optimal channel efficiency in a sensory network spontaneous neural activity has been increasingly recognized as a subject of key relevance in neuroscience it exhibits nontrivial spatiotemporal structure reflecting the organization of the underlying neural network and has proved to be closely intertwined with stimulus induced activity patterns as an additional contribution in this regard we report computational studies that strongly suggest that a stimulus free feature rules the behavior of an important psychophysical measure of the sensibility of a sensory system to a stimulus the so called dynamic range indeed in this paper we show that the entropy of the distribution of avalanche lifetimes information efficiency since it can be interpreted as the efficiency of the network seen as a communication channel always accompanies the dynamic range in the benchmark model for sensory systems specifically by simulating the kinouchi copelli kc model on two broad families of model networks we generically observed that both quantities always increase or decrease together as functions of the average branching ratio the control parameter of the kc model and that the information efficiency typically exhibits critical optimization jointly with the dynamic range i e both quantities are optimized at the same value of that control parameter that turns out to be the critical point of a nonequilibrium phase transition in contrast with the practice of taking power laws to identify critical points in most studies describing measured neuronal avalanches we rely on data collapses as more robust signatures of criticality to claim that critical optimization may happen even when the distribution of avalanche lifetimes is not a power law as suggested by a recent experiment finally we note that the entropy of the size distribution of avalanches information capacity does not always follow the dynamic range and the information efficiency when they are critically optimized despite being more widely used than the latter to describe the computational capabilities of a neural network this strongly suggests that dynamical rules allowing a proper temporal matching of the states of the interacting neurons is the key for achieving good performance in information processing rather than increasing the number of available units

excitable greenberg hastings cellular automaton model on scale free networks we study the excitable greenberg hastings cellular automaton model on scale free networks we obtain analytical expressions for no external stimulus the uncoupled case it is found that the curves the average activity f versus the external stimulus rate r can be fitted by a hill function but not exactly there exists a relation f ensuremath sim r ensuremath alpha for the low stimulus response where the stevens hill exponent ensuremath alpha ranges from ensuremath alpha 1 in the subcritical regime to ensuremath alpha 0 5 at criticality at the critical point the range is maximal but not divergent we also calculate the average activity f k r and the dynamic range ensuremath delta k p for nodes with given connectivity k it is interesting that nodes with larger connectivity have larger optimal range which could be applied in biological experiments to reveal the network topology

periodic boundary value problems for a class of functional differential equations in this paper we show that the method of upper and lower solutions coupled with the monotone iterative technique is valid to obtain constructive proofs of existence of solutions for nonlinear periodic boundary value problems of functional differential equations without assuming properties of monotonicity in the nonlinear part

psychophysics of time perception and intertemporal choice models intertemporal choice and psychophysics of time perception have been attracting attention in econophysics and neuroeconomics several models have been proposed for intertemporal choice exponential discounting general hyperbolic discounting exponential discounting with logarithmic time perception of the weber fechner law a q exponential discount model based on tsallis s statistics simple hyperbolic discounting and stevens power law exponential discounting exponential discounting with stevens power time perception in order to examine the fitness of the models for behavioral data we estimated the parameters and aicc akaike information criterion with small sample correction of the intertemporal choice models by assessing the points of subjective equality indifference points at seven delays our results have shown that the orders of the goodness of fit for both group and individual data were weber fechner discounting general hyperbola stevens power law discounting simple hyperbolic discounting exponential discounting indicating that human time perception in intertemporal choice may follow the weber fechner law indications of the results for neuropsychopharmacological treatments of addiction and biophysical processing underlying temporal discounting and time perception are discussed

growth model for fractal scale free networks generated by a random walk abstract the diversity of networked systems with fractal structures suggests that knowing the underlying mechanism that generates the fractality is necessary for building a model of the development of complex networks in the present paper we propose a growth model of a network generated by a random walk and show that the evolving graph forms a fractal structure with various properties including the scale free property if the graph which provides a space where a random walk occurs by itself is formed by the random walk the proposed model is regulated by two parameters p v and p e which define the probability of either a roundabout path via a new vertex or a shortcut being formed by the random walk respectively the power law exponent describing the vertex degree distribution is determined by the ratio p e p v and is related to an internal factor f i via the relation 1 f i 1 where f i is a parameter that describes the local structure generated by the random walk a sufficiently small p v provides the small world property to the model network the small world property is usually considered to be incompatible with the fractal scaling property open m c l c d c where open m c is the average number of vertices which can be reached from a randomly chosen vertex in at most l c steps however we demonstrate that fractality can be reconciled with the small world property by introducing a size dependent fractal cluster dimension d c

