Artificial Neural Network Modelling by Subana Shanmuganathan, Sandhya Samarasinghe

By Subana Shanmuganathan, Sandhya Samarasinghe

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

It provides contemporary result of ANNs in modelling small, huge and intricate platforms lower than 3 different types, particularly, 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 ebook goals at serving undergraduates, postgraduates and researchers in ANN computational modelling.

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In comparison to the original network (a), the addition of the neuron has improved performance. The neuron addition post-training is not universally beneficial, sometimes causing loss of learned patterning information. d Connection blocking between input neurons and hidden neurons (X to 3 and Y to 5) and hidden neurons and output neuron (3 to output) causes loss of memory of correct patterning information • Split—splitting a neuron involves modifying the weights of the original neuron a and new neuron b so that the two new neurons contain the same number of connections as the parent neuron, wa (4a, 4b).

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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. Neural Networks 12(6), 1386–1399 (2001) Order in the Black Box: Consistency and Robustness … 43 9.

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