Advanced Intelligent Computing Theories and Applications: by Yongquan Zhou, Yanlian Du, Zhengxin Huang (auth.), De-Shuang

By Yongquan Zhou, Yanlian Du, Zhengxin Huang (auth.), De-Shuang Huang, Martin McGinnity, Laurent Heutte, Xiao-Ping Zhang (eds.)

The foreign convention on clever Computing (ICIC) was once shaped to supply an annual discussion board devoted to the rising and hard themes in synthetic intel- gence, laptop studying, trend attractiveness, picture processing, bioinformatics, and computational biology. It goals to assemble researchers and practitioners from either academia and to percentage principles, difficulties, and ideas with regards to the m- tifaceted elements of clever computing. ICIC 2010, held in Changsha, China, August 18-21, 2010, constituted the sixth - ternational convention on clever Computing. It equipped upon the luck of ICIC 2009, ICIC 2008, ICIC 2007, ICIC 2006, and ICIC 2005, that have been held in Ulsan, Korea, Shanghai, Qingdao, Kunming and Hefei, China, respectively. This 12 months, the convention centred in general at the theories and methodologies in addition to the rising functions of clever computing. Its target was once to unify the image of up to date clever computing recommendations as an critical idea that highlights the traits in complex computational intelligence and bridges theoretical learn with purposes. as a result, the subject matter for this convention used to be “Advanced clever Computing know-how and Applications.” Papers concentrating on this subject matter have been solicited, addressing theories, methodologies, and functions in technology and technology.

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Extra resources for Advanced Intelligent Computing Theories and Applications: 6th International Conference on Intelligent Computing, ICIC 2010, Changsha, China, August 18-21, 2010. Proceedings

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References 1. : A Cooperative Approach to Particle Swarm Optimization. IEEE Transactions on Evolutionary Computation 8, 225–239 (2004) 2. : An Orthogonal Genetic Algorithm with Quantization for Global Numerical Optimization. IEEE Trans. on Evolution Computation 5, 41–53 (2001) 3. : Good Point Set Based Genetic Algorithm. Chinese J. Computers 24, 917–922 (2001) 4. : A Good Nodes Set Evolution Strategy for Constrained Optimization. In: IEEE Congress of Evolutionary Computation, Singapore, pp. 943–950 (2007) 5.

Fig. 2. Performance results of a sample run of ANN on AFFA dataset In this case study, 20 training cases are used in Matlab neural network tool that executes back propagation feed forward neural network technique. After sensing multiple inputs, weighted inputs are evaluated in the neuron that range from 0 to 1. Learning the training cases requires the hit and trial method to find the best performance by 20 M. J. Khan measuring the root mean squared error (RMSE) and applying different hidden layers.

The general nonlinear programming problem can be formulated as (in minimization sense): G Find x which optimizes G G f (x ) , x = ( x1, x2 ,", xn ) ∈ ℜn , (1) G where x ∈ Ω ⊆ S is decision variable. S is an n-dimensional rectangle space in ℜ n and is defined by its lower and upper bounds: l (i) ≤ xi ≤ u(i), 1 ≤ i ≤ n, The feasible region Ω ⊆ S is defined by a set of m additional constraints ( p ≥ 0 ): G g j ( x ) ≤ 0, j = 1,", q , and h j ( xG ) = 0 , j = q + 1,", p , (2) (3) where q is the number of inequality constraints and ( p − q ) is the number of equality constraints (in both cases, constraints could be linear or nonlinear).

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