By Subana Shanmuganathan, Sandhya Samarasinghe

ISBN-10: 3319284932

ISBN-13: 9783319284934

ISBN-10: 3319284959

ISBN-13: 9783319284958

This booklet covers theoretical facets in addition to fresh 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 complicated platforms 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 monetary Systems. The booklet goals at serving undergraduates, postgraduates and researchers in ANN computational modelling.

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Extra info for Artificial Neural Network Modelling

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These data demonstrate how artificial neural networks can be used as models for cognitive robustness and plasticity in developmental and regenerative biology. 1 Biological Relevance: Memory Is Retained During Remodeling It was once a long-standing central dogma of neurobiology that the central nervous system was incapable of accommodating neuron growth and death. This theory has since been overturned [7–9], which opens up a new field of study to understand how individual neurons and their environment contribute to the overall plasticity and growth of the brain [10, 11].

3 5. 50 40 4. 30 2 3. 1 2. 20 10 4 3 2 1 1. 1 2 3 4 5 6 7 8 9 10 11 12 13 Fig. 19 a Ward index against number of clusters; b SOM clustered into optimum number of 7 clusters: yellow (neurons 1, 6, 7), red (5, 14), brown (3, 9, 10), cyan (4, 13), pale blue (2, 11), dark blue (8, 15) and green (12) Order in the Black Box: Consistency and Robustness … 37 Fig. 1 Optimising a Network for Practical Real-Life Problems Breast Cancer Classification Correlation of weighted hidden neuron networks were also tested on a real world problem of breast cancer classification.

3 and 4 do not provide any clues as to the existence of an internally consistent pattern, we next explore hidden neuron activation. Activation yj for each neuron j is a nonlinear transformation of the weighted sum of inputs: yj ¼ f ða0j þ a1j xÞ ¼ 1 1 þ eÀða0j þ a1j xÞ ð2Þ The hidden neuron activations for the previous three cases of weight initialization are shown in Fig. 5 as a function of the input x. The Figure reveals an interesting effect. Although actual weights are not identical for the three cases, hidden neuron activations follow some identifiable patterns.

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Artificial Neural Network Modelling by Subana Shanmuganathan, Sandhya Samarasinghe


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