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

Computational intelligence (CI) incorporates a diversity of nature-inspired tools that express clever habit in complicated environments.

This clearly-structured, classroom-tested textbook/reference provides a methodical advent to the sector of CI. offering an authoritative perception into all that's priceless for the profitable program of CI equipment, the ebook describes basic options and their functional implementations, and explains the theoretical historical past 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 ideas, and software program tools
* includes a variety of examples and definitions during the text
* provides self-contained discussions on man made neural networks, evolutionary algorithms, fuzzy platforms and Bayesian networks
* Covers the newest ways, together with ant colony optimization and probabilistic graphical models
* Written via a staff of highly-regarded specialists in CI, with wide event in either academia and industry

Students of computing device technology will locate the textual content a must-read reference for classes on man made intelligence and clever structures. The ebook is usually an amazing self-study source for researchers and practitioners excited about all components of CI.

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

Sample text

The left diagram shows the error for the input x = 0, for which an output of 1 is desired. Since the threshold logic unit computes a 1 if xw ≥ θ , the error is 0 for a negative threshold and 1 for a positive threshold. ) The middle diagram shows the error for the input x = 1, for which an output of 0 is desired. Here both the weight and the threshold have an influence. If the weights are less than the threshold, we have xw < θ and thus the output and consequently the error is 0. The diagram on the right shows the sum of these individual errors.

Extun ) and an output vector o (l) = (ov1 , . . , ovm ). If we are given a fixed learning task, we desire to train a neural network in such a way that it produces for all training patterns l ∈ Lfixed the outputs contained in the output vector o (l) if the external inputs of the corresponding input vector i (l) are fed into the network. In practice, this optimum can rarely be achieved and thus one may have to accept a partial or approximate solution. In order to determine how well a neural network solves a fixed learning task, an error function is employed, which measures how well the actual outputs coincide with the desired outputs in the training patterns.

3 Training Neural Networks One of the most enticing properties of (artificial) neural networks is the possibility to train them for certain tasks with the help of example data. To some degree, we already considered this possibility in the preceding chapter with the help of the delta rule. Although the delta rule is only applicable for single threshold logic units and cannot be transferred to networks directly, it already illustrates the basic principle: training a neural network consists in adapting the connection weights and possibly some other parameters (like thresholds) such that a certain criterion is optimized.

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