adaptive minimum bit error rate equalization for binary signaling we consider the design and adaptation of a linear equalizer with a finite number of coefficients in the context of a classical linear intersymbol interference channel with gaussian noise and a memoryless decision device if the number of equalizer coefficients is sufficient the popular minimum mean squared error mmse linear equalizer closely approximates the optimal linear equalizer that directly minimizes bit error rate ber however when the number of equalizer coefficients is insufficient to approximate the channel inverse the minimum ber equalizer can outperform the mmse equalizer by as much as 16 db in certain cases we propose a simple stochastic adaptive algorithm for realizing the minimum ber equalizer compared to the least mean square algorithm the proposed algorithm can provide a substantial reduction in ber with no increase in complexity

design of fuzzy control systems with guaranteed stability abstract this paper addresses the analysis and design of fuzzy control systems the fuzzy systems are represented by a family of local state space models with aggregation the controller is designed by considering each local state feedback controller and a compensating controller the compensating controller is based on the well known variable structure control theory it is shown that this controller guarantees global stability of the closed loop fuzzy system the issues of a state observer for the fuzzy system is also addressed a numerical example and an example of nonlinear system control are given for demonstration of the performance of the method

on extensions of free nilpotent lie algebras of type 2 in this paper we study the structure of lie algebras which have free t nilpotent lie algebras n2 t of type 2 as nilradical and give a detailed construction for them we prove that the dimension of any lie algebra g of this class is dimn2 t k if g is solvable k 2 otherwise the levi subalgebra of g is sl2 k the split simple 3 dimensional lie algebra of 2 2 matrices of trace zero and then k 4 as an application of the main results we get the classification over algebraically closed fields of lie algebras with nilradical n2 1 n2 2 and n2 3

low dimensional real lie algebras all real lie algebras of dimension up to 8 that admit a nontrivial levi decomposition are found

n dimensional superintegrable systems from symplectic realizations of lie coalgebras the construction of n dimensional nd integrable systems from coalgebras is reviewed in the case of poisson coalgebras a necessary condition for the integrability of the nd coalgebra hamiltonian coming from a given coalgebra is obtained in terms of the dimension of the symplectic realization and the number of nonlinear casimir functions from this viewpoint the full set of three four and five dimensional lie poisson coalgebras is analysed and many new families of multiparametric nd integrable systems coming from the cases that fulfil the integrability condition are obtained including the explicit form of the integrals of the motion the superintegrability of these hamiltonians is also emphasized and the generalization of the whole construction to the quantum mechanical case is straightforward

direct digital control auto tuning and supervision using fuzzy logic abstract we describe algorithms for the design and implementation of fuzzy logic controllers and present results of their application to temperature control of a heated air stream auto tuning methods are studied for adjustment of conventional parameters to compensate for actual values of control variables or of external variables and for improvement of performance from observation of results conditions for verifying the consistency of the linguistic protocol with its implementation are presented in an appendix

classification of solvable lie algebras with a given nilradical by means of solvable extensions of its subalgebras abstract we construct all solvable lie algebras with a specific n dimensional nilradical n n 3 which contains the previously studied filiform n 2 dimensional nilpotent algebra n n 2 1 as a subalgebra but not as an ideal rather surprisingly it turns out that the classification of such solvable algebras can be deduced from the classification of solvable algebras with the nilradical n n 2 1 also the sets of invariants of coadjoint representation of n n 3 and its solvable extensions are deduced from this reduction in several cases they have polynomial bases i e the invariants of the respective solvable algebra can be chosen to be casimir invariants in its enveloping algebra

solvable lie algebras of dimension six six dimensional solvable lie algebras over the field of real numbers that possess nilradicals of dimension four are classified into equivalence classes this completes mubarakzyanov s classification of the real six dimensional solvable lie algebras

extending the functional equivalence of radial basis function networks and fuzzy inference systems we establish the functional equivalence of a generalized class of gaussian radial basis function rbfs networks and the full takagi sugeno model 1983 of fuzzy inference this generalizes an existing result which applies to the standard gaussian rbf network and a restricted form of the takagi sugeno fuzzy system the more general framework allows the removal of some of the restrictive conditions of the previous result

fuzzy logic controller design utilizing multiple contending software agents the concept of fuzzy logic control based on a distributed multi agent system is described in this paper it is shown that a system composed of a number of cooperating and contending intelligent agents can perform well in terms of sensing the measured input data and producing output control signals each agent s implementation consists of a number of fuzzy logic control rules although at each time step all the agents compete for the control of the system by proposing a possible action the main controller chooses among the selections based on a number of evaluation criteria it is through the simultaneous cooperation to overall control the system and competition to take control at a given time that the system yields convincing results the effectiveness of the novel methodology is illustrated through the control of a robotic system that keeps an inverted pendulum in balance while being perturbed by a human force in addition the proposed methodology is applied to the practical control of a service mobile manipulator interacting with a human it is shown that fuzzy logic control combined with intelligent multi agent architecture can yield effective and efficient intelligent controllers

