbed convection diffusion problem whose solution contains exponential boundary and corner layers shishkin meshes are frequently used to solve such problems numerically we compare and evaluate the performance of several numerical methods on these meshes and summarise the theoretical convergence results available in the literature

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

comment on garden of eden states in a traffic model revisited recently huang and lin suggested a combination of two successful mean field theories the 2 cluster approximation and paradisical mean field for the nagel schreckenberg cellular automaton model of traffic flow we argue that this new approximation is inconsistent since it violates the kolmogorov conditions

optimal estimation for discrete time linear systems in the presence of multiplicative and time correlated additive measurement noises in this paper the state estimation problem for discrete time linear systems influenced by multiplicative and time correlated additive measurement noises is considered where the multiplicative noises are zero mean white noise sequences and the time correlated additive noise is described by a linear system model with white noise an optimal linear estimator for the system under consideration is proposed which does not require computing the inverse of state transition matrix the proposed estimator has a recursive structure and has time independent computation and storage load computer simulations are carried out to demonstrate the performance of the proposed estimator the simulation results show the superiority of the proposed estimator

re entrant behaviour in the domany kinzel cellular automaton we present numerical and analytical results for a special kind of one dimensional probabilistic cellular automaton the so called domany kinzel automaton it is shown that the phase boundary separating the active and the recently found chaotic phase exhibits re entrant behaviour furthermore exact results for the p2 0 line are discussed

kalman filtering for continuous time uncertain systems with markovian jumping parameters studies the problem of kalman filtering for a class of uncertain linear continuous time systems with markovian jumping parameters the system under consideration is subjected to time varying norm bounded parameter uncertainties in the state and measurement equations stochastic quadratic stability of the above system is analyzed a state estimator is designed such that the covariance of the estimation error is guaranteed to be within a certain bound for all admissible uncertainties which is in terms of solutions of two sets of coupled algebraic riccati equations

extended kalman filtering with stochastic nonlinearities and multiple missing measurements in this paper the extended kalman filtering problem is investigated for a class of nonlinear systems with multiple missing measurements over a finite horizon both deterministic and stochastic nonlinearities are included in the system model where the stochastic nonlinearities are described by statistical means that could reflect the multiplicative stochastic disturbances the phenomenon of measurement missing occurs in a random way and the missing probability for each sensor is governed by an individual random variable satisfying a certain probability distribution over the interval 0 1 such a probability distribution is allowed to be any commonly used distribution over the interval 0 1 with known conditional probability the aim of the addressed filtering problem is to design a filter such that in the presence of both the stochastic nonlinearities and multiple missing measurements there exists an upper bound for the filtering error covariance subsequently such an upper bound is minimized by properly designing the filter gain at each sampling instant it is shown that the desired filter can be obtained in terms of the solutions to two riccati like difference equations that are of a form suitable for recursive computation in online applications an illustrative example is given to demonstrate the effectiveness of the proposed filter design scheme

local structure theory for cellular automata abstract the mean field theory for cellular automata wolfram and schulman and seiden is generalized to the local structure theory the local structure theory is a sequence of finitely parameterized models of the statistical features of a cellular automaton s evolution the n th model in the sequence takes into account correlations in terms of the probability of blocks of n states a class of measures the n block measures is introduced the local structure operator of order n maps n block measures to n block measures in a manner which reflects the cellular automaton map on blocks of states the fixed points of the map on measures approximate the invariant measures of the cellular automaton the ability of the local structure theory to model evolution from uncorrelated initial distributions is studied the theory gives exact results in simple cases in more complex cases monte carlo numerical experiments suggest that an accurate statistical portrait of cellular automaton evolution is obtained the invariant measures of a cellular automaton and the stability of these measures may be obtained from the local structure theory the local structure theory appears to be a powerful method for characterization and classification of cellular automata nearest neighbor cellular automata with two states per cell are studied using this method

cellular automaton rules conserving the number of active sites this paper shows how to determine all of the unidimensional two state cellular automaton rules of a given number of inputs which conserve the number of active sites these rules have to satisfy a necessary and sufficient condition if the active sites are viewed as cells occupied by identical particles these cellular automaton rules represent evolution operators of systems of identical interacting particles whose total number is conserved some of these rules which allow motion in both directions mimic ensembles of one dimensional pseudorandom walkers numerical evidence indicates that the corresponding stochastic processes might be non gaussian

nonclassical critical exponents out of mean field results abstract we consider a mean field systematic approach to study the critical behavior of cooperative models the sequence of approximate mean field results are analyzed by the introduction of a scaling hypothesis from which we can obtain the critical parameter as well as the nonclassical critical exponents the method is applied to the domany kinzel probabilistic cellular automaton and to the two dimensional glauber ising model

