By Yuhu Cheng, Xuesong Wang, Wei Sun (auth.), De-Shuang Huang, Laurent Heutte, Marco Loog (eds.)
The foreign convention on clever Computing (ICIC) was once shaped to supply an annual discussion board devoted to the rising and demanding subject matters in synthetic intelligence, computer studying, bioinformatics, and computational biology, and so forth. It goals to carry - gether researchers and practitioners from either academia and to percentage rules, difficulties and strategies relating to the multifaceted features 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 luck of ICIC 2006 and ICIC 2005 held in Kunming and Hefei, China, 2006 and 2005, respectively. This 12 months, the convention focused more often than not at the theories and methodologies in addition to the rising functions of clever computing. Its objective used to be to unify the image of up to date clever computing strategies as an crucial idea that highlights the developments in complicated computational intelligence and bridges theoretical learn with functions. for that reason, the subject matter for this convention was once “Advanced clever Computing expertise and Applications”. Papers targeting this topic 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. With Aspects of Contemporary Intelligent Computing Techniques: Third International Conference on Intelligent Computing, ICIC 2007, Qingdao, China, August 21-24, 2007. Proceedings
Artificial neural network are predictive tools capable of solving highly non-linear problems. A neural net consists of a large number of simple processing elements called neurons. Each neuron of the neural network is connected to other neurons by means of directed communication links, each with an associated weight, which represents information being used by the net to solve the problem. At the beginning, these connections are assigned independent weighting factors. The input to each node is multiplied by its associated weighting factor and then summed with the product of each of the other input nodes and their respective weighting factors.
Consider the following delayed BAM neural networks: x(t) ˙ = −x(t) + Af y(t − τ ) + I1 y(t) ˙ = −y(t) + Bg x(t − τ ) + I2 . (1) It has two layers, denoted by the symbols X and Y . 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.
Some of the main challenges faced during a field development study include: The presence of large degrees of freedom to be examined; the possibility of having extensive interaction of parameters the sparse nature of the field data; the time constraints encountered. Conventionally, in field development studies, the existing production data is utilized to perform a history match. -S. Huang, L. Heutte, and M. ): ICIC 2007, CCIS 2, pp. 29–38, 2007. © Springer-Verlag Berlin Heidelberg 2007 30 F. Salehi, R.