an interpretation of membership functions and the properties of general probabilistic operators as fuzzy set operators part i case of type 1 fuzzy sets abstract the grade of membership is assumed to be determined as the relative size of evidence grounds supporting a given element s membership compared to all the necessary reference grounds relevant to the judgement of membership grade and is interpreted as conditional probability conditioned by the given element by assigning the dependence relations of the evidence grounds to those of the fuzzy subsets the set operators are defined as probabilistic operators with dependence relations the properties of the operators are studied and it is found that with type 1 fuzzy subsets they satisfy all the basic properties satisfied by the crisp set operators illustrative examples are given describing the construction of membership functions and the selection of set operators in relation to the situations to be modelled the proposed operators a kind of parametrized operators with an index value of the dependence relations as a parameter cover the entire operators ranging from the so called max min operators to the bold union intersection operators an extension of the above operator range is also discussed

membership function as an evaluation abstract after giving an overview of the different kinds of mathematical forms of the membership functions we extract the different demands and determine the rational class of the membership functions from the generalisation of our result we can determine it giving four parameters the internal a b the sharpness and the decision level we show the correspondence of this membership function to the zimmermann zysno model and using their empirical data we also test our mathematical model we also show the connections between the operators of the evaluation and membership functions which gives the generalisation of this concept in a more general form as a special case we can get a linear form

fuzzy rules fuzzy functions a combination of logic and arithmetic operations for fuzzy numbers a general class of fuzzy numbers is introduced triangular fuzzy numbers tfns in extended representation this representation allows the operations of fuzzy arithmetic as well as fuzzy logic to be defined within this class of extended tfn at least approximately this leads naturally to the definition of fuzzy functions and it is shown that sugeno type fuzzy rules can be expressed by such functions in this context the sugeno controller is able to use fuzzy numbers as input variables thus extending the original concept of using crisp values as input

adaptive minimum ber linear multiuser detection for ds cdma signals in multipath channels the problem of constructing adaptive minimum bit error rate mber linear multiuser detectors is considered for direct sequence code division multiple access ds cdma signals transmitted through multipath channels based on the approach of kernel density estimation for approximating the bit error rate ber from training data a least mean squares lms style stochastic gradient adaptive algorithm is developed for training linear multiuser detectors computer simulation is used to study the convergence speed and steady state ber misadjustment of this adaptive mber linear multiuser detector and the results show that it outperforms an existing lms style adaptive mber algorithm presented by yeh et al see proc globecom sydney australia p 3590 95 1998

robust h sub spl infin control for uncertain discrete time delay fuzzy systems via output feedback controllers this work investigates the problem of robust output feedback h sub spl infin control for a class of uncertain discrete time fuzzy systems with time delays the state space takagi sugeno fuzzy model with time delays and norm bounded parameter uncertainties is adopted the purpose is the design of a full order fuzzy dynamic output feedback controller which ensures the robust asymptotic stability of the closed loop system and guarantees an h sub spl infin norm bound constraint on disturbance attenuation for all admissible uncertainties in terms of linear matrix inequalities lmis a sufficient condition for the solvability of this problem is presented explicit expressions of a desired output feedback controller are proposed when the given lmis are feasible the effectiveness and the applicability of the proposed design approach are demonstrated by applying this to the problem of robust h sub spl infin control for a class of uncertain nonlinear discrete delay systems

structure of real lie algebras abstract restrictions are presented on the radical structure of an indecomposable lie algebra these restrictions are consequences of both the levi theorem and the representation theory of semisimple lie algebras as an application all real lie algebras of dimension 9 that admit a nontrivial levi decomposition are found these algebras are expressed in terms of known solvable and simple algebras of smaller dimensions and in terms of representations of simple algebras