filtering for discrete fuzzy stochastic systems with sensor nonlinearities this paper deals with the filtering problem for discrete time fuzzy stochastic systems with sensor nonlinearities there exist time varying parameter uncertainties and random noise depending on state and external disturbance the characteristic of nonlinear sensor is handled by a decomposition method by means of the parallel distributed compensation technique the design method of the robust h filter is presented sufficient conditions for the stochastic stability of the filtering error systems are derived such that the filter parameters can be explicitly obtained simulation results are given to illustrate the proposed method

algebraic properties of cellular automata cellular automata are discrete dynamical systems of simple construction but complex and varied behaviour algebraic techniques are used to give an extensive analysis of the global properties of a class of finite cellular automata the complete structure of state transition diagrams is derived in terms of algebraic and number theoretical quantities the systems are usually irreversible and are found to evolve through transients to attractors consisting of cycles sometimes containing a large number of configurations

long range correlations in chaotic cellular automata abstract one dimensional cellular automata of class 3 are studied as models for spatially extended chaotic systems their irreversible dynamics can generate weak but long range spatial correlations the one labelled 22 by wolfram is treated in an exemplary way its stationary state is approximated by a markov chain the regular grammar underlying the markov chain is chosen with care so that it includes a maximum amount of information on the stationary state the approximation can be performed without simulating the dynamics of the cellular automaton 22 and reproduces its long range correlations qualitatively their origin is explained intuitively and they are argued to be a frequent feature of cellular automata with more complicated rules

evolutionary matching pennies game on bipartite regular networks evolutionary games are studied here with two types of players located on a chessboard or on a bipartite random regular graph each player s income comes from matching pennies games played with the four neighbors the players can modify their own strategies according to a myopic strategy update resembling the glauber dynamics for the kinetic ising model this dynamical rule drives the system into a stationary state where the two strategies are present with the same probability without correlations between the nearest neighbors while a weak correlation is induced between the second and the third neighbors in stationary states the deviation from the detailed balance is quantified by the evaluation of entropy production finally our analysis is extended to evolutionary games where the uniform pair interactions are composed of an anticoordination game and a weak matching pennies game this system preserves the ising type order disorder transitions at a critical noise level decreasing with the strength of the matching pennies component for both networks

randomly evolving idiotypic networks modular mean field theory we develop a modular mean field theory for a minimalistic model of the idiotypic network the model comprises the random influx of new idiotypes and a deterministic selection it describes the evolution of the idiotypic network towards complex modular architectures the building principles of which are known the nodes of the network can be classified into groups of nodes the modules which share statistical properties each node experiences only the mean influence of the groups to which it is linked given the size of the groups and linking between them the statistical properties such as mean occupation mean lifetime and mean number of occupied neighbors are calculated for a variety of patterns and compared with simulations for a pattern which consists of pairs of occupied nodes correlations are taken into account

fuzzy weighting dependent approach to h_ infty filter design for time delay fuzzy systems this correspondence is concerned with the hinfin filter design for takagi sugeno t s fuzzy systems with state delay a delay dependent design method is proposed in terms of linear matrix inequalities lmi the main contribution is the use of fuzzy weighting dependent lyapunov functionals which can reduce the conservatism arisen from the quadratic lyapunov functional approach an illustrative example is given to show the effectiveness of the present method

suboptimal reduced order filtering through an lmi based method this paper addresses the reduced order filtering design problem for continuous time linear systems the h2 and hinfin norms of the estimation error used as performance criteria are discussed and a new linear matrix inequalities lmi based method for reduced order filter design is proposed differently from the methods available in the literature to date the one presented here does not solve the associated nonconvex problem by means of an appropriate optimization procedure it is based on the a priori determination of a certain matrix that simultaneously rends convex the problem to be solved and reduces the suboptimality degree of the solution its efficiency is tested by means of two examples the first one borrowed from the literature allows the comparison of our method for the h2 norm case with three earlier techniques one of them being the well known balanced truncation the second one of higher order consists of the estimation of the displacement of a tapered bar using an hinfin norm criterion

computation theoretic aspects of cellular automata abstract cellular automata may be viewed as computational models of complex systems they also can be seen as continuous functions on a particular family of compact metric spaces this paper surveys results largely motivated by an attempt to answer questions posed by the latter approach by means of techniques from the former

