By Dazhong Ma, Jinhai Liu, Zhanshan Wang (auth.), Jun Wang, Gary G. Yen, Marios M. Polycarpou (eds.)
The two-volume set LNCS 7367 and 7368 constitutes the refereed lawsuits of the ninth foreign Symposium on Neural Networks, ISNN 2012, held in Shenyang, China, in July 2012. The 147 revised complete papers offered have been rigorously reviewed and chosen from various submissions. The contributions are dependent in topical sections on mathematical modeling; neurodynamics; cognitive neuroscience; studying algorithms; optimization; development acceptance; imaginative and prescient; photo processing; info processing; neurocontrol; and novel applications.
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Extra info for Advances in Neural Networks – ISNN 2012: 9th International Symposium on Neural Networks, Shenyang, China, July 11-14, 2012. Proceedings, Part II
A reliability for each sample is calculated from each binary PELM model, and the sample is assigned to the class with the largest combined reliability by using the winner-takes-all strategy. 1 Binary of Extreme Learning Machine The ELM network is regarded as a special single-hidden layer network. The output of an ELM is L f (x) = β i G (ai , bi , x) = β ⋅ h ( x ) , (1) i =1 where h (x ) is the output vector for the hidden layer with respect to input x. The parameters for hidden layer nodes are randomly assigned and the output weight βi which connects the ith hidden node to the output nodes is then analytically determined.
Journal of Environmental Management 90(2), 772–778 (2009) 6. : Adaptive Fuzzy C-Means clustering in process monitoring. Chemometrics and Intelligent Laboratory Systems 45(1-2), 23–38 (1999) 7. : Support Vector Machines in Water Quality Management. Analytica Chimica Acta 703(2), 152–162 (2011) 8. : Application of Neural Networks to Water and Wastewater Treatment Plant Operation. ISA Transactions 31(1), 25–33 (1992) Multi-class Classification with One-Against-One Using Probabilistic Extreme Learning 19 9.
Gi ( yˆ ) = 1 SEPi 2π e 1 yˆ − yˆi 2 − ( ) 2 SEPi (5) Parameters of probability density function are estimated by nonlinear least squares. Suppose that the prior probabilities P(ω c ) = N c N and the conditional probabilistic densities p( y | ωc ) for c = 0,1 . For an unknown sample, the probability with prediction yˆu for the class ωc is given by the Bayes formula : Rc , k = P (ωc yˆu ) = p( yˆu ωc ) × P (ωc ) p( yˆu ) (6) Bayes formula shows that the prior probability p(ωc ) is converted into a posterior probability p(ωc yˆu ) by prediction yˆu .