fuzzy adaptive learning control network with on line neural learning abstract this paper addresses the structure and the associated on line learning algorithms of a feedforward multilayered connectionist network for realizing the basic elements and functions of a traditional fuzzy logic controller the proposed fuzzy adaptive learning control network falcon can be contrasted with the traditional fuzzy logic control systems in their network structure and learning ability the connectionist structure of the proposed falcon can be constructed from training examples by neural learning techniques to find proper fuzzy partitions membership functions and fuzzy logic rules two complementary on line structure parameter learning algorithms falcon fsm and falcon art are proposed for constructing the falcon dynamically the falcon fsm combines the backpropagation learning scheme for parameter learning and a fuzzy similarity measure for structure learning the falcon fsn can find proper fuzzy logic rules membership functions and the size of output partitions simultaneously in the falcon fsm algorithm the input and output spaces are partitioned into grids the grid typed space partitioning certainly makes both the fuzzy logic controller software emulation and fuzzy chip implementation convenient however as the number of input output variables increases the number of partitioned grids will grow combinatorially to avoid the problem of combinatorial growth of partitioned grids in some complex systems the falcon art algorithm is developed which can partition the input and output spaces in a more flexible way based on the distribution of the training data the falcon art combines the backpropagation learning scheme for parameter learning and a fuzzy art algorithm for structure learning the falcon art can on line partition the input and output spaces tune membership functions and find proper fuzzy logic rules dynamically computer simulations were conducted to illustrate the performance and applicability of both falcon fsm and falcon art learning algorithms

flc design for bounded separable functions with linear input output relations as a special case a systematic procedure is presented for designing a knowledge base which exactly implements a specified bounded separable function in fuzzy logic the design of a fuzzy logic control flc for local linear control is a special case of the result examples including controller design for a nonlinear process control application are presented

trajectory stabilization of a model car via fuzzy control abstract this paper deals with trajectory stabilization of a computer simulated model car via fuzzy control stability conditions of fuzzy systems are given in accordance with the definition of stability in the sense of lyapunov first we approximate a computer simulated model car whose dynamics is nonlinear by t s takagi and sugeno fuzzy model fuzzy control rules which guarantee stability of the control system under a condition are derived from the approximated fuzzy model the simulation results show that the fuzzy control rules effectively realize trajectory stabilization of the model car along a given reference trajectory from all initial positions under a condition and the dynamics of the approximated fuzzy model agrees well with that of the model car

solvable lie algebras with heisenberg ideals all finite dimensional indecomposable solvable lie algebras l n f having the heisenberg algebra h n as the nilradical are constructed the number of non nilpotent elements f that can be added to h n satisfies f or n 1 the casimir and generalized casimir operators of the algebras l n f are obtained

fuzzy algorithmic control of a model car by oral instructions this paper is concerned with an experimental study of fuzzy algorithmic control of a model car a fuzzy algorithmic controller is designed by referring to an operator s experience and knowledge the car is equipped with ultrasonic sensing devices and a micro computer it is controlled by oral instructions such as go straight turn right enter garage and go out of garage

design of self learning fuzzy sliding mode controllers based on genetic algorithms abstract in this paper genetic algorithms were applied to search a sub optimal fuzzy rule base for a fuzzy sliding mode controller two types of fuzzy sliding mode controllers based on genetic algorithms were proposed the fitness functions were defined so that the controllers which can drive and keep the state on the user defined sliding surface would be assigned a higher fitness value the sliding surface plays a very important role in the design of a fuzzy sliding mode controller it can dominate the dynamic behaviors of the control system as well as reduce the size of the fuzzy rule base in conventional fuzzy logic control an increase in either input variables or the associated linguistic labels would lead to the exponential growth of the number of rules the number of parameters or the equivalent length of strings used in the computations of genetic algorithms for a fuzzy logic controller are usually quite extensive as a result the considerable computation load prevents the use of genetic operations in the tuning of membership functions in a fuzzy rule base this paper shows that the number of rules in a fuzzy sliding mode controller is a linear function of the number of input variables the computation load of the inference engine in a fuzzy sliding mode controller is thus smaller than that in a fuzzy logic controller moreover the string length of parameters is shorter in a fuzzy sliding mode controller than in a fuzzy logic controller when the parameters are searched by genetic algorithms the simulation results showed the efficiency of the proposed approach and demonstrated the applicability of the genetic algorithm in the fuzzy sliding mode controller design

knowledge based fuzzy control of systems a method of fuzzy control of systems based on a concept of human thinking is investigated the fuzzy controller consists of a knowledge base containing a number of time responses of a process the elements of this base are cells in which an input and a corresponding output of a system are stored the cells are organized according to different types of relations the result is a knowledge structure which contains a model of the process to be controlled the following phases are distinguished the learning phase during which the relation between the imput and the output of a system is learned and the knowledge structure is constructed and a phase in which the knowledge structure is applied to control the process the method also provides an indication of the reliability of the control action

fuzzy control of model car this paper discusses fuzzy control of a model car fuzzy control rules are derived by modelling an expert s driving actions experiments are performed using a model car with a sensing unit and a micro computer

