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Download PDF by Yuhu Cheng, Xuesong Wang, Wei Sun (auth.), De-Shuang Huang,: Advanced Intelligent Computing Theories and Applications.

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

ISBN-10: 3540742816

ISBN-13: 9783540742814

ISBN-10: 3540742824

ISBN-13: 9783540742821

The foreign convention on clever Computing (ICIC) used to be shaped to supply an annual discussion board devoted to the rising and hard themes in man made intelligence, laptop studying, bioinformatics, and computational biology, and so on. It goals to deliver - gether researchers and practitioners from either academia and to percentage principles, 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 outfitted upon the luck 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 goal was once to unify the image of latest clever computing thoughts as an critical idea that highlights the traits in complicated computational intelligence and bridges theoretical learn with purposes. consequently, the topic for this convention was once “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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Additional info 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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15) and ησ are learning rates for centers and widths respectively. The updating rule of the connecting weights between the normalized and the output layers is w j (t + 1) = w j (t ) − η w ∂E = w j (t ) + η w ( y d − y )Φ j (xt ) . ∂w j (16) where, η w is a learning rate for network weights. 4 Simulation Research Using the following nonlinear function to test the approximation property of the proposed RBF network. 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].

The FAST Architecture: A Neural Network with Flexible Adaptable-size Topology, Proceedings of Fifth International Conference on Microelectronics for Neural Networks, 12-14, (1996) 337–340 9. :Neural Models of Incremental Supervised and Unsupervised Learning. PhD thesis, Swiss Federal Institute of Technology-Lausanne, Lausanne, DPFL, Thesis 863(1990). 10. :Neuronlike Adaptive Elements that Can Solve Difficult Learning Control Problems,IEEE Transactions on System, Man, and Cybernetics SMC-13 (1983)834-846 11.

Production profile of well C predicted by simulator and NS-ANN model Figure 6 compares cumulative oil production between new wells A, B and C predicted by numerical simulator and NS-ANN model. This Figure shows with the exception of well B, the perfect match between the NS-ANN’s response and the cumulative oil production obtained from a numerical simulator for wells A and C. Figure 7 shows the best and the worst case locations for the new well. The lighter shades in the Figure indicate regions of high cumulative field productivity while darker shades represent regions that have already been depleted to a certain extent, 36 F.

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


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