By Subana Shanmuganathan, Sandhya Samarasinghe
This ebook covers theoretical facets in addition to contemporary cutting edge functions of synthetic Neural networks (ANNs) in common, environmental, organic, social, commercial and automatic systems.
It offers contemporary result of ANNs in modelling small, huge and complicated structures less than 3 different types, specifically, 1) Networks, constitution Optimisation, Robustness and Stochasticity 2) Advances in Modelling organic and Environmental Systems and three) Advances in Modelling Social and financial Systems. The ebook goals at serving undergraduates, postgraduates and researchers in ANN computational modelling.
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Network Jacobians. J. Geophys. Res. 109, D10305 (2004). 1029/ 2003JD004175 14. K. Warne, G. Prasad, S. Rezvani, L. Maguire, Statistical computational intelligence techniques for inferential model development: A comparative evaluation and novel proposition for fusion. Eng. Appl. Artif. Intell. 17, 871–885 (2004) 15. I. Rivals, L. Personnaz, Construction of Conﬁdence Intervals for neural networks based on least squares estimation. Neural Networks 13, 463–484 (2000) 16. J. C. Tan, C. Xiang, Estimating the number of hidden neurons in a feed forward network using the singular value decomposition IEEE Trans.
H. Lee, Geometrical interpretation and architecture selection of MLP, IEEE Trans. Neural Networks 16(1), (2005) 18. A. Castillo, J. J. Merelo, V. Rivas, G. Romero, A. Prieto, Evolving multilayer perceptrons. Neural Process. Lett. 12(2), 115–127 (2000) 19. X. Yao, Evolutionary artiﬁcial neural networks. Proc. IEEE 87(9), 1423–1447 (1999) 20. S. Samarasinghe, Optimum Structure of Feed Forward Neural Networks by SOM Clustering of Neuron Activations. Proceedings of the International Modelling and Simulation Congress (MODSM) (2007) 21.
The training algorithms employed and the difﬁculty of the pattern recognition problem tested are key factors determining the impact of perturbations. The results show that certain perturbations, such as neuron splitting and scaling, can achieve memory persistence by functional recovery of lost patterning information. The study of models integrating both growth and reduction, combined with distributed information processing is an essential ﬁrst step for a computational theory of pattern formation, plasticity, and robustness.