By Andries P. Engelbrecht

ISBN-10: 0470848707

ISBN-13: 9780470848708

ISBN-10: 0585459665

ISBN-13: 9780585459660

Computational intelligence is the research of adaptive mechanisms to allow or facilitate clever behaviour in complicated and altering environments. As such, computational intelligence combines man made neural networks, evolutionary computing, swarm intelligence and fuzzy platforms.

This publication offers a hugely readable and systematic creation to the basics of computational intelligence. In-depth remedies of the extra very important and most often used strategies also are given. The booklet offers therapy of computational intelligence in a fashion which permits the reader to simply enforce different options, and to use those recommendations to unravel real-world, complicated difficulties.

Key positive aspects contain:

  • A balanced remedy of the several computational intelligence paradigms
  • Inclusion of swarm intelligence
  • insurance of the latest advancements in computational intelligence
  • entire algorithms awarded in pseudo-code to permit implementation in any language
  • contains various workouts to contain and stimulate the reader extra

The cautious and thought of method of this key topic makes this publication acceptable for either the first-time reader, in addition to members already lively within the box.

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Justify your answer. 8. What are the advantages of using the hyperbolic tangent activation function instead of the sigmoid activation function? This page intentionally left blank Chapter 3 Single neurons have limitations in the type of functions they can learn. A single neuron (implementing a SU) can be used to realize linearly separable functions only. As soon as functions that are not linearly separable need to be learned, a layered network of neurons is required. Training these layered networks is more complex than training a single neuron, and training can be supervised, unsupervised or through reinforcement.

3 CHAPTER 2. THE ARTIFICIAL NEURON Widrow-Hoff Learning Rule For the Widrow-Hoff learning rule [Widrow 1987], assume that f = netp. 16) The Widrow-Hoff learning rule, also referred to as the least-means-square (LMS) algorithm, was one of the first algorithms used to train layered neural networks with multiple adaptive linear neurons. This network was commonly referred to as the Madaline [Widrow 1987, Widrow and Lehr 1990]. 4 Generalized Delta Learning Rule The generalized delta learning rule is a generalization of the Widrow-Hoff learning rule which assumes differentiable activation functions.

4. 2(d) for 0 = 0): f AN (net -6)= The sigmoid function is a continuous version of the ramp function, with f A N (net) G (0,1). The parameter A controls the steepness of the function. Usually, A = 1. 5. 2(e) for 0 = 0): e(net-0) _ e-(net-B) fAN(net -0) = or also defined as f A N (net6) = The output of the hyperbolic tangent is in the range (—1,1). 6. 9) where net — 0 is the mean and a1 the variance of the Gaussian distribution. 3 Artificial Neuron Geometry Single neurons can be used to realize linearly separable functions without any error.

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