Abstract
Adaptive and learning control systems normally require methods which search automatically for the set of parameters which optimize a performance index. Since environmental or other conditions may change in a learning situation, the optimization must occur continuously and adapt to the existing conditions. If the performance index is unimodal and noisy, but non-stationary, a combination of gradient and random methods yields a search method which exhibits the advantages of both the random and stochastic approximation methods. The overall rate of convergence is improved and under certain conditions can be optimized. In addition, the random component of such a search provides a capability for multimodal search applications.
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© 1971 Plenum Press, New York
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McMurtry, G.J. (1971). Adaptive Optimization in Learning Control. In: Fu, K.S. (eds) Pattern Recognition and Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-7566-5_16
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DOI: https://doi.org/10.1007/978-1-4615-7566-5_16
Publisher Name: Springer, Boston, MA
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