Get Artificial Neural Network Modelling PDF

Intelligence Semantics

By Subana Shanmuganathan, Sandhya Samarasinghe

ISBN-10: 3319284932

ISBN-13: 9783319284934

ISBN-10: 3319284959

ISBN-13: 9783319284958

This publication covers theoretical points in addition to fresh cutting 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, particularly, 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 e-book goals at serving undergraduates, postgraduates and researchers in ANN computational modelling.

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Given n inputs for the actual outputs x and expected outputs r, the equation calculates the mean difference between the expected and actual outputs over the input set (1). 2 ð1Þ Perturbation Methods We implemented and tested seven perturbation methods on artificial neural networks, falling under the categories of neuron removal, addition, and connection blocking. The merging and splitting techniques, used to implement cell proliferation and programmed cell death (apoptosis) were adapted from a neuron pruning and growing method in an iterative training algorithm [30].

Lobo Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA © Springer International Publishing Switzerland 2016 S. Shanmuganathan and S. 1007/978-3-319-28495-8_3 45 46 J. Hammelman et al. 1 Introduction The animal brain is widely considered to be the material substrate of memory. It may thus be expected that maintenance of complex memories requires stability of brain structure. Remarkably, several studies in a number of animal species revealed that this is not the case [1].

A notable feature of the commonly used pruning methods is that they optimize the structure iteratively and require a certain amount of heuristic judgment. In what follows, two pruning methods, Optimal Brain Damage (OBD) [5–7] and Variance Nullity measure (VN) [8] are implemented and compared with the proposed correlation method. 1 Network Pruning with Optimum Brain Damage (OBD) In OBD [5–7], weights that are not important for input-output mapping are found and removed. This is based on a saliency measure of a weight, as given in Eq.

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Artificial Neural Network Modelling by Subana Shanmuganathan, Sandhya Samarasinghe

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