deterministic excitable media under poisson drive power law responses spiral waves and dynamic range when each site of a spatially extended excitable medium is independently driven by a poisson stimulus with rate h the interplay between creation and annihilation of excitable waves leads to an average activity f it has recently been suggested that in the low stimulus regime h approximately 0 the response function f h of hypercubic deterministic systems behaves as a power law f approximately h m moreover the response exponent m has been predicted to depend only on the dimensionality d of the lattice m 1 1 d t ohta and t yoshimura physica d 205 189 2005 in order to test this prediction we study the response function of excitable lattices modeled by either coupled morris lecar equations or greenberg hastings cellular automata we show that the prediction is verified in our model systems for d 1 2 and 3 provided that a minimum set of conditions is satisfied under these conditions the dynamic range which measures the range of stimulus intensities that can be coded by the network activity increases with the dimensionality d of the network the power law scenario breaks down however if the system can exhibit self sustained activity spiral waves in this case we recover a scenario that is common to probabilistic excitable media as a function of the conductance coupling g among the excitable elements the dynamic range is maximized precisely at the critical value g c above which self sustained activity becomes stable we discuss the implications of these results in the context of neural coding

scaling law for the transient behavior of type ii neuron models we study the transient regime of type ii biophysical neuron models and determine the scaling behavior of relaxation times ensuremath tau near but below the repetitive firing critical current ensuremath tau ensuremath simeq c i c ensuremath i ensuremath ensuremath delta for both the hodgkin huxley and morris lecar models we find that the critical exponent is independent of the numerical integration time step and that both systems belong to the same universality class with ensuremath delta 1 2 for appropriately chosen parameters the fitzhugh nagumo model presents the same generic transient behavior but the critical region is significantly smaller we propose an experiment that may reveal nontrivial critical exponents in the squid axon

analytic formulation of linguistic rules for fuzzy controller in this paper an analytic formulation of a fuzzy controller using rules with two conditions is given this formulation will be exploited in order to prove the nonlinearity of the controller and to establish equivalence between fuzzy controllers and pi ones

local world evolving networks with tunable clustering abstract we propose an extended local world evolving network model including a triad formation tf step in the process of network evolution random fluctuation in the number of new edges is involved we derive analytical expressions for degree distribution clustering coefficient and average path length our model can unify the generic properties of real life networks scale free degree distribution high clustering and small inter node separation moreover in our model the clustering coefficient is tunable simply by changing the expected number of tf steps after a single local preferential attachment step

pinning synchronization of delayed complex dynamical networks with nonlinear coupling abstract in this paper we find that complex networks with the watts strogatz or scale free ba random topological architecture can be synchronized more easily by pin controlling fewer nodes than regular systems theoretical analysis is included by means of lyapunov functions and linear matrix inequalities lmi to make all nodes reach complete synchronization numerical examples are also provided to illustrate the importance of our theoretical analysis which implies that there exists a gap between the theoretical prediction and numerical results about the minimum number of pinning controlled nodes

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

dynamics of directed graphs the world wide web we introduce and simulate a growth model of the world wide web based on the dynamics of outgoing links that is motivated by the conduct of the agents in the real web to update outgoing links re directing them towards constantly changing selected nodes emergent statistical correlation between the distributions of outgoing and incoming links is a key feature of the dynamics of the web the growth phase is characterized by temporal fractal structures which are manifested in the hierarchical organization of links we obtain quantitative agreement with the recent empirical data in the real web for the distributions of in and out links and for the size of the connected component in a fully grown network of n nodes we study the structure of connected clusters of nodes that are accessible along outgoing links from a randomly selected node the distributions of size and depth of the connected clusters with a giant component exhibit supercritical behavior by decreasing the control parameter average fraction of updated and added links per time step towards c n 10 the web can resume a critical structure with no giant component in it we find a different universality class when the updates of links are not allowed i e for 0 corresponding to the network of science citations

