Download Advanced intelligent computing theories and applications : by Huang D.-S., Zhao Z., Bevilacqua V., Figueroa J.C. (eds.) PDF

By Huang D.-S., Zhao Z., Bevilacqua V., Figueroa J.C. (eds.)

This ebook constitutes the refereed complaints of the sixth overseas convention on clever Computing, ICIC 2010, held in Changsha, China, in August 2010. The eighty five revised complete papers provided have been rigorously reviewed and chosen from a quite a few submissions. The papers are geared up in topical sections on neural networks, evolutionary studying & genetic algorithms, fuzzy idea and types, fuzzy platforms and smooth computing, particle swarm optimization and area of interest know-how, supervised & semi-supervised studying, unsupervised & reinforcement studying, combinatorial & numerical optimization, platforms biology and computational biology, neural computing and optimization, nature encouraged computing and optimization, wisdom discovery and knowledge mining, synthetic existence and synthetic immune platforms, clever computing in picture processing, specific consultation on new hand dependent biometric equipment, targeted consultation on contemporary advances in snapshot segmentation, distinct consultation on theories and purposes in complex clever computing, exact consultation on seek established software program engineering, unique consultation on bio-inspired computing and functions, distinct consultation on develop in dimensionality relief equipment and its functions, precise consultation on protein and gene bioinformatics: equipment and functions

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If rank ( FP ) = rank ( F ) , then s QA (∞) = s BQ ( ∞ ) = FQ FP+ s P (0) Proof. Let rank ( FP ) = rank ( F ) = r decompose F as follows: ⎡ C1 ⎤ ⎡C ⎤ P× r r× N L×r , D ∈C , C1 ∈ C , F = ⎢ 1 ⎥ ⋅ D = C0 ⋅ D , where C 0 = ⎢ ⎥ ∈ C C C ⎣ 2⎦ ⎣ 2⎦ C 2 ∈ C Q× r FP = C1 D ,F Q = C2 D , rank ( FP ) = rank ( F ) = r = rank (C1 D) ≤ rank (C1 ) , namely rank (C1 ) ≥ r (4) and r = rank(C1D) ≥ rank(C1 ) + rank(D) − r = rank(C1 ) , namely rank(C1 ) ≤ r Combining (4) and (5), we obtain rank (C1 ) = r . Thus FP+ = (C1 D ) + = D + C1+ FQ ( I − FP+ FP ) = FQ − FQ FP+ FP = C 2 D − C 2 DD + (C1H C1 ) −1 C1H C1 D = C2 D − C2 DD+ D = C2 D − C2 D = 0 (5) Further Research on Extended Alternating Projection Neural Network Since FQ ( I − FP+ FP ) = 0 ⇒ ( FQ ( I − FP+ FP )) + = 0 , we can deduce 37 sBQ (f) FQFPsP (0) sQA (f) , and our proof is complete.

For the sake of presentation clarity, we only add hidden nodes one by one in our proposed EM-ELM-TV algorithm, which can be easily generalized to the group-by-group node addition means. Incremental-Based ELM Algorithms for TV-NN 13 Assume we have a set of training data {(x[n], t[n])}N n=1 , the target matrix T, the residual matrix Ek = Gk G†k T − T, the maximum number of hidden nodes Kmax , and the expected learning accuracy . We get Algorithm 2. Algorithm 2. EM-ELM-TV 1: Randomly generate one hidden node.

200806130003). References 1. : Neural Networks and Physical Systems with Emergent Collective Abilities. Proceedings of the National Academy of Sciences, USA, 2554–2558 (1982) 2. : Oscillatory Phenomena and Stability of Periodic Solutions in a Simple Neural Network with Delay. Nonlinear Phenomena in Complex Systems 5, 407–417 (2002) 3. : Absolutely Exponential Stability of a Class of Neural Networks with Unbounded Delay. J. Neural Networks 17, 391–397 (2004) 4. : Global Exponential Stability of Hopfield Neural Networks.

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