By Subana Shanmuganathan, Sandhya Samarasinghe
This booklet covers theoretical features in addition to fresh cutting edge functions of synthetic Neural networks (ANNs) in normal, environmental, organic, social, business and automatic systems.
It offers fresh result of ANNs in modelling small, huge and intricate platforms below 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 publication goals at serving undergraduates, postgraduates and researchers in ANN computational modelling.
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Additional resources for Artificial Neural Network Modelling
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).
8 reveal a pattern that is consistent. In each plot, there is one dominant weighted activation pattern with a negative slope. In Fig. 8b, c, the other (a) (b) y 10 8 6 4 2 -4 -2 Neu-2 Neu-3 Neu-4 2 4 x (c) y 2 Neu-1 -4 -2 -2 -4 -6 -8 2 4 x Neu-2 Neu-3 Neu-4 y 30 20 10 Neu-1 -4 -2 -10 -20 -30 2 4 x Neu-1 Neu-2 Neu-3 Neu-4 Fig. 8 Weighted hidden neuron activation and correlation matrices for the three random weight initializations for random sample 1: a Initialization 1, b Initialization 2, and c Initialization 3 Order in the Black Box: Consistency and Robustness … 25 three patterns are almost parallel to each other.
The normalized activation patterns for each network presented so far were mapped to a 2-dimensional SOM  (4 neuron map) and the most efﬁcient number of clusters was determined by the Ward clustering  of SOM neurons. Ward is an efﬁcient statistical clustering method suitable and effective for relatively small datasets. Figure 13 presents the results for only two networks, one with 4 neurons that was depicted as random weight initialization 2 in Fig. 8b and the other with 5 neurons presented in Fig.