By Yuhu Cheng, Xuesong Wang, Wei Sun (auth.), De-Shuang Huang, Laurent Heutte, Marco Loog (eds.)

The foreign convention on clever Computing (ICIC) used to be shaped to supply an annual discussion board devoted to the rising and tough issues in synthetic intelligence, desktop studying, bioinformatics, and computational biology, and so forth. It goals to deliver - gether researchers and practitioners from either academia and to proportion rules, difficulties and recommendations relating to the multifaceted elements of clever computing. ICIC 2007, held in Qingdao, China, August 21-24, 2007, constituted the 3rd - ternational convention on clever Computing. It equipped upon the good fortune of ICIC 2006 and ICIC 2005 held in Kunming and Hefei, China, 2006 and 2005, respectively. This yr, the convention centred typically at the theories and methodologies in addition to the rising purposes of clever computing. Its objective used to be to unify the image of latest clever computing strategies as an essential idea that highlights the tendencies in complex computational intelligence and bridges theoretical study with purposes. as a result, the subject matter for this convention was once “Advanced clever Computing know-how and Applications”. Papers concentrating on this subject have been solicited, addressing theories, methodologies, and functions in technological know-how and technology.

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Extra resources for Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques: Third International Conference on Intelligent Computing, ICIC 2007, Qingdao, China, August 21-24, 2007. Proceedings

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1 1 − x + 2 x 2 exp⎜⎜ − ⎟⎟ . ⎝ 2 ⎠ ( ) (17) 8 Y. Cheng, X. Wang, and W. Sun The number of the training samples is N = 600 , and each input sample x obeys the uniform distribution within the range of [-4, 4]. 04. 05 . The normalized training error is defined as follows. 1 N RSME = where N ∑ (e ) 2 k (18) . k =1 ek is the training error of the kth training sample. We applied two kinds of fuzzy normalized RBF networks to approximate the nonlinear function described in Eq. (18) respectively under the same training samples and the same running parameters during the simulation process.

X = (x1 , x2 , · · · , xn )T ∈ IRn , y = (y1 , y2 , · · · , ym )T ∈ IRm are the state vectors of layer X and layer Y of the neural networks , x(t − τ ) = (x1 (t − τ1 ), x2 (t − τ2 ), · · · , xn (t − τn ))T , y(t − τ ) = (y1 (t − τn+1 ), y2 (t − τn+2 ), · · · , ym (t − τn+m ))T , τi ≥ 0 is the time delay of the neuron i and 0 ≤ τi ≤ τ, i = 1, 2, · · · , n + m. -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 .

A. Sajjadian and Mr. Taheri for their useful suggestions, valuable information. References 1. : Artificial Neural Networks. Mcgraw-Hill (1997) 2. : Fundamentals of Neural Networks. Prentice-Hall, Englewood Cliffs, NJ(1994) 3.

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