Bio-Inspired Computing and Applications: 7th International by YuXi Liu, ZhiFeng Zhang (auth.), De-Shuang Huang, Yong Gan,

By YuXi Liu, ZhiFeng Zhang (auth.), De-Shuang Huang, Yong Gan, Prashan Premaratne, Kyungsook Han (eds.)

The three-volume set LNCS 6838, LNAI 6839, and LNBI 6840 constitutes the completely refereed post-conference lawsuits of the seventh overseas convention on clever Computing, ICIC 2011, held in Zhengzhou, China, in August 2011. This quantity comprises ninety three revised complete papers, from a complete of 281 shows on the convention - conscientiously reviewed and chosen from 832 preliminary submissions. The papers tackle all matters in complex clever Computing, particularly Methodologies and purposes, together with theories, methodologies, and purposes in technology and expertise. They comprise more than a few innovations akin to man made intelligence, development reputation, evolutionary computing, informatics theories and purposes, computational neuroscience and bioscience, smooth computing, human laptop interface matters, etc.

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Extra resources for Bio-Inspired Computing and Applications: 7th International Conference on Intelligent Computing, ICIC 2011, Zhengzhou,China, August 11-14. 2011, Revised Selected Papers

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Addison-Wesley, Reading (1989) 14. : Machine Learning: Neural Networks, Genetic Algorithms, and Fuzzy Systems. com Abstract. The paper studies finite precision Extended Alternating Projection Neural Network (FPEAP) and its related problems. An improved training method of FPEAP has been present after considering the finite precision influence on the training method of EAP. Then the mathematical relation among the factors influencing the association times has been studied. Finally simulation experiments have been designed and simulation results demonstrate validity of theoretical analyses.

In the conductance-based integrate-and-fire neuron model, we have g xhex , y (t ) dt g xvex , y (t ) dt =− =− 1 g xhih , y (t ) ex g xhex , y (t ) + S x , y (t ) q , τ ex 1 dt g xvih , y (t ) ex g xvex , y (t ) + S x , y (t ) q , τ ex dt =− =− 1 τ ih 1 τ ih ih g xhih , y (t ) + S x , y (t ) q , (1) ih g xvih , y (t ) + S x , y (t ) q , (2) where τex and τih are time constants for excitatory and inhibitory synapses, ex is short for excitatory, and ih for inhibitory. If the neuron generates a spike, the conductance of excitatory and inhibitory synapses increase an amount of q ex and qih respectively.

For the proposed CNN model, the energy modifier can be described as n xi H = λ ∑ ∫ ziφ ( si , ui , x − I 0 )dx i (15) 0 In the following, we apply the unified framework to verify (15) is the energy modifier of the proposed CNN: ∂ ( E Hop + H ) dy i ∂E =− =− dt ∂ xi ∂ xi =− yi τ ⎡ n ⎤ ⎣⎢ j≠i ⎦⎥ + ⎢ ∑ wij x j + I i ⎥ − λ z iφ ( s i , u i , xi − I 0 ) (16) n ∂EHop dyi ∂E y =− = − i + ∑ wij x j + Ii and , the relationship are given by dt ∂ xi ∂xi τ j Hopfied. Applying Euler discretization, (12) can be rewritten as Where − yi (t + 1) = (1 − Δt τ ⎡ n ⎤ ⎢⎣ j ≠i ⎥⎦ ) yi (t ) + Δt ⎢∑ wij x j (t ) + Ii ⎥ − Δtλ zi (t )φ (si (t ), ui (t ), xi (t ) − I 0 ) (17) where Δt is the time step.

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