on levi extensions of nilpotent lie algebras abstract levi s theorem decomposes any arbitrary lie algebra over a field of characteristic zero as a direct sum of a semisimple lie algebra named levi factor and its solvable radical given a solvable lie algebra r a semisimple lie algebra s is said to be a levi extension of r in case a lie structure can be defined on the vector space s r the assertion is equivalent to s der r where der r is the derivation algebra of r for some representation of s onto r our goal in this paper is to present some general structure results on nilpotent lie algebras admitting levi extensions based on free nilpotent lie algebras and modules of semisimple lie algebras in low nilpotent index a complete classification will be given the results are based on linear algebra methods and leads to computational algorithms

fuzzy control of unknown multiple input multiple output plants abstract a self learning fuzzy logic system is given for control of unknown multiple input multiple output mimo plants some nomenclature and several data types are introduced to develop a general formulation for controller activity a random optimization algorithm is given to train the controller a plant model is not required for training instead training is guided by observations of plant responses to inputs

h sub spl infin controller synthesis of fuzzy dynamic systems based on piecewise lyapunov functions and bilinear matrix inequalities this work presents an h sub spl infin controller design method for fuzzy dynamic systems based on techniques of piecewise smooth lyapunov functions and bilinear matrix inequalities it is shown that a piecewise continuous lyapunov function can be used to establish the global stability with h sub spl infin performance of the resulting closed loop fuzzy control systems and the control laws can be obtained by solving a set of bilinear matrix inequalities bmis two examples are given to illustrate the application of the proposed methods

a neuro fuzzy system architecture for behavior based control of a mobile robot in unknown environments abstract a neuro fuzzy system architecture for behavior based control of a mobile robot in unknown environments is presented a neural network is used to understand environments its inputs are a heading angle between the robot and a specified target and range information acquired by an array of ultrasonic sensors the output from the neural network is a trained reference motion direction for robot navigation the methodology of the behavior based control approach proposed in this paper is 1 to analyze and to decompose a complex task based on stimulus response behavior and 2 to quantitatively formulate each type of behavior with a simple feature by fuzzy sets and fuzzy rules as well as to coordinate conflicts and competition among multiple types of behavior by fuzzy reasoning an advantage is that building fuzzy sets and rules for each simple featured type of behavior is much easier than for a complex task based upon a reference motion direction and distances between the robot and obstacles different types of behavior are fused by fuzzy logic to control the velocities of the two rear wheels of the robot simulation experiments show that the proposed neuro fuzzy system can improve navigation performance in complex and unknown environments in addition this architecture is suitable for multisensor fusion and integration

a class of solvable lie algebras and their casimir invariants a nilpotent lie algebra with an n 1 dimensional abelian ideal is studied all indecomposable solvable lie algebras with as their nilradical are obtained their dimension is at most n 2 the generalized casimir invariants of and of its solvable extensions are calculated for n 4 these algebras figure in the petrov classification of einstein spaces for larger values of n they can be used in a more general classification of riemannian manifolds

all solvable extensions of a class of nilpotent lie algebras of dimension n and degree of nilpotency n 1 we construct all solvable lie algebras with a specific n dimensional nilradical of degree of nilpotency n 1 and with an n 2 dimensional maximal abelian ideal we find that for given n such a solvable algebra is unique up to isomorphisms using the method of moving frames we construct a basis for the casimir invariants of the nilradical we also construct a basis for the generalized casimir invariants of its solvable extension consisting entirely of rational functions of the chosen invariants of the nilradical

fuzzy logic in commercial expert system results and prospects abstract this paper surveys the use of fuzzy methods in commercial applications of the technology of expert systems and in commercial products which aim to support such applications it attempts to evaluate the utility of such an approach to uncertainty management compared to other well known methods of handling uncertainty in expert systems starting from this base it attempts an evaluation of the prospects for fuzzy expert systems in the medium term as a survey it attempts to list applications and commercial products as comprehensively as is practical and includes an extensive bibliography on the topic

sugeno type controllers are universal controllers abstract we show that given any continuous real valued function on a compact set there is a sugeno type controller that will approximate this function to any degree of accuracy an application of this result to fuzzy control is presented which shows that this type of sugeno controller is a universal controller

adaptive conjugate gradient dfes for wideband mimo systems new adaptive equalization algorithms for wireless systems operating over wideband multiple input multiple output mimo channels are proposed the problem of mimo decision feedback equalizer dfe design is formulated as a set of linear equations with multiple right hand sides rhss evolving in time by applying an adaptive modified conjugate gradient algorithm we derive an equalizer with identical convergence improved tracking capabilities but higher computational load as compared to the recursive least squares rls algorithm to reduce its complexity two updating strategies of the equalizer filters based on galerkin projections are employed

