Polish (Poland)English (United Kingdom)

Creating Learning Sets for Control Systems Using an Evolutionary Method

Type of Publication: In Book
Year: 2012
Editor: Rutkowski, Leszek and Korytkowski, Marcin and Scherer, Rafal and Tadeusiewicz, Ryszard and Zadeh, Lotfi A. and Zurada, Jacek M.
Volume: 7269
Pages: 206-213
Publisher: Springer Berlin Heidelberg
Address: Berlin, Heidelberg
Series: Lecture Notes in Computer Science
ISBN: 978-3-642-29353-5
The acquisition of the knowledge which is useful for developing of artificial intelligence systems is still a problem. We usually ask experts, apply historical data or reap the results of mensuration from a real simulation of the object. In the paper we propose a new algorithm to generate a representative training set. The algorithm is based on analytical or discrete model of the object with applied the k–nn and genetic algorithms. In this paper it is presented the control case of the issue illustrated by well known truck backer–upper problem. The obtained training set can be used for training many AI systems such as neural networks, fuzzy and neuro–fuzzy architectures and k–nn systems.

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