Advanced Intelligent Computing Theories and Applications. by De-Shuang Huang, Laurent Heutte, Marco Loog

By De-Shuang Huang, Laurent Heutte, Marco Loog

This ebook - together with the 2 volumes LNCS 4681 and LNAI 4682 - constitutes the refereed complaints of the 3rd foreign convention on clever Computing, ICIC 2007, held in Qingdao, China in August 2007. The clever computing know-how contains a variety of ideas reminiscent of synthetic intelligence, perceptual and development popularity, evolutionary and adaptive computing, informatics theories and purposes, computational neuroscience and bioscience, delicate computing, case established and limited reasoning, brokers, networking and laptop supported co-operative operating, human laptop interface concerns. ICIC subject matter unifies the image of latest clever computing concepts as an fundamental idea that highlights the developments in complicated computational intelligence and bridges theoretical study with purposes.

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Additional info for Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques: Third International Conference ... in Computer and Information Science)

Sample text

The generalized saturation function are the connected weigh from neuron k in layer Y to neuron i in layer X and from neuron i in layer X to neuron k in layer Y respectively, I1 , I2 are the bias vectors of the neurons in layer X and layer Y respectively. This paper is organized as following: In Section 2, some notations and lemmas are given used in this paper latter; In Section 3, system’s equation (1) is rewritten by decomposing the state space and we obtain the stability criteria which is independent of τ , the attractive domain is also estimated; And in Section 4, the results are concluded and the paper’s feature is pointed out.

The article aims at combining a FAST-based algorithm and Q-learning algorithm into a reinforcement learning algorithm called ARM Q-learning algorithm that improves Q-learning algorithm on learning speed and learning stability. The article is organized in four sections; the first Section being this introduction. In Section 2 we discuss the theories of clustering and reinforcement learning. Section 3 presents the simulation results and results analysis. Finally, discussion is drawn in Section 4. 1 Adaptive Resonance Theory ART is a dynamic neural network structure.

S. Huang, L. Heutte, and M. ): ICIC 2007, CCIS 2, pp. 21–28, 2007. c Springer-Verlag Berlin Heidelberg 2007 22 J. Chen and J. Xu g(x) = (s(x1 ), s(x2 ), · · · , s(xn ))T , s(·) has the following general saturation linearity form with any r > 0 and the figure: s(u) = 1 (|u + r| − |u − r|), u ∈ IR . 2 (2) A = (aik )n×m , B = (bki )m×n are the weight matrices between neurons, aik , bki s ✻ r −r r o −r ✲ u Fig. 1. The generalized saturation function are the connected weigh from neuron k in layer Y to neuron i in layer X and from neuron i in layer X to neuron k in layer Y respectively, I1 , I2 are the bias vectors of the neurons in layer X and layer Y respectively.

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