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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 advanced environments.

This clearly-structured, classroom-tested textbook/reference provides a methodical advent to the sector of CI. delivering an authoritative perception into all that's worthwhile for the profitable software of CI equipment, the booklet describes basic ideas and their useful implementations, and explains the theoretical historical past underpinning proposed ideas to universal difficulties. just a easy wisdom of arithmetic is required.

Topics and features:
* offers digital supplementary fabric at an linked web site, together with module descriptions, lecture slides, routines with ideas, and software program tools
* comprises quite a few examples and definitions in the course of the text
* provides self-contained discussions on man made neural networks, evolutionary algorithms, fuzzy structures and Bayesian networks
* Covers the most recent methods, together with ant colony optimization and probabilistic graphical models
* Written via a workforce of highly-regarded specialists in CI, with vast adventure in either academia and industry

Students of desktop technological know-how will locate the textual content a must-read reference for classes on synthetic intelligence and clever platforms. The ebook can be an awesome self-study source for researchers and practitioners fascinated with all parts of CI.

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

Example text

An alternative way of obtaining these adaptation rules are the following considerations: if a threshold logic unit produces an output of 1 instead of a desired 0, then the threshold is too small and/or the weights are too large. Hence, we should increase the threshold a bit and reduce the weights. Of course, the latter is reasonable only if the corresponding input is 1, as otherwise the weight has no influence on the output. 5 Training the Parameters 27 Fig. 18 Turning the threshold into a weight the weights are too small.

16 on page 26 the initial values are θ = 32 and w = 3. It is easy to check that the online training shown here in tabular form corresponds exactly to the one shown graphically in Fig. 16 on the left. 3 shows batch training. It corresponds exactly to the procedure depicted in Fig. 16 in the middle or on the right. Again, the fully trained threshold logic unit with the same parameters is depicted, together with its geometric interpretation, in Fig. 19. As another example, we consider a threshold logic unit with two inputs that is to be trained in such a way that it computes the conjunction of its inputs.

Principles of Neurodynamics. E. L. McClelland, eds. Parallel Distributed Processing: Explorations in the Microstructures of Cognition, Vol. E. E. J. Williams. Learning Internal Representations by Error Propagation. E. E. J. Williams. Learning Representations by Back-Propagating Errors. D. Wasserman. Neural Computing: Theory and Practice. J. Werbos. Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. D. O. Widner. Single State Logic. AIEE Fall General Meeting, 1960.

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