cascaded fuzzy neural network model based on syllogistic fuzzy reasoning in recent years there has been an increasing interest in the fusion of neural networks and fuzzy logic most of the existing fuzzy neural network fnn models have been proposed to implement different types of single stage fuzzy reasoning mechanisms single stage fuzzy reasoning however is only the most basic among a human being s various types of reasoning mechanisms syllogistic fuzzy reasoning where the consequence of a rule in one reasoning stage is passed to the next stage as a fact is essential to effectively build up a large scale system with high level intelligence in view of the fact that the fusion of syllogistic fuzzy logic and neural networks has not been sufficiently studied a new fnn model based on syllogistic fuzzy reasoning termed cascaded fnn cfnn is proposed in this paper from the stipulated input output data pairs the model can generate an appropriate syllogistic fuzzy rule set through structure genetic learning and parameter back propagation learning procedures proposed in this paper in addition we particularly discuss and analyze the performance of the proposed model in terms of approximation ability and robustness as compared with single stage fnn models the effectiveness of the proposed cfnn model is demonstrated through simulating two benchmark problems in fuzzy control and nonlinear function approximation domain

genetic algorithm based optimal fuzzy controller design in the linguistic space in this paper a genetic algorithm ga based optimal fuzzy controller design is proposed the design procedure is accomplished by establishing an index function as the consequent part of the fuzzy control rule the inputs of the controller after scaling are utilized by the index function for computing the output linguistic value this linguistic value can then be used to map the suitable fuzzy control actions this proposed novel fuzzy control rule has crisp input and fuzzified output characteristics the index function plays a role in mapping the desired fuzzy sets for defuzzification resulting in a controlled hypersurface in the linguistic space formed by the input fuzzy variables two types of index functions both linear and nonlinear are introduced for controlling systems with different degrees of nonlinearity the parameters of the index function are obtained by applying a simple ga with a suitable fitness function various controlled systems result in various parameter sets depending on their dynamics under the acquired optimal parameter set the optimal index function can be used to generate the desired control actions several simulation examples are given to verify the performance of the proposed ga based fuzzy controller

fuzzy logic controller based on genetic algorithms abstract based on genetic algorithms gas a method of designing a fuzzy logical controller for complex processes is proposed after a brief overview of ga the selection of ga s control parameters is discussed for designing mimo fuzzy control systems we explained the optimization procedure of the fuzzy rules selection on a large fuzzy input search space a double inverted pendulum device is used to test the effectiveness of the design method based on gas not only simulation results are shown in the paper but also the practical operation results are given

normalized adaptive channel equalizer based on minimal symbol error rate existing minimum symbol error rate equalizers were derived based on the symbol error rate objective function due to the complexity of the objective function the derivation is not straightforward in this paper we present a new approach to derive the minimum symbol error rate adaptive equalizers the problem is formulated as minimizing the norm between two subsequent parameter vectors under the constraint of symbol error rate minimization the constrained optimization problem then is solved with the lagrange multiplier method which results in an adaptive algorithm with normalization simulation results show that the proposed algorithm outperforms the existing adaptive minimum symbol error rate equalizer in convergence speed and steady state performance

solvable lie algebras with naturally graded nilradicals and their invariants the indecomposable solvable lie algebras with graded nilradical of maximal nilindex and a heisenberg subalgebra of codimension one are analysed and their generalized casimir invariants are calculated it is shown that rank one solvable algebras have a contact form which implies the existence of an associated dynamical system moreover due to the structure of the quadratic casimir operator of the nilradical these algebras contain a maximal non abelian quasi classical lie algebra of dimension 2n 1 indicating that gauge theories with ghosts are possible on these subalgebras

non semisimple lie algebras with levi factor fraktur so 3 fraktur sfraktur l 2 bbb r and their invariants we analyse the number of functionally independent generalized casimir invariants for non semisimple lie algebras r with levi factors isomorphic to 3 and 2 in dependence of the pair r formed by a representation r of and a solvable lie algebra we show that for any dimension n 6 there exist lie algebras r with non trivial levi decomposition such that r 0

traffic signal control on similarity logic reasoning the main intention in this study is to tie fuzzy reasoning to many valued logic framework a lukasiewicz many valued logic similarity based fuzzy control algorithm is introduced and tested in three realistic traffic signal control systems the results are compared to fuzzy control systems where the inference is based on standard matlab fuzzy logic toobox s mamdani style system the compared traffic signal control modes are signalized pedestrian crossing and multi phase signal control with phase selection the statistical significance in differences of expectations obtained by different control schemes has been tested by two sided approximate students test on significance level 0 01 simulations made by hutsim traffic signal simulator show that the performances of the new control algorithm and that of standard mamdani style algorithm are almost equal however if traffic density is high then the new algorithm gives significantly better statistical results moreover a stronger mathematical approach offers a natural smoothness test

