Computational Intelligence: A Methodological Introduction by Frank Klawonn, Christian Borgelt, Matthias Steinbrecher,

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

Computational intelligence (CI) contains a diversity of nature-inspired equipment that convey clever habit in complicated environments.

This clearly-structured, classroom-tested textbook/reference offers a methodical advent to the sphere of CI. offering an authoritative perception into all that's beneficial for the winning program of CI tools, the e-book describes basic ideas and their useful implementations, and explains the theoretical heritage underpinning proposed ideas to universal difficulties. just a uncomplicated wisdom of arithmetic is required.

Topics and features:
* offers digital supplementary fabric at an linked web site, together with module descriptions, lecture slides, workouts with suggestions, and software program tools
* includes a number of examples and definitions in the course of the text
* offers self-contained discussions on man made neural networks, evolutionary algorithms, fuzzy platforms and Bayesian networks
* Covers the most recent ways, together with ant colony optimization and probabilistic graphical models
* Written by way of a group of highly-regarded specialists in CI, with broad event in either academia and industry

Students of laptop technology will locate the textual content a must-read reference for classes on man made intelligence and clever structures. The booklet can also be a great self-study source for researchers and practitioners fascinated with all components of CI.

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Additional resources for Computational Intelligence: A Methodological Introduction (Texts in Computer Science)

Example text

Wn , θ), which states how well, for given weights and threshold, the computed function coincides with the desired one. Our objective is, of course, to determine the weights and the threshold in such a way that the error vanishes, that is, that the error function becomes 0. To achieve this, we try to reduce the value of the error function in every step. We illustrate this procedure with the help of a very simply example, namely a threshold logic unit with only one input. The parameters of this unit are to be 24 3 Threshold Logic Units Fig.

Un )⊤ is again a bias vector. 2 Function Approximation 53 which can be simplified to outU2 = A12 · outU1 + b12 , with an n × m matrix A12 and an n-dimensional vector b12 . Analogously, we obtain for the computations of outputs of the neurons of a layer U3 , which follows layer U2 , from the outputs of the neurons of layer U2 outU3 = A23 · outU2 + b23 , and therefore for computing the outputs of the neurons of layer U3 from the outputs of the neurons of layer U1 outU3 = A13 · outU1 + b13 , where A13 = A23 · A12 and b13 = A23 · b12 + b23 .

Un } be the neurons of two layers of a multilayer perceptron, where U2 may follow U1 . We construct an n × m matrix ⎛ ⎞ wu 1 v1 w u 1 v2 . . w u 1 vm ⎜ w u 2 v1 w u 2 v2 . . w u 2 vm ⎟ ⎜ ⎟ W=⎜ . .. ⎟ . ⎝ . . ⎠ w u n v1 w u n v2 ... wu n v m of the weights of the connections between these two layers, setting wui vj = 0 if there is no connection between neuron vj and neuron ui . The advantage of such a matrix is that it allows us to write the network input of the neurons of the layer U2 as netU2 = W · inU2 = W · outU1 where netU2 = (netu1 , .

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