hierarchical fuzzy modelling for monitoring depth of anaesthesia abstract a hierarchical structure based on fuzzy modelling which can monitor depth of anaesthesia doa from many clinical signs in the operating theatre is described the first level uses numerical clinical signs such as systolic arterial pressure sap and heart rate hr via a rule base from anaesthetists experience or a self organizing learning algorithm to interpret primary depth of anaesthesia pdoa the second level is focused on non numerical clinical signs such as sweating sw lacrimation la and pupil response pr which can be merged with the first level of pdoa to decide doa with more confidence furthermore linguistic rules and a simple fuzzy modelling concept have been used to model a patient during induction and maintenance stages not only can this model simulate new ideas and monitoring methods but it can also be used clinically on line to give an estimate of the adequacy of doa successful results have given confidence to perform on line clinical trials in the operating theatre two configurations of fuzzy modelling to determine the rule base are described in this paper firstly a self organizing fuzzy modelling approach has the ability to generate rules on line from input and output data secondly if there is no direct measurement for the system output or it is very difficult to interpret a self organizing learning system which can learn rules from off line input and output data can be utilized for modelling such systems

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

nonlinear boundary value problem for first order impulsive integro differential equations of mixed type this paper discusses nonlinear boundary value problem for first order impulsive integro differential equations of mixed type the lower and upper solutions and monotone iterative techniques are used to achieve the existence results two examples are provided to illustrate the efficiency of the obtained results

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

exact density profile of a stochastic reaction diffusion process we calculate exactly the time dependent density profile of a one dimensional stochastic reaction diffusion process of hard core particles subjected to the reactions aa oo and ao oa the solution is based on the fundamental property that the evolution operator defined over an appropriate vector space transforms vectors with n kinks into vectors with n or n 2 kinks only in this space a basis vector is represented by a string of plus and minus signs and a kink is defined as a pair of opposite signs the exact time dependent profiles are calculated for the cases of uncorrelated initial states that are translational invariant as well as initial states that are inhomogeneous in space

uniform synchronous criticality of diversely random complex networks abstract we investigate collective synchronous behaviors in random complex networks of limit cycle oscillators with the non identical asymmetric coupling scheme and find a uniform coupling criticality of collective synchronization which is independent of complexity of network topologies numerical simulations on categories of random complex networks have verified this conclusion

projective cluster synchronization in drive response dynamical networks abstract in this paper we study the projective cluster synchronization in a drive response dynamical network with 1 n coupled partially linear chaotic systems because the scaling factors characterizing the dynamics of projective synchronization remain unpredictable pinning control ideas are adopted to direct the different scaling factors onto the desired values it is also shown that the projection cluster synchronization can be realized by controlling only one node in each cluster numerical simulations on the chaotic lorenz system are illustrated to verify the theoretical results

response of electrically coupled spiking neurons a cellular automaton approach experimental data suggest that some classes of spiking neurons in the first layers of sensory systems are electrically coupled via gap junctions or ephaptic interactions when the electrical coupling is removed the response function firing rate vs stimulus intensity of the uncoupled neurons typically shows a decrease in dynamic range and sensitivity in order to assess the effect of electrical coupling in the sensory periphery we calculate the response to a poisson stimulus of a chain of excitable neurons modeled by n state greenberg hastings cellular automata in two approximation levels the single site mean field approximation is shown to give poor results failing to predict the absorbing state of the lattice while the results for the pair approximation are in good agreement with computer simulations in the whole stimulus range in particular the dynamic range is substantially enlarged due to the propagation of excitable waves which suggests a functional role for lateral electrical coupling for probabilistic spike propagation the hill exponent of the response function is alpha 1 while for deterministic spike propagation we obtain alpha 1 2 which is close to the experimental values of the psychophysical stevens exponents for odor and light intensities our calculations are in qualitative agreement with experimental response functions of ganglion cells in the mammalian retina

