Download Bio-Inspired Computing and Applications: 7th International by YuXi Liu, ZhiFeng Zhang (auth.), De-Shuang Huang, Yong Gan, PDF

By YuXi Liu, ZhiFeng Zhang (auth.), De-Shuang Huang, Yong Gan, Prashan Premaratne, Kyungsook Han (eds.)

The three-volume set LNCS 6838, LNAI 6839, and LNBI 6840 constitutes the completely refereed post-conference court cases of the seventh overseas convention on clever Computing, ICIC 2011, held in Zhengzhou, China, in August 2011. This quantity comprises ninety three revised complete papers, from a complete of 281 shows on the convention - conscientiously reviewed and chosen from 832 preliminary submissions. The papers handle all matters in complex clever Computing, specifically Methodologies and functions, together with theories, methodologies, and purposes in technology and know-how. They comprise quite a number strategies similar to synthetic intelligence, trend reputation, evolutionary computing, informatics theories and purposes, computational neuroscience and bioscience, delicate computing, human computing device interface matters, etc.

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Additional info for Bio-Inspired Computing and Applications: 7th International Conference on Intelligent Computing, ICIC 2011, Zhengzhou,China, August 11-14. 2011, Revised Selected Papers

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G. ) IWANN 1993. LNCS, vol. 686, pp. 137–142. Springer, Heidelberg (1993) 13. : Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989) 14. : Machine Learning: Neural Networks, Genetic Algorithms, and Fuzzy Systems. com Abstract. The paper studies finite precision Extended Alternating Projection Neural Network (FPEAP) and its related problems. An improved training method of FPEAP has been present after considering the finite precision influence on the training method of EAP.

The training data is based on the data from 2008/01/01 to 2008/12/31 and the test data is based on the data from 2009/01/01 to 2009/06/31. For Citigroup the size of the training and test data respectively is 254 and 124. For Motors Liquidation Company the size of the training and test data respectively is 253 and 124. In this paper we try to use various numbers of layers and nodes for testing our model of PARCONE. Figure 3 show as our model predicted the stock price index and actual value of original stock price index.

Simulation researches on 10-city TSP indicate that the proposed networks can find the optimal solutions of combinatorial optimization problems. Keywords: Self-feedback, Chaotic neural network, Energy function, Morlet wavelet function. 1 Introduction Chaotic neural networks (CNNs) can acquire the ability to escape from the local minima of the energy function by introducing chaotic search mechanism into the original Hopfield neural network (HNN) [1-7]. Different from external chaos, chaotic search mechanism in CNN is generated by the self-feedback item of CNN.

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