universal fuzzy controllers for a class of nonlinear systems in this paper we address the issues of universal fuzzy controllers we first present a universal function approximation theorem based on a fuzzy dynamic model then we show the results of universal fuzzy controllers for a large class of nonlinear systems

genetic based fuzzy hybrid multichannel filters for color image restoration abstract on the design of multichannel filters especially in color image restoration it is not easy to simultaneously achieve three objectives noise attenuation chromaticity retention and edges or details preservation in this paper we propose a new class of multichannel filters called genetic based fuzzy hybrid multichannel gfhm filters to reach these three objectives simultaneously the design of gfhm filters is mainly based on human concept heuristic rules and genetic algorithms because the human concept can be readily and efficiently expressed by fuzzy implicative rules gfhm filters can take the useful characteristics of filtering behavior of three filters a vector median a vector directional and an identity filter since genetic algorithms possess the global searching capability for an optimal solution they are able to effectively optimize gfhm filters to improve the filtering performance in color image restoration applications extensive simulation results illustrate that gfhm filters not only achieve these three objectives but also possess the robust and the adaptive capability moreover these simulation results also demonstrate that the performance of gfhm filters outperforms that of other proposed filtering techniques

solvable lie algebras with an mathbb n graded nilradical of maximal nilpotency degree and their invariants the class of solvable lie algebras with an graded nilradical of maximal nilpotency index is classified it is shown that such solvable extensions are unique up to isomorphism the generalized casimir invariants for the graded nilradicals and their associated solvable extensions are computed by the method of moving frames

classification of solvable real rigid lie algebras with a nilradical of dimension n 6 the complete classification of real solvable rigid lie algebras possessing a nilradical of dimension at most six is given eleven new isomorphism classes of indecomposable algebras are obtained it is further shown that the resulting solvable lie algebras have a vanishing second chevalley cohomology group thus correspond to algebraically rigid lie algebras

solvable lie algebras with abelian nilradicals a procedure is presented for classifying solvable lie algebras with abelian nilradicals theorems on the structure of such algebras are proven and their centres are constructed many examples are analysed

on the structure of maximal solvable extensions and of levi extensions of nilpotent lie algebras we establish an improved upper estimate on the dimension of any solvable lie algebra with its nilradical isomorphic to a given nilpotent lie algebra next we consider levi decomposable algebras with a given nilradical and investigate restrictions on possible levi factors originating from the structure of characteristic ideals of we present a new perspective on turkowski s classification of levi decomposable algebras up to dimension 9

mamdani type fuzzy controllers are universal fuzzy controllers abstract this paper addresses the issues of universal fuzzy controllers and shows that the mamdani type fuzzy controllers are universal fuzzy controllers furthermore a constructive procedure is also presented to obtain the universal fuzzy controller if the reference fuzzy model is given

gradual inference rules in approximate reasoning abstract gradual inference rules of the form the more x is f the more y is g or of similar forms with the less instead of the more which express a progressive change of the degree to which the entity y satisfies the gradual property g when the degree to which the entity x satisfies the gradual property f is modified are often encountered in commonsense reasoning a representation of such rules by means of fuzzy sets is proposed and discussed this representation turns out to be based on a special implication function already considered in multiple valued logic patterns of reasoning involving gradual inference rules are formalized their links with interpolation mechanisms are pointed out

theory of the fuzzy controller abstract we first introduce notation in an attempt to standardize the description of a basic fuzzy controller if r 1 r 2 r n are the real number inputs to the fuzzy controller and is its defuzzified output then f r 1 r n for some function f by the theory of the fuzzy controller we mean in this paper the study of the structure of f for elementary fuzzy controllers using this standardized notation we first survey the known results on the theory of the fuzzy controller and then discuss important future research topics in this area

turbo multi user detection for ofdm sdma systems relying on differential evolution aided iterative channel estimation a differential evolution de algorithm aided iterative channel estimation and turbo multi user detection mud scheme is proposed for multi user multi input multiple output aided orthogonal frequency division multiplexing space division multiple access ofdm sdma systems the proposed scheme iteratively exchanges the estimated channel information and the detected data between the channel estimator and mud employing a turbo technique which gradually improves the accuracy of the channel estimation and the mud especially for the first iteration quadrature amplitude modulation qam is employed in most wireless standards by virtue of providing a high throughput however the optimal maximum likelihood ml mud becomes extremely complex for employment in qam aided multi user systems hence two different de aided mud schemes the de aided minimum symbol error rate mser mud as well as the discrete de aided ml mud were developed and their achievable performance versus complexity was characterized the simulation results demonstrate that the proposed de aided channel estimator is capable of approaching the cramer rao lower bound with just two or three iterations the ultimate bit error rate lower bound of the single user additive white gaussian noise scenario has been approached in the range of eb n0 10 db and eb n0 6 db for the de aided mser mud and the discrete de aided ml mud respectively

