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Representation of Finite State Automata in Recurrent Radial Basis Function Networks
, 1996
"... to :hs paper we propose some techniques ft>r injccling linite Stale automata rate l.ec:rr,zn Radial Basis Functlt>n networks (R2BF). When providing proper hints and constraining the v,oght space prlpe'ly. we show that thc,e nelworks behave as automata. A teebraque is snggcsted /"t eb ..."
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Cited by 37 (5 self)
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to :hs paper we propose some techniques ft>r injccling linite Stale automata rate l.ec:rr,zn Radial Basis Functlt>n networks (R2BF). When providing proper hints and constraining the v,oght space prlpe'ly. we show that thc,e nelworks behave as automata. A teebraque is snggcsted /"t ebrorag the lemmng process re develop aulomata representationq that is based on adding a pro)per penalty tunelton to the mdinary cost. Successful experinental results are shown for tuducttvc mcrenc.' 1 regular gramrnar Keywords: Attemala, backpropagation t[rough trine, high(rder neural networks, induclix. c reference. learning item hints. radial basis ftlnctions, rectarent radial basra tnnclmns. recurrent netw(>rks 1. introduction The ability (>f learning fiom examples is certainly lhe most appealing l'eature c)f neu ral networks. In the last lw years, several researchers have used conncctontst models for solving different kinds ol probfoms ranging from robot control to pattern recogmtioa Coping wilh optimization of [unctions with several thousands of x, ariablcs s quite common Surprisingly, in many practical cases, global or near global r)ptimization is attained also wth non sophistteated numertcal methods. For example, successlul applications of neural nets fi)r recognition of handwritten characters (le Cun, 189) md for phoncmc discrimination (Waibcl c al., 1989) ave bccn proposed which d() n<,t report serious convergence problems Some attempts to understand the theoretical reasons )r lhc successes and atlures of supervised }earrang schemes have been carried oat which explain when such schemes are likely to succeed in discovering oplmal solutions (Bmnchini cl al.. 1994; Gori & Tesi, 1992; Yu, 192), and to gencrali7c to new examples (Baum & Haussler. 1989L These results give st>me ...
A Neural Network Primer
, 1994
"... Neural networks are composed of basic units somewhat analogous to neurons. These units are linked to each other by connections whose strength is modifiable as a result of a learning process or algorithm. Each of these units integrates independently (in parallel) the information provided by its sy ..."
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Cited by 30 (8 self)
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Neural networks are composed of basic units somewhat analogous to neurons. These units are linked to each other by connections whose strength is modifiable as a result of a learning process or algorithm. Each of these units integrates independently (in parallel) the information provided by its synapses in order to evaluate its state of activation. The unit response is then a linear or nonlinear function of its activation. Linear algebra concepts are used, in general, to analyze linear units, with eigenvectors and eigenvalues being the core concepts involved. This analysis makes clear the strong similarity between linear neural networks and the general linear model developed by statisticians. The linear models presented here are the perceptron, and the linear associator. The behavior of nonlinear networks can be described within the framework of optimization and approximation techniques with dynamical systems (e.g., like those used to model spin glasses). One of the main notio...
Computational Complexity Of Neural Networks: A Survey
, 1994
"... . We survey some of the central results in the complexity theory of discrete neural networks, with pointers to the literature. Our main emphasis is on the computational power of various acyclic and cyclic network models, but we also discuss briefly the complexity aspects of synthesizing networks fr ..."
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Cited by 23 (6 self)
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. We survey some of the central results in the complexity theory of discrete neural networks, with pointers to the literature. Our main emphasis is on the computational power of various acyclic and cyclic network models, but we also discuss briefly the complexity aspects of synthesizing networks from examples of their behavior. CR Classification: F.1.1 [Computation by Abstract Devices]: Models of Computationneural networks, circuits; F.1.3 [Computation by Abstract Devices ]: Complexity Classescomplexity hierarchies Key words: Neural networks, computational complexity, threshold circuits, associative memory 1. Introduction The currently again very active field of computation by "neural" networks has opened up a wealth of fascinating research topics in the computational complexity analysis of the models considered. While much of the general appeal of the field stems not so much from new computational possibilities, but from the possibility of "learning", or synthesizing networks...
Computing with Truly Asynchronous Threshold Logic Networks
 THEORETICAL COMPUTER SCIENCE
, 1995
"... We present simulation mechanisms by which any network of threshold logic units with either symmetric or asymmetric interunit connections (i.e., a symmetric or asymmetric "Hopfield net") can be simulated on a network of the same type, but without any a priori constraints on the order of upd ..."
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Cited by 19 (7 self)
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We present simulation mechanisms by which any network of threshold logic units with either symmetric or asymmetric interunit connections (i.e., a symmetric or asymmetric "Hopfield net") can be simulated on a network of the same type, but without any a priori constraints on the order of updates of the units. Together with earlier constructions, the results show that the truly asynchronous network model is computationally equivalent to the seemingly more powerful models with either ordered sequential or fully parallel updates.
Neural Networks and Complexity Theory
 In Proc. 17th International Symposium on Mathematical Foundations of Computer Science
, 1992
"... . We survey some of the central results in the complexity theory of discrete neural networks, with pointers to the literature. 1 Introduction The recently revived field of computation by "neural" networks provides the complexity theorist with a wealth of fascinating research topics. Whi ..."
