Artificial neural networks (ANN) are widely used in diverse fields
of science and industry. Though there have been numerous techniques
used for their implementations, the choice of a specific
implementation is subjected to different factors including cost,
accuracy, processing speed and overall performance. Featured with
synaptic plasticity, the process of training is concerned with
adjusting the individual weights between each of the individual ANN
neurons until we can achieve close to the desired output. This book
introduces the common trajectory-driven and evolutionary-based ANN
training algorithms.
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