Download e-book for kindle: Computational Intelligence: A Methodological Introduction by Rudolf Kruse, Christian Borgelt, Frank Klawonn, Christian

By Rudolf Kruse, Christian Borgelt, Frank Klawonn, Christian Moewes, Matthias Steinbrecher, Pascal Held

This clearly-structured, classroom-tested textbook/reference offers a methodical advent to the sphere of CI. offering an authoritative perception into all that's priceless for the winning program of CI equipment, the ebook describes basic innovations and their sensible implementations, and explains the theoretical historical past underpinning proposed suggestions to universal difficulties. just a easy wisdom of arithmetic is needed. positive factors: presents digital supplementary fabric at an linked web site, together with module descriptions, lecture slides, routines with recommendations, and software program instruments; comprises a variety of examples and definitions in the course of the textual content; provides self-contained discussions on man made neural networks, evolutionary algorithms, fuzzy platforms and Bayesian networks; covers the newest methods, together with ant colony optimization and probabilistic graphical types; written by way of a crew of highly-regarded specialists in CI, with wide event in either academia and industry.

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Extra resources for Computational Intelligence: A Methodological Introduction

Example text

Inuvn , which correspond to the outputs outv1 , . . , outvn of the predecessors of the neuron u, and the connection weights wuv1 , . . , wuvn . This computation can be influenced by additional parameters σ1 , . . , σl (see, for instance, Sect. 5). From the network input, a certain number of parameters θ1 , . . , θk , and possibly a feedback of the current activation (u) of the neuron u (see, for instance, Chap. 9) the activation function fact computes the (u) new activation actu of the neuron u.

We obtain ⎛ ⎞ 2 −2 0 ⎜2 2 0⎟ 2 0 2 0 ⎟ W1 = ⎜ ⎝ 0 2 2 ⎠ and W2 = 0 2 0 2 , 0 −2 2 where the matrix W1 represents the connections from the input layer to the hidden layer and the matrix W2 the connections from the hidden layer to the output layer. Note that in these matrices zero elements correspond to missing/absent connections. With the help of the matrix notation of the weights it is easy to show why sigmoid or generally non-linear activation functions are decisive for the computational capabilities of a multi-layer perceptron.

3), which is closely related to the logistic function. In addition, it should be clear that any unipolar sigmoid function can be turned into a bipolar one by simply multiplying it by 2 and subtracting 1. Using bipolar sigmoid activation functions does not cause any fundamental differences. In this book, we therefore confine ourselves to unipolar sigmoid activation functions. All considerations and derivations of the subsequent sections can easily be transferred to bipolar functions. The strictly layered structure of a multi-layer perceptron and the special network input function of the hidden as well as the output neurons suggest to describe the network structure with the help of a weight matrix, as already discussed in Chap.

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