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

boundary value problem of second order impulsive functional differential equations this paper discusses a kind of linear boundary value problem for a nonlinear second order impulsive functional differential equations we establish several existence results by using the lower and upper solutions and monotone iterative techniques an example is discussed to illustrate the efficiency of the obtained result

dynamical mean field theory of spiking neuron ensembles response to a single spike with independent noises a semianalytical dynamical mean field theory has been developed for a study of dynamics of an ensemble of n unit fitzhugh nagumo neurons subject to white noises assuming weak noises and the gaussian distribution of state variables we have driven equations of motions for moments of local and global variables dynamical mean field approximation dma has replaced original 2n dimensional stochastic differential equations des by eight dimensional deterministic des whereas the conventional moment method yields n 2n 3 dimensional deterministic des for local variables we have discussed the dependence of the spike firing precision and the synchronization on the noise intensity synaptic coupling and the size of the neuron ensemble the spike timing precision is shown to be improved by increasing the size of the neuron ensemble even when there are no couplings among neurons when the coupling is introduced neurons in ensembles respond to an input spike with a partial synchronization results calculated by our dma theory are in good agreement with those obtained by direct simulations dma theory is extended to a large cluster which can be divided into multiple subclusters according to their functions a model calculation has demonstrated that when the noise intensity is moderate the spike propagation with a fairly precise timing is possible among noisy subclusters with feed forward couplings as in the synfire chain we have compared dma theory with the conventional moment method showing that the former may be alternatively derived from the latter by a reduction in the number of moments with the mean field approximation

phase diagram of a probabilistic cellular automaton with three site interactions we study a 1 1 dimensional probabilistic cellular automaton that is closely related to the domany kinzel stochastic cellular automaton dkca but in which the update of a given site depends on the state of three sites at the previous time step thus compared with the dkca there is an additional parameter p 3 representing the probability for a site to be active at time t given that it and its nearest neighbors were active at time t 1 we study phase transitions and critical behavior for the activity and for damage spreading using one and two site mean field approximations and simulations for p 3 0 and p 3 1 we find evidence for a line of tricritical points in the p 1 p 2 p 3 parameter space obtained using a mean field approximation at pair level to construct the phase diagram in simulations we employ the growth exponent method in an interface representation for p 3 0 the phase diagram is similar to the dkca but the damage spreading transition exhibits a reentrant phase for p 3 1 the growth exponent method reproduces the two absorbing states first and second order phase transitions bicritical point and damage spreading transition recently identified by bagnoli et al phys rev e 63 046116 2001

effect of node deleting on network structure abstract the ever increasing knowledge of the structure of various real world networks has uncovered their complex multi mechanism governed evolution processes therefore a better understanding of the structure and evolution of these networked complex systems requires us to describe such processes in a more detailed and realistic manner in this paper we introduce a new type of network growth rule which comprises addition and deletion of nodes and propose an evolving network model to investigate the effect of node deleting on network structure it is found that with the introduction of node deleting network structure is significantly transformed in particular degree distribution of the network undergoes a transition from scale free to exponential forms as the intensity of node deleting increases at the same time nontrivial disassortative degree correlation develops spontaneously as a natural result of network evolution in the model we also demonstrate that node deleting introduced in the model does not destroy the connectedness of a growing network so long as the increasing rate of edges is not excessively small in addition it is found that node deleting will weaken but not eliminate the small world effect of a growing network and generally it will decrease the clustering coefficient in a network

an iterative method for functional differential equations a suitable periodic boundary conditions for a functional differential equations email protected t f t x x t are conditions of the form x 0 q x 2 p q in this paper we use the notion of upper and lower solutions coupled with monotone iterative method to prove the existence of solutions of this periodic boundary value problem

dynamic range of hypercubic stochastic excitable media we study the response properties of d dimensional hypercubic excitable networks to a stochastic stimulus each site modeled either by a three state stochastic susceptible infected recovered susceptible system or by the probabilistic greenberg hastings cellular automaton is continuously and independently stimulated by an external poisson rate h the response function mean density of active sites ensuremath rho versus h is obtained via simulations for d 1 2 3 4 and mean field approximations at the single site and pair levels ensuremath forall text d in any dimension the dynamic range and sensitivity of the response function are maximized precisely at the nonequilibrium phase transition to self sustained activity in agreement with a reasoning recently proposed moreover the maximum dynamic range attained at a given dimension d is a decreasing function of d