an approach to h control of fuzzy dynamic systems this paper presents an approach to h control of a class of continuous time nonlinear systems which can be represented by a fuzzy dynamic model a suitable piecewise differentiable quadratic lyapunov function is used to establish asymptotic stability and h performance of the closed loop fuzzy control system a constructive algorithm is developed to obtain the h feedback control law which involves solving a set of suitable algebraic riccati equations an example is given to illustrate the application of the method

fuzzy sets theory applications in traffic and transportation abstract an entire array of different traffic and transportation parameters are clearly characterized by uncertainty subjectivity imprecision and ambiguity thus in the mathematical modeling phase of traffic and transportation processes whose individual parameters are uncertain ambiguous or subjectively estimated mathematical methods must be used that are able to satisfactorily deal with uncertainty ambiguity and subjectivity fuzzy set theory is a very convenient mathematical device for treating uncertainty subjectivity ambiguity and indetermination the purpose of this paper is to point out the possibility of using fuzzy set theory in solving complex traffic and transportation problems classification and analysis of up to date results in applying fuzzy set theory in modeling complex traffic and transportation processes are given in this paper

globally stable direct fuzzy model reference adaptive control abstract in the paper a fuzzy adaptive control algorithm is presented it belongs to the class of direct model reference adaptive techniques based on a fuzzy takagi sugeno model of the plant the plant to be controlled is assumed to be nonlinear and predominantly of the first order consequently the resulting adaptive and control laws are very simple and thus interesting for use in practical applications the system remains stable in the presence of unmodelled dynamics disturbances parasitic high order dynamics and reconstruction errors are treated explicitly the global stability of the overall system is proven in the paper i e it is shown that all signals remain bounded while the tracking error and estimated parameters converge to some residual set that depends on the size of disturbance and high order parasitic dynamics the proposed algorithm is tested on a simulated three tank system its performance is compared to the performance of a classical mrac

fuzzy adaptive controller design for the joint space control of an agricultural robot the proper execution of agricultural robotic tasks needs the use of adaptive control techniques this fact is mainly due to the nature of the systems to control which are difficult to model and time varying systems after a review of previous works concerning adaptive control a solution using a fuzzy adaptive controller is studied for the joint control of such robots an analytic representation of a particular fuzzy system is first developed to deduce useful conclusions for the controller design then a specialized learning architecture is used to allow the reconstruction of an error signal required for a gradient method for on line modification of the consequent part of the inference rules of a sugeno s fuzzy controller at the same time a second level constituted by static rules meta rules is introduced to cope with some limits of the learning architecture clustering of some rules is proposed to be able to learn those that are not fired most of the time but essential for unusual robot motions thanks to this new structure the controller is dedicated to each meta rule and the number of rules with respect to a solution without meta rule is considerably reduced simulation results during large on line variations in system parameters derived from a typical example of an agricultural robot show the effectiveness of the proposed approach the controller stability is verified by using the so called cell to cell mapping algorithm finally the feasibility of the implementation of this algorithm in low end hardware is shown

solvable lie algebras with triangular nilradicals all finite dimensional indecomposable solvable lie algebras having the triangular algebra t n as their nilradical are constructed the number of non nilpotent elements f in satisfies and the dimension of the lie algebra is

ordering distance and closeness of fuzzy sets abstract in dense rule bases where the observation usually overlaps with several antecedents in the rule base various algorithms are used for approximate reasoning and control if the antecedents are located sparsely and the observation does not overlap as a rule with any of the antecedents function approximation techniques combined with the resolution principle lead to applicable conclusions this kind of approximation is possible only if a new concept of ordering and distance i e a metric in the state space and a partial ordering among convex and normal fuzzy sets cnf sets is introduced so the fuzzy distance of two cnf sets can be defined and by this distance closeness and similarity of cnf sets as well

self learning fuzzy controllers based on temporal backpropagation a generalized control strategy that enhances fuzzy controllers with self learning capability for achieving prescribed control objectives in a near optimal manner is presented this methodology termed temporal backpropagation is model sensitive in the sense that it can deal with plants that can be represented in a piecewise differentiable format such as difference equations neural networks gmdh structures and fuzzy models regardless of the numbers of inputs and outputs of the plants under consideration the proposed approach can either refine the fuzzy if then rules of human experts or automatically derive the fuzzy if then rules if human experts are not available the inverted pendulum system is employed as a testbed to demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired fuzzy controller

