Advances in neural networks - ISNN 2008 5th International by Fuchun Sun, Jianwei Zhang, Jinde Cao, Wen Yu PDF

By Fuchun Sun, Jianwei Zhang, Jinde Cao, Wen Yu

ISBN-10: 3540877312

ISBN-13: 9783540877318

The quantity set LNCS 5263/5264 constitutes the refereed court cases of the fifth foreign Symposium on Neural Networks, ISNN 2008, held in Beijing, China in September 2008.

The 192 revised papers provided have been rigorously reviewed and chosen from a complete of 522 submissions. The papers are prepared in topical sections on computational neuroscience; cognitive technology; mathematical modeling of neural structures; balance and nonlinear research; feedforward and fuzzy neural networks; probabilistic tools; supervised studying; unsupervised studying; help vector computer and kernel tools; hybrid optimisation algorithms; laptop studying and knowledge mining; clever keep an eye on and robotics; development acceptance; audio snapshot processinc and desktop imaginative and prescient; fault analysis; functions and implementations; functions of neural networks in digital engineering; mobile neural networks and complicated keep an eye on with neural networks; nature encouraged  tools of high-dimensional discrete information research; trend acceptance and data processing utilizing neural networks.

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Extra resources for Advances in neural networks - ISNN 2008 5th International Symposium on Neural Networks, ISNN 2008, Beijing, China, September 24-28, 2008: proceedings

Example text

NM rearranges the elements of X to form the matrix X(d) ∈ RNd ×Nd+1 Nd+2 ···NM N1 ···Nd−1 , which is the ensemble of vectors in RNd obtained by keeping index nd fixed and varying the other indices. Matricizing a tensor is similar to vectoring a matrix. The PARAFAC model was suggested independently by Carroll and Chang[11] under the name CANDECOMP(canonical decomposition) and by Harshman[12] under the name PARAFAC(parallel factor analysis) which has gained increasing attention in the data mining field.

The frequency selectivity of peripheral auditory system such as basilar membrane is simulated by a bank of cochlear filters, which have an impulse response in the following form: gi (t) = ai tn−1 e2πbi ERB(fi )t cos(2πfi t + φi ), (1 ≤ i ≤ N ), (13) 16 Q. Wu, L. Zhang, and G. Shi where n is the order of the filters, N is the number of filterbanks. For the ith filter bank, fi is the center frequency, ERB(fi ) is the equivalent rectangular bandwidth (ERB) of the auditory filter, φi is the phase, and ai , bi ∈ R are constants where bi determines the rate of decay of the impulse response, which is related to bandwidth.

Biol. Psychol. 52, 95–111 (2000) 7. : The Late Positive Components of Visual Evoked Responses Sensitive to Emotional Factors. Act. Nerv. Super. (suppl. 3), 334 (1982) 8. : P300 and the Emotional States Studied by Psycho Physiological Methods. Int. J. Psychophysiol. 7, 364–366 (1989) 9. : Event-related Potentials of Emotional Memory: Encoding Pleasant, Unpleasant, and Neutral Pictures. Cogn. Affect. Behav. Neurosci. 2, 252–263 (2002) 10. : Cognitive and biological determinants of P300: an integrative review.

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Advances in neural networks - ISNN 2008 5th International Symposium on Neural Networks, ISNN 2008, Beijing, China, September 24-28, 2008: proceedings by Fuchun Sun, Jianwei Zhang, Jinde Cao, Wen Yu


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