oscillatory epidemic prevalence in growing scale free networks we study the persistent epidemic prevalence with oscillatory behavior and the extinction of computer viruses via e mails on a contact relational network growing with new users for which scale free structure is estimated from real data typical oscillatory phenomenon is simulated in a stochastic model for the execution and detection of viruses the conditions of extinction by random and targeted immunizations for hubs are derived through bifurcation analysis for simpler deterministic models by using a mean field approximation without the connectivity correlations we can qualitatively understand the mechanisms of the spread in linearly growing scale free networks

how much information is needed to be the majority during the binary state opinion formation abstract in this paper we try to propose a toy model which follows the majority rule with the fermi function to uncover the role of the heterogeneous interaction between individuals in opinion formation in order to do this we define the impact factor i f i says individual i as the exponential function of its connectivity k i with the tunable parameter also shows the public information that can be collected by individuals in the system we realize our model in scale free networks with mean connectivity k we find that much more public information 2 and less public information 1 cannot let either of the two opinions be the majority during the opinion formation furthermore 1 is a constant and equal to 0 76 0 04 and 2 decreases as a power law function of the mean connectivity k of the network our work can provide some perspectives and tools to understand the diversity of opinion in social networks

advanced fuzzy logic control of a model car in extreme situations abstract state of the art in fuzzy logic control applications is the simple calculus of fuzzy if then rules predominantly using min max operators for the implementation mainly precompiler based tools are used though this concept is sufficient for simple control applications or consumer goods it is often insufficient for complex control applications this work investigates which advanced methods render the application of fuzzy control in complex problems possible and how fuzzy controllers may be built more efficiently the results of the theoretical work is demonstrated by the real model of a high performance race car

nonlinear boundary value problem of first order impulsive functional differential equations this paper discusses nonlinear boundary value problem for first order impulsive functional differential equations we establish several existence results by using the lower and upper solutions and monotone iterative techniques two examples are discussed to illustrate the efficiency of the obtained results

epidemic dynamics behavior in some bus transport networks abstract we abstract bus transport networks btns to complex networks using the space p approach first we select three actual btns in three major cities in china namely beijing shanghai and hangzhou using the sis model we simulate and study the epidemic spreading in the three btns we obtain the density of infected vertices varying with time and the stationary density of infected vertices varying with infection rate second we simulate and study the epidemic spreading in a recently introduced btn evolution model the network properties of which correspond well with those of actual btns third we use mean field theory to analyze the epidemic dynamics behavior of the btn evolution model and obtain the theoretical epidemic threshold of this model the theoretical value agrees well with the simulation results based on the work in this paper we provide the following possible forecasts for epidemic dynamics in actual btns an actual btn should have a finite positive epidemic threshold if the effective infection rate is above this threshold the epidemic spread in the network and the density of infected vertices finally stabilizes in a balanced state below this threshold the number of infected vertices decays exponentially fast and the epidemic cannot spread on a large scale

application of fuzzy logic control in industry abstract in this paper an overview is given of the various ways fuzzy logic can be used to improve industrial control the application of fuzzy logic in control is illustrated by two case studies the first example shows how fuzzy logic incorporated in the hardware of an industrial controller helps to finetune a pid controller without the operator having any a priori knowledge of the system to be controlled the second example is from process industry here fuzzy logic supervisory control is implemented in software and enhances the operation of a sintering oven through a subtle combination of priority management and deviation controlled timing finally the key areas of research in fuzzy logic control are discussed first the paper discusses how fuzzy logic control can be combined with other methods by properly separating the a priori model knowledge of the process under control a hierarchy of nonlinear control methods is established after a short discussion of how to optimize an intuitive fuzzy rule base we show that it is possible to derive conditions for asymptotic stability and robustness of fuzzy logic controllers using classical nonlinear analysis

pair approximation of the stochastic susceptible infected recovered susceptible epidemic model on the hypercubic lattice we investigate the time evolution and steady states of the stochastic susceptible infected recovered susceptible sirs epidemic model on one and two dimensional lattices we compare the behavior of this system obtained from computer simulations with those obtained from the mean field approximation mfa and pair approximation pa the former latter approximates higher order moments in terms of first second order ones we find that the pa gives consistently better results than the mfa in one dimension the improvement is even qualitative

