Abstract
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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Gabryel, M., Woźniak, M., K. Nowicki, R. (2012). Creating Learning Sets for Control Systems Using an Evolutionary Method. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Swarm and Evolutionary Computation. EC SIDE 2012 2012. Lecture Notes in Computer Science, vol 7269. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29353-5_24
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DOI: https://doi.org/10.1007/978-3-642-29353-5_24
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