Subana Shanmuganathan, Sandhya Samarasinghe's Artificial Neural Network Modelling PDF

By Subana Shanmuganathan, Sandhya Samarasinghe

ISBN-10: 3319284932

ISBN-13: 9783319284934

ISBN-10: 3319284959

ISBN-13: 9783319284958

This e-book covers theoretical facets in addition to contemporary leading edge functions of man-made Neural networks (ANNs) in usual, environmental, organic, social, commercial and automatic systems.

It offers contemporary result of ANNs in modelling small, huge and complicated platforms lower 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 fiscal Systems. The booklet goals at serving undergraduates, postgraduates and researchers in ANN computational modelling.

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S. Touretzky (1990), pp. 598–605 6. B. G. J. Wolff, Optimal brain surgeon and general network pruning. IEEE International Conference on Neural Networks, vol. 1, (San Francisco, 1992), pp. 293–298 7. B. G. Stork, Second-order derivatives for network pruning: Optimal brain surgeon, in Advances in Neural Information Processing Systems, vol. 5, ed. by C. Lee Giles, S. J. D. Cowan, (1993), pp. 164–171 8. P. Engelbrecht, A new pruning heuristic based on variance analysis of sensitivity information. IEEE Trans.

Proceedings of the International Modelling and Simulation Congress (MODSM) (2011) 28. S. Samarasinghe, Hydrocomplexity: New Tools for Solving Wicked Water Problems Hydrocomplexité: Nouveaux outils pour solutionner des problèmes de l’eau complexes (IAHS Publ. 338) (2010) Artificial Neural Networks as Models of Robustness in Development and Regeneration: Stability of Memory During Morphological Remodeling Jennifer Hammelman, Daniel Lobo and Michael Levin Abstract Artificial neural networks are both a well-established tool in machine learning and a mathematical model of distributed information processing.

Neural Networks 12(6), 1386–1399 (2001) Order in the Black Box: Consistency and Robustness … 43 9. K. Hagiwara, Regularization learning, early stopping and biased estimator. Neurocomputing 48, 937–955 (2002) 10. M. Hagiwara, Removal of hidden units and weights for backpropagation networks. Proc. Int. Joint Conf. Neural Networks 1, 351–354 (1993) 11. F. Aires, Neural network uncertainty assessment using Bayesian statistics with application to remote sensing: 1. Network weights. J. Geophys. Res. 109, D10303 (2004).

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Artificial Neural Network Modelling by Subana Shanmuganathan, Sandhya Samarasinghe

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