structure of n clique networks embedded in a complex network we propose the n clique network as a powerful tool for understanding global structures of combined highly interconnected subgraphs and provide theoretical predictions for statistical properties of the n clique networks embedded in a complex network using the degree distribution and the clustering spectrum furthermore using our theoretical predictions we find that the statistical properties are invariant between 3 clique networks and original networks for several observable real world networks with the scale free connectivity and the hierarchical modularity the result implies that structural properties are identical between the 3 clique networks and the original networks

epidemic dynamics and endemic states in complex networks we study by analytical methods and large scale simulations a dynamical model for the spreading of epidemics in complex networks in networks with exponentially bounded connectivity we recover the usual epidemic behavior with a threshold defining a critical point below that the infection prevalence is null on the contrary on a wide range of scale free networks we observe the absence of an epidemic threshold and its associated critical behavior this implies that scale free networks are prone to the spreading and the persistence of infections whatever spreading rate the epidemic agents might possess these results can help understanding computer virus epidemics and other spreading phenomena on communication and social networks

intensity coding in two dimensional excitable neural networks abstract in the light of recent experimental findings that gap junctions are essential for low level intensity detection in the sensory periphery the greenberg hastings cellular automaton is employed to model the response of a two dimensional sensory network to external stimuli we show that excitable elements sensory neurons that have a small dynamical range are shown to give rise to a collective large dynamical range therefore the network transfer gain function which is hill or stevens law like is an emergent property generated from a pool of small dynamical range cells providing a basis for a neural psychophysics the growth of the dynamical range with the system size is approximately logarithmic suggesting a functional role for electrical coupling for a fixed number of neurons the dynamical range displays a maximum as a function of the refractory period which suggests experimental tests for the model a biological application to ephaptic interactions in olfactory nerve fascicles is proposed

random boolean network model exhibiting deterministic chaos this paper considers a simple boolean network with n nodes each node s state at time t being determined by a certain number of parent nodes which may vary from one node to another this is an extension of a model studied by andrecut and ali int j mod phys b 15 17 2001 who consider the same number of parents for all nodes we make use of the same boolean rule as andrecut and ali provide a generalization of the formula for the probability of finding a node in state 1 at a time t and use simulation methods to generate consecutive states of the network for both the real system and the model the results match well we study the dynamics of the model through sensitivity of the orbits to initial values bifurcation diagrams and fixed point analysis we show that the route to chaos is due to a cascade of period doubling bifurcations which turn into reversed period halving bifurcations for certain combinations of parameter values

statistical pulse dynamics in a reaction diffusion system abstract we study a nonlinear response to random external stimuli in an excitable reaction diffusion system numerical simulations are carried out in one dimension to investigate formation of excited domains pulses in response to stimuli and pair annihilate upon collision our main concern is how the area of excited domains in the steady state depends on the strength of stimuli we have found three different stimuli response behaviors a power law dependence for sufficiently weak stimuli a logarithmic dependence for intermediate strength and an oscillatory behavior for strong stimuli the power law behavior can be understood by a theoretical analysis

representation and use of imprecise temporal knowledge in dynamic systems abstract in this paper the importance of representation and use of imprecise temporal knowledge for problem solving in industrial dynamic systems is emphasized first a representation of imprecise temporal knowledge is then given for describing the dynamic behavior of systems a novel problem on on line temporal matching and numeric matching is presented and a method for solving this problem is proposed a method for fuzzy proposition reasoning and temporal reasoning is put forward finally as two application examples the fault diagnosis of a fluidized catalytic cracking unit is taken up to illustrate the usefulness of the proposed knowledge representation and methods

growing networks with mixed attachment mechanisms networks grow and evolve when new nodes and links are added in there are two methods to add the links uniform attachment and preferential attachment we take account of the addition of links with mixed attachment between uniform attachment and preferential attachment in proportion by using numerical simulations and analysis based on a continuum theory we obtain that the degree distribution p k has an extended power law form p k k k0 when the number of edges k of a node is much larger than a certain value k0 the degree distribution reduces to the power law form p k k and when k is much smaller than k0 the degree distribution degenerates into the exponential form it has been found that degree distribution possesses this extended power law form for many real networks such as the movie actor network the citation network of scientific papers and diverse protein interaction networks

