Titlebook: Computational Intelligence; A Methodological Int Rudolf Kruse,Christian Borgelt,Pascal Held Textbook 20131st edition Springer-Verlag London

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Nonrelativistic Stochastic Quantum Mechanicsnd process knowledge about the given problem setting. For certain types of problems, techniques inspired by natural or biological processes proved successful. These techniques belong to the field of computational intelligence. Our main objective with this textbook is to give a methodical introductio

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https://doi.org/10.1007/978-3-319-02609-1in this and the subsequent chapters to specific forms of (artificial) neural networks. We start with the best-known and most widely used form, the so-called . (MLP), which is closely related to the networks of threshold logic units we studied in a previous chapter. They exhibit a strictly layered st

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Springer Tracts in Advanced Roboticsf layers is always three, that is, there is exactly one hidden layer. In addition, radial basis function networks differ from multi-layer perceptrons in the network input and activation functions, especially in the hidden layer. In this hidden layer . are employed, which are responsible for the name

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Algorithms for Constrained Optimizationter, however, we lift all restrictions and consider recurrent networks without any constraints. Such general recurrent networks are well suited to represent . and to solve them (approximately) in a numerical fashion. If the type of differential equation is known that describes a given system, but th

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S. Bhatnagar,H. Prasad,L. Prashanthhich also includes, for example, particle swarm and ant colony optimization, which are inspired by other biological structures and processes, as well as classical methods like simulated annealing, which is inspired by a thermodynamical process. The core principle of evolutionary algorithms is to app

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