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Cited by 16 (4 self)
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. We survey some of the central results in the complexity theory of discrete neural networks, with pointers to the literature. 1 Introduction The recently revived field of computation by "neural" networks provides the complexity theorist with a wealth of fascinating research topics. While much of the general appeal of the field stems not so much from new computational possibilities, but from the possibility of "learning", or synthesizing networks directly from examples of their desired inputoutput behavior, it is nevertheless important to pay attention also to the complexity issues: firstly, what kinds of functions are computable by networks of a given type and size, and secondly, what is the complexity of the synthesis problems considered. In fact, inattention to these issues was a significant factor in the demise of the first stage of neural networks research in the late 60's, under the criticism of Minsky and Papert [51]. The intent of this paper is to survey some of the centra...
A cellular genetic algorithm with selfadjusting acceptance thereshold
 in Proceedings of the First IEE/IEEE International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications
, 1995
"... We present a genetic algorithm (GA) whose population possesses a spatial structure. The GA is formulated as a probabilistic cellular automaton: The individuals are distributed over a connected graph and the genetic operators are applied locally in some neighborhood of each individual. By adding a se ..."
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Cited by 12 (3 self)
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We present a genetic algorithm (GA) whose population possesses a spatial structure. The GA is formulated as a probabilistic cellular automaton: The individuals are distributed over a connected graph and the genetic operators are applied locally in some neighborhood of each individual. By adding a self–organizing acceptance threshold schedule to the proportionate reproduction operator we can prove that the algorithm converges to the global optimum. First results for a multiple knapsack problem indicate a significant improvement in convergence behavior. The algorithm can be mapped easily onto parallel computers. 1
The Computational Power of Discrete Hopfield Nets with Hidden Units
 Neural Computation
, 1996
"... We prove that polynomial size discrete Hopfield networks with hidden units compute exactly the class of Boolean functions PSPACE/poly, i.e., the same functions as are computed by polynomial spacebounded nonuniform Turing machines. As a corollary to the construction, we observe also that networks wi ..."
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Cited by 11 (6 self)
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We prove that polynomial size discrete Hopfield networks with hidden units compute exactly the class of Boolean functions PSPACE/poly, i.e., the same functions as are computed by polynomial spacebounded nonuniform Turing machines. As a corollary to the construction, we observe also that networks with polynomially bounded interconnection weights compute exactly the class of functions P/poly, i.e., the class computed by polynomial timebounded nonuniform Turing machines.
An Overview Of The Computational Power Of Recurrent Neural Networks
 Proceedings of the 9th Finnish AI Conference STeP 2000{Millennium of AI, Espoo, Finland (Vol. 3: &quot;AI of Tomorrow&quot;: Symposium on Theory, Finnish AI Society
, 2000
"... INTRODUCTION The two main streams of neural networks research consider neural networks either as a powerful family of nonlinear statistical models, to be used in for example pattern recognition applications [6], or as formal models to help develop a computational understanding of the brain [10]. His ..."
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Cited by 10 (3 self)
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INTRODUCTION The two main streams of neural networks research consider neural networks either as a powerful family of nonlinear statistical models, to be used in for example pattern recognition applications [6], or as formal models to help develop a computational understanding of the brain [10]. Historically, the brain theory interest was primary [32], but with the advances in computer technology, the application potential of the statistical modeling techniques has shifted the balance. 1 The study of neural networks as general computational devices does not strictly follow this division of interests: rather, it provides a general framework outlining the limitations and possibilities aecting both research domains. The prime historic example here is obviously Minsky's and Papert's 1969 study of the computational limitations of singlelayer perceptrons [34], which was a major inuence in turning away interest from neural network learning to symbolic AI techniques for more
Injecting Nondeterministic Finite State Automata into Recurrent Neural Networks
, 1993
"... In this paper we propose a method for injecting timewarping nondeterministic finite state automata into recurrent neural networks. The proposed algorithm takes as input a set of automata transition rules and produces a recurrent architecture. The resulting connection weights are specified by means ..."
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Cited by 9 (1 self)
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In this paper we propose a method for injecting timewarping nondeterministic finite state automata into recurrent neural networks. The proposed algorithm takes as input a set of automata transition rules and produces a recurrent architecture. The resulting connection weights are specified by means of linear constraints. In this way, the network is guaranteed to carry out the assigned automata rules, provided the weights belong to the constrained domain and the inputs belong to an appropriate range of values, making possible a boolean interpretation. In a subsequent phase, the weights can be adapted in order to obtain the desired behavior on corrupted inputs, using learning from examples. One of the main concerns of the proposed neural model is that it is no longer focussed exclusively on learning, but also on the identification of significant architectural and weight constraints derived systematically from automata rules, representing the partial domain knowledge on a given problem. I...
On the Computational Power of Discrete Hopfield Nets
 In: Proc. 20th International Colloquium on Automata, Languages, and Programming
, 1993
"... . We prove that polynomial size discrete synchronous Hopfield networks with hidden units compute exactly the class of Boolean functions PSPACE/poly, i.e., the same functions as are computed by polynomial spacebounded nonuniform Turing machines. As a corollary to the construction, we observe also th ..."
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Cited by 7 (4 self)
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. We prove that polynomial size discrete synchronous Hopfield networks with hidden units compute exactly the class of Boolean functions PSPACE/poly, i.e., the same functions as are computed by polynomial spacebounded nonuniform Turing machines. As a corollary to the construction, we observe also that networks with polynomially bounded interconnection weights compute exactly the class of functions P/poly. 1 Background Recurrent, or cyclic, neural networks are an intriguing model of massively parallel computation. In the recent surge of research in neural computation, such networks have been considered mostly from the point of view of two types of applications: pattern classification and associative memory (e.g. [16, 18, 21, 24]), and combinatorial optimization (e.g. [1, 7, 20]). Nevertheless, recurrent networks are capable also of more general types of computation, and issues of what exactly such networks can compute, and how they should be programmed, are becoming increasingly topica...