a randomized approach with geometric constraints to fingerprint verification abstract in this paper a fuzzy bipartite weighted graph model is proposed to solve fingerprint verification problem a fingerprint image is preprocessed first to form clusters of feature points which are called feature point clusters twenty four attributes are extracted for each feature point cluster the attributes are characterized by fuzzy values attributes of an input image to be verified are considered as the set of left nodes in a fuzzy bipartite weighted graph and the attributes of claimed template fingerprint image are considered as the set of right nodes in the graph the fingerprint verification problem is thus converted into a fuzzy bipartite weighted graph matching problem a matching algorithm is proposed for the fuzzy bipartite weighted graph model to find an optimal matching with a goodness score experimental results reveal the feasibility of the proposed approach in fingerprint verification

reduction of fuzzy control rules by means of premise learning method and case study rule number reduction is important for fuzzy control of complex processes with high dimensionality it is stated in the paper that this issue can be treated effectively by means of learning premises with general structure since conditions of rules are generalised by a genetic algorithm ga rather than enumerated according to every and connection of input fuzzy sets a parsimonious knowledge base with a reduced number of rules can be expected on the other hand to give a numerical evaluation of possible conflicts among rules a consistency index of the rule set is established this index is integrated into the fitness function of the ga to search for a set of optimal rule premises yielding not only good control performance but also little or no inconsistency in the fuzzy knowledge base the advantage of the proposed method is demonstrated by the case study of development of a compact fuzzy controller to balance an inverted pendulum in the laboratory

mber space time decision feedback equalization assisted multiuser detection for multiple antenna aided sdma systems this paper proposes a space time decision feedback equalization st dfe assisted multiuser detection mud scheme for multiple receiver antenna aided space division multiple access systems a minimum bit error rate mber design is invoked for the mud which is shown to be capable of improving the achievable bit error rate performance and enhancing the attainable system capacity over that of the standard minimum mean square error mmse design an adaptive implementation of the mber st dfe assisted mud is proposed using a stochastic gradient based least bit error rate algorithm which is demonstrated to consistently outperform the classical least mean square lms algorithm while achieving a lower computational complexity than the lms algorithm for the binary signalling scheme our simulation results demonstrate that the mber st dfe assisted mud is more robust to channel estimation errors as well as to potential error propagation imposed by decision feedback errors compared to the mmse st dfe assisted mud

adaptive minimum symbol error rate decision feedback equalization for multilevel pulse amplitude modulation the design of decision feedback equalizers dfes is typically based on the minimum mean square error mmse principle as this leads to effective adaptive implementation in the form of the least mean square algorithm it is well known however that in certain situations the mmse solution can be distinctly inferior to the optimal minimum symbol error rate mser solution we consider the mser design for multilevel pulse amplitude modulation block data adaptive implementation of the theoretical mser dfe solution is developed based on the parzen window estimate of a probability density function furthermore a sample by sample adaptive mser algorithm called the least symbol error rate lser is derived for adaptive equalization applications the proposed lser algorithm has a complexity that increases linearly with the equalizer length computer simulation is employed to evaluate the proposed alternative mser design for equalization application with multilevel signaling schemes

an approach to adaptive control of fuzzy dynamic systems this paper discusses adaptive control for a class of fuzzy dynamic models the adaptive control law is first designed in each local region and then constructed in the global domain it is shown that the resulting fuzzy adaptive control system is globally stable robustness issues of the adaptive control system are also addressed a simulation example is given for demonstration of the application of the approach

controller synthesis of fuzzy dynamic systems based on piecewise lyapunov functions this paper presents a kind of controller synthesis method for fuzzy dynamic systems based on a piecewise smooth lyapunov function the basic idea of the proposed approach is to construct controllers for the fuzzy dynamic systems in such a way that a piecewise continuous lyapunov function can be used to establish the global stability with h sub spl infin performance of the resulting closed loop fuzzy control systems it is shown that the control laws can be obtained by solving a set of linear matrix inequalities that is numerically feasible with commercially available software an example is given to illustrate the application of the proposed methods

cdna microarray image processing using fuzzy vector filtering framework this paper presents a novel filtering framework capable of processing cdna microarray images the proposed two component adaptive vector filters integrate well known concepts from the areas of fuzzy set theory nonlinear filtering multidimensional scaling and robust order statistics by appropriately setting the weighting coefficients in a generalized framework the method is capable of removing noise impairments while preserving structural information in cdna microarray images noise removal is performed by tuning a membership function which utilizes distance criteria applied to cdna vectorial inputs at each image location the classical vector representation adopted here for a two channel processing task as well as a new color ratio model representation are used simulation studies reported in this paper indicate that the proposed adaptive fuzzy vector filters are computationally attractive yield excellent performance and are able to preserve structural information while efficiently suppressing noise in cdna microarray data