Download Advanced Intelligent Computing Theories and Applications. by De-Shuang Huang, Laurent Heutte, Marco Loog PDF

By De-Shuang Huang, Laurent Heutte, Marco Loog

This e-book - together with the 2 volumes LNCS 4681 and LNAI 4682 - constitutes the refereed lawsuits of the 3rd overseas convention on clever Computing, ICIC 2007, held in Qingdao, China in August 2007. The clever computing expertise encompasses a variety of suggestions similar to synthetic intelligence, perceptual and trend reputation, evolutionary and adaptive computing, informatics theories and functions, computational neuroscience and bioscience, smooth computing, case dependent and limited reasoning, brokers, networking and computing device supported co-operative operating, human laptop interface matters. ICIC subject matter unifies the image of up to date clever computing concepts as an essential idea that highlights the developments in complex computational intelligence and bridges theoretical examine with purposes.

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Locally Asymptotical Behaviors of Delayed BAM Neural Networks 27 Proof. In this case, φ(x∗i ) = φ(yk∗ ) = 0, i = 1, 2, · · · , n, k = 1, 2, · · · , m, and A∗ = 0, B ∗ = 0, furthermore, we have T = 0, ||T ||∞ = 0 < 1. The proof is complete. Corollary 3. Assume that z ∗ = (x∗ , y ∗ )T is an equilibrium of System (1). If |x∗i | < r, i = 1, 2, · · · , N, 1 ≤ N ≤ n, |x∗i | > r, i = N + 1, · · · , n and |yk∗ | < r, k = 1, 2, · · · , M, 1 ≤ M ≤ m, |yk∗ | > r, k = M + 1, · · · , m, and M N max { 1≤i≤n |bki |} < 1 |aik |} < 1, max { 1≤k≤m k=1 i=1 then, z ∗ is delay-dependent asymptotical stability.

Oil Field Development Using Neural Network Model 37 Oil Production Rate of Sector(STB/Day) 2400 without new well 2200 field development scheme, NS-ANN 2000 1800 1600 1400 1200 1000 0 365 730 1095 1460 1825 Time(Days) Fig. 8. Effect of new well on production profile of sector field obtained by NS-ANN model Acknowledgments The authors would like to acknowledge the financial and scientific support from the Islamic Azad University, Karaj branch (KIAU). We would like to thank the Research Institute of Petroleum Industry (RIPI), Iranian Offshore Oil Company (IOOC) for giving the required data for this project.

The weights between the normalized layer and the output layer are set as the following equation after merging. wj = 1 (w j + w p ). 3 Deleting Neurons Aiming at the external migration, attenuation, and atrophy phenomena of hidden neurons during the network learning process, the strategy of deleting neurons is given as follows. count > C max . (10) w j < wmin . (11) σ j < σ min . (12) where, count denotes the cumulative times of a hidden neuron not being activated from the last time of being activated to the current time, C max , wmin , and σ min are thresholds of the external migration, attenuation, and atrophy of redundant hidden neurons which should be selected appropriately.

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