h sub 2 h sub spl infin filter design for systems with polytope bounded uncertainty this correspondence presents a strategy for robust h2 h infin filtering design with regional pole placement applied to discrete or continuous time linear time invariant lti systems with polytope bounded uncertainty to overcome the conservatism of linear matrix inequality lmi based formulations the proposed design approach is based on a multiobjective optimization algorithm applied directly in the space of the filter parameters the h2 and hinfin norms computed in all polytope vertices and in possible worst case interior points are taken as the optimization objectives a combination of branch and bound algorithm and lmi guaranteed cost formulations is applied to compute the coordinates of the worst case norms and to validate the filter design to the whole polytope examples are presented to illustrate the effectiveness of the proposed strategy

critical discontinuous phase transition in the threshold contact process we analyze a threshold contact process on a square lattice in which particles are created on empty sites with at least two neighboring particles and are annihilated spontaneously we show by means of monte carlo simulations that the process undergoes a discontinuous phase transition at a definite value of the annihilation parameter in accordance with the gibbs phase rule and that the discontinuous transition exhibits critical behavior the simulations were performed by using boundary conditions in which the sites of the border of the lattice are permanently occupied by particles

classification of networks of automata by dynamical mean field theory dynamical mean field theory is used to classify the 65536 different networks of binary automata on a square lattice with nearest neighbour interactions application of mean field theory gives 700 different mean field classes which fall in seven classes of different asymptotic dynamics characterised by fixed points and 2 cycles

min max approximation of transfer functions with application to filter design this paper investigates the problem of frequency specific fs model approximation of transfer functions using a min max approach the objective is to find an approximation model for a transfer function such that the maximum error gain over a specific frequency range is minimized first a linear matrix inequality condition characterizing the fs gain of a transfer function is derived by using the generalized kalman yakubovich popov lemma and then a simple iterative approach is proposed to optimize the approximation model numerical experiments show that the proposed approach can produce better approximation models over a specific frequency range than some existing approaches moreover it is indicated how to apply the proposed approximation approach to the design problem of infinite impulsive response digital filters and design examples clearly illustrate that the proposed design flow can generate filters comparable with the latest design method

generalized kyp lemma unified frequency domain inequalities with design applications the celebrated kalman yakubovic spl caron popov kyp lemma establishes the equivalence between a frequency domain inequality fdi and a linear matrix inequality and has played one of the most fundamental roles in systems and control theory this paper first develops a necessary and sufficient condition for an s procedure to be lossless and uses the result to generalize the kyp lemma in two aspects the frequency range and the class of systems and to unify various existing versions by a single theorem in particular our result covers fdis in finite frequency intervals for both continuous discrete time settings as opposed to the standard infinite frequency range the class of systems for which fdis are considered is no longer constrained to be proper and nonproper transfer functions including polynomials can also be treated we study implications of this generalization and develop a proper interface between the basic result and various engineering applications specifically it is shown that our result allows us to solve a certain class of system design problems with multiple specifications on the gain phase properties in several frequency ranges the method is illustrated by numerical design examples of digital filters and proportional integral derivative controllers

phenomenology of nonlocal cellular automata dynamical systems with nonlocal connections have potential applications to economic and biological systems this paper studies the dynamics of nonlocal cellular automata in particular all two state three input nonlocal cellular automata are classified according to the dynamical behavior starting from random initial configurations and random wirings although it is observed that sometimes a rule can have different dynamical behaviors with different wirings the nonlocal cellular automata rule space is studied using a mean field parametrization which is ideal for the situation of random wiring nonlocal cellular automata can be considered as computers carrying out computation at the level of each component their computational abilities are studied from the point of view of whether they contain many basic logical gates in particular i ask the question of whether a three input cellular automaton rule contains the three fundamental logical gates two input rules and and or and one input rule not a particularly interesting edge of chaos nonlocal cellular automaton the rule 184 is studied in detail it is a system of coupled selectors or multiplexers it is also part of the fredkin s gate a proposed fundamental gate for conservative computations this rule exhibits irregular fluctuations of density large coherent structures and long transient times

robust and reduced order filtering new lmi based characterizations and methods this paper addresses several challenging problems of robust filtering we derive new linear matrix inequality lmi characterizations of minimum variance or h sub 2 performance and demonstrate that they allow the use of parameter dependent lyapunov functions while preserving tractability of the problem the resulting conditions are less conservative than earlier techniques which are restricted to fixed not parameter dependent lyapunov functions the remainder of the paper discusses reduced order filter problems new lmi based nonconvex optimization formulations are introduced for the existence of reduced order filters and several efficient optimization algorithms of local and global optimization are proposed nontrivial and less conservative relaxation techniques are presented as well the viability and efficiency of the proposed approaches are then illustrated through computational experiments and comparisons with existing methods