Emerging Intelligent Computing Technology and Applications: by Chunhua Feng, Zhenkun Huang (auth.), De-Shuang Huang,

By Chunhua Feng, Zhenkun Huang (auth.), De-Shuang Huang, Phalguni Gupta, Xiang Zhang, Prashan Premaratne (eds.)

This e-book constitutes the refereed court cases of the eighth overseas convention on clever Computing, ICIC 2012, held in Huangshan, China, in July 2012. The 242 revised complete papers awarded within the 3 volumes LNCS 7389, LNAI 7390, and CCIS 304 have been conscientiously reviewed and chosen from 753 submissions. The papers during this quantity (CCIS 304) are geared up in topical sections on Neural Networks; Particle Swarm Optimization and area of interest know-how; Kernel equipment and assisting Vector Machines; Biology encouraged Computing and Optimization; wisdom Discovery and knowledge Mining; clever Computing in Bioinformatics; clever Computing in development acceptance; clever Computing in snapshot Processing; clever Computing in machine imaginative and prescient; clever regulate and Automation; wisdom Representation/Reasoning and professional platforms; Advances in info defense; Protein and Gene Bioinformatics; delicate Computing and Bio-Inspired Techiques in Real-World functions; Bio-Inspired Computing and Applications.

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Therefore, the objective function of MRNN is the combination of the sum of squared error and the sum of squares of the slopes of the classification function. Besides, we derived a more sophisticated formula similar to the traditional weight decay technique from the MRNN, establishing a more rigorous theoretical basis for the technique. This study employed several real application examples to test the MRNN. The results led to the following conclusions. (1) As long as the penalty coefficient was in the appropriate range, MRNN performed better than pure MLP.

1. Fig. 3. Fig. 2. Fig. 4. Acknowledgement. This work was supported by NNSF of China (10961005). References 1. : Existence and Stability of Equilibria of the ContinuousTime Hopfield Neural Network. J. Comput. Appl. Math. 169, 117–125 (2004) 2. : Delay-dependent Global Asymptotic Stability for Delayed Cellular Neural Networks. Commun. Nonlinear Sci. Numer. Simulat. 14, 1057–1063 (2009) 3. : Delay-dependent Global Stability Condition for Delayed Hopfield Neural Networks. Nonlinear Analysis, RWA 8, 997–1002 (2007) 4.

Neurocomputing 73, 991–1005 (2010) 13. : A Bifurcation Problem for A Functional Differential Equation of Finitely Retarded Type. J. Math. Anal. Appl. 35, 312–348 (1971) 14. : Oscillation Theory of Delay Differential Equations with Applications. tw 2 Abstract. To enhance the generalization of neural network model, we proposed a novel neural network, Minimum Risk Neural Networks (MRNN), whose principle is the combination of minimizing the sum of squares of error and maximizing the classification margin, based on the principle of structural risk minimization.

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