lobby index in networks abstract we propose a new node centrality measure in networks the lobby index which is inspired by hirsch s h index it is shown that in scale free networks with exponent the distribution of the l index has power tail with exponent 1 properties of the l index and extensions are discussed

weighted scale free networks with stochastic weight assignments we propose a model of weighted scale free networks incorporating a stochastic scheme for weight assignments to the links taking into account both the popularity and fitness of a node as the network grows the weights of links are driven either by the connectivity with probability p or by the fitness with probability 1 ensuremath p numerical results show that the total weight exhibits a power law distribution with an exponent ensuremath sigma that depends on the probability p the exponent ensuremath sigma decreases continuously as p increases for p 0 the scaling behavior is the same as that of the connectivity distribution an analytical expression for the total weight is derived so as to explain the features observed in the numerical results numerical results are also presented for a generalized model with a fitness dependent link formation mechanism

explicit formulas for fuzzy controller abstract in this paper we present some explicit formulas of fuzzy control in the simple case of one condition these formulas are then used on the one hand to study the effects of some design factors inference operators number of partitions etc and on the other hand to carry out simulations

the influence of connectivity on the firing rate in a neuronal network with electrical and chemical synapses a b s t r a c t we study the firing rate properties of a cellular automaton model for a neuronal network with chemical synapses we propose a simple mechanism in which the nonlocal connections are included through electrical and chemical synapses in the latter case we introduce a time delay which produces self sustained activity nonlocal connections or shortcuts are randomly introduced according to a specified connection probability there is a range of connection probabilities for which neuron firing occurs as well as a critical probability for which the firing ceases in the absence of time delay the critical probability for nonlocal shortcuts depends on the network size according to a power law we also compute the firing rate amplification factor by varying both the connection probability and the time delay for different network sizes

stochastic resonance in coupled threshold elements on a barabasi albert network abstract stochastic resonance is investigated in a system of threshold elements located at nodes and coupled along edges of a barabasi albert network driven by a common subthreshold periodic signal and independent noises array enhanced stochastic resonance is observed i e increase of the spectral power amplification evaluated from the mean output of the network due to proper coupling this enhancement occurs though the response of individual threshold elements to the periodic signal is very diverse due to a power law distribution of their connectivity numerical results are qualitatively explained using simple linear response theory in the mean field approximation

stability and stabilizability of fuzzy neural linear control systems this paper discusses stability analysis of fuzzy neural linear fnl control systems which consist of combinations of fuzzy models neural network nn models and linear models the authors consider a relation among the dynamics of nn models those of fuzzy models and those of linear models it is pointed out that the dynamics of linear models and nn models can be perfectly represented by takagi sugeno t s fuzzy models whose consequent parts are described by linear equations in particular the authors present a procedure for representing the dynamics of nn models via t s fuzzy models next the authors recall stability conditions for ensuring stability of fuzzy control systems in the sense of lyapunov the stability criteria is reduced to the problem of finding a common lyapunov function for a set of lyapunov inequalities the stability conditions are employed to analyze stability of fnl control systems finally stability analysis for four types of fnl control systems is demonstrated

exact solutions for stochastic adsorption desorption models and catalytic surface processes we investigate the stochastic kinetics of adsorption desorption cooperative processes with single particle biased diffusion for certain choices of the transition probability rates a quantum spin analogy allows for an exact solution of dynamic correlation functions in the steady state for nonequilibrium translationally invariant initial conditions equal time correlation functions and nonsteady currents are calculated exactly working from the master equation depending on the relative values of the transition rates these quantities exhibit either power law decay or exponential decay with a variety of subdominant power law prefactors our results are compared with monte carlo simulations

mean field theory for scale free random networks random networks with complex topology are common in nature describing systems as diverse as the world wide web or social and business networks recently it has been demonstrated that most large networks for which topological information is available display scale free features here we study the scaling properties of the recently introduced scale free model that can account for the observed power law distribution of the connectivities we develop a mean eld method to predict the growth dynamics of the individual vertices and use this to calculate analytically the connectivity distribution and the scaling exponents the mean eld method can be used to address the properties of two variants of the scale free model that do not display power law scaling c 1999 elsevier science b v all rights reserved