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

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

Language: English

Pages: 492

ISBN: 1447150120

Format: PDF / Kindle (mobi) / ePub


This clearly-structured, classroom-tested textbook/reference presents a methodical introduction to the field of CI. Providing an authoritative insight into all that is necessary for the successful application of CI methods, the book describes fundamental concepts and their practical implementations, and explains the theoretical background underpinning proposed solutions to common problems. Only a basic knowledge of mathematics is required. Features: provides electronic supplementary material at an associated website, including module descriptions, lecture slides, exercises with solutions, and software tools; contains numerous examples and definitions throughout the text; presents self-contained discussions on artificial neural networks, evolutionary algorithms, fuzzy systems and Bayesian networks; covers the latest approaches, including ant colony optimization and probabilistic graphical models; written by a team of highly-regarded experts in CI, with extensive experience in both academia and industry.

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slopes where the membership degrees increase and decrease are always 1 and −1, respectively. However, it is reasonable to allow a scaling of the standard metric so that other forms of fuzzy sets as extensional hulls can result. This scaling can have two different meanings. The degree of similarity of two measured values depends on the measuring unit. Two values measured in kilo-units can have a small distance and might be considered as almost indistinguishable or very similar, while the very

Christian Borgelt2, Frank Klawonn3, Christian Moewes1, Matthias Steinbrecher4 and Pascal Held1 (1)Faculty of Computer Science, Otto-von-Guericke University Magdeburg, Magdeburg, Germany (2)Intelligent Data Analysis & Graphical Models Research Unit, European Centre for Soft Computing, Mieres, Spain (3)FB Informatik, Ostfalia University of Applied Sciences, Wolfenbüttel, Germany (4)SAP Innovation Center, Potsdam, Germany Abstract This chapter introduces required theoretical concepts for the

process knowledge are still a research topic. For certain types of problems, techniques that are inspired by natural or biological processes proved successful (Brownlee 2011). These approaches signify a paradigm change away from symbolic representations and towards inference strategies for adaptation and learning. Among such methods we find artificial neural networks, evolutionary algorithms and fuzzy systems (Engelbrecht 2007; Mumford and Jain 2009). These novel methods have demonstrated their

the reference vectors have been initialized with random weights from the interval [−0.5,0.5]. Due to the randomness of the initialization, the (relative) position of the reference vectors is completely independent of the (relative) position of the output neurons, so that no grid structure can be discerned. Fig. 7.8Unfolding of a self-organizing map trained with random patterns from the square [−1,1]×[−1,1] (indicated by the frames). The lines connect the reference vectors of neighboring neurons

stop the process in order to retrieve a final solution. Such a criterion may be, for example, that the algorithm is terminated (1) after a user-specified number of generations have been created, (2) there has been no improvement (of the best solution candidate) for a user-specified number of generations, or (3) a user-specified minimum solution quality has been obtained. To complete the specification of an evolutionary algorithm, we have to choose the values of several parameters, which include,

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