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【期刊论文】Asymmetric Hopfield-type Networks: Theory and Applications
徐宗本, ZONG-BEN Xu, Guo-QING HU AND CHUNG-PING KWONG
Neural Networks Vol. 9 No.3 pp. 483-501, 1996,-0001,():
-1年11月30日
The Hopfield-type networks with asymmetric interconnections are studied from the standpoint of taking them as computational models. Two fundamental properties, feasibility and reliability, of the networks related to their use are established with a newly-developed convergence principle and a classification theory on energy functions. The convergence principle generalizes that previously known for symmetric networks and underlies the feasibility. The classification theory, which categorizes the traditional energy functions into regular, normal and complete ones according to their roles played in connection with the corresponding networks, implies that the reliability and high efficiency of the networks can follow respectively from the regularity and the normality of the corresponding energy functions. The theories developed have been applied to solve a classical NP-hard graph theory problem: finding the maximal independent set of a graph. Simulations demonstrate that the algorithms deduced from the asymmetric theories outperform those deduced from the symmetric theory.
Asymmetric Hopfield-type networks,, Convergence principle,, Classification theory on energy functions,, Regular and normal correspondence,, Maximal independent set problem,, Combinatorial optimization.,
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【期刊论文】A New Model of Simulated Evolutionary Computation-Convergence Analysis and Specifications
徐宗本, Kwong-Sak Leung, Senior Member, IEEE, Qi-Hong Duan, Zong-Ben Xu, and C. K. Wong, Fellow
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, VOL. 5, NO.1, FEBRUARY 2001,-0001,():
-1年11月30日
There have been various algorithms designed for simulating natural evolution. This paper proposes a new simulated evolutionary computation model called the abstract evolutionary algorithm (AEA), which unifies most of the currently known evolutionary algorithms and describes the evolution as an abstract stochastic process composed of two fundamental operators: selection and evolution operators. By axiomatically characterizing the properties of the fundamental selection and evolution operators, several general convergence theorems and convergence rate estimations for the AEA are established. The established theorems are applied to a series of known evolutionary algorithms, directly yielding new convergence conditions and convergence rate estimations of various specific genetic algorithms and evolutionary strategies. The present work provides a significant step toward the establishment of a unified theory of simulated evolutionary computation.
Aggregating and scattering rate,, evolutionary strategy,, genetic algorithm,, selection intensity,, selection pressure,, stochastic process.,
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【期刊论文】A Decomposition Principle for Complexity Reduction of Artificial Neural Networks
徐宗本, ZONG-BEN XU AND CHUNG-PING KWONG
Neural Networks Vol. 9 No.6 pp. 999-1016, 1996,-0001,():
-1年11月30日
A decomposition principle is developed for systematic determination of the dimensionality and the connections of Hopfield-type associative memory networks. Given a set of high dimensional prototype vectors of given memory objects, we develop decomposition algorithms to extract a set o flower dimensional key features of the pattern vectors. Every key feature can be used to build an associative memory with the lowest complexity, and more than one key feature can be simultaneously used to build networks with higher recognition accuracy. In the latter case, we further propose a "decomposed neural network" based on a new encoding scheme to reduce the network complexity. In contrast to the original Hopfield network, the decomposed networks not only increase the network's storage capacity, but also reduce the network's connection complexity from quadratic to linear growth with the network dimension. Both theoretical analysis and simulation results demonstrate that the proposed principle is powerful.
Decomposition principle,, Hopfield-type networks,, Interpolation operator,, Best approximation projection,, Associative memories,, Elementary matrix transformation
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【期刊论文】A comparative study of two modeling approaches in neural networks
徐宗本, Zong-Ben Xua, Hong Qiaob, Jigen Penga, Bo Zhangc, *
Neural Networks 17(2004)73-85,-0001,():
-1年11月30日
The neuron state modeling and the local field modeling provides two fundamental modeling approaches to neural network research, based on which a neural network system can be called either as a static neural network model or as a local field neural network model. These two models are theoretically compared in terms of their trajectory transformation property, equilibrium correspondence property, nontrivial attractive manifold property, global convergence as well as stability in many different senses. The comparison reveals an important stability invariance property of the two models in the sense that the stability (in any sense) of the static model is equivalent to that of a subsystem deduced from the local field model when restricted to a specific manifold. Such stability invariance property lays a sound theoretical foundation of validity of a useful, cross-fertilization type stability analysis methodology for various neural network models.
Static neural network modeling, Local field neural network modeling, Recurrent neural networks, Stability analysis, Asymptotic stability, Exponential stability, Global convergence, Globally attractive
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