Abstract
Recently, there has been much emphasis on increasing the learning capability and structural flexibility of neural networks (NNs). Optimization or self-tuning is often required for designs, planning of actions, motions and tasks In most cases, the ordinary control theory can not be easily applied, specially for real-time application which is affected by uncertain parameters and environment factors. An effective method to overcome this problem is to find the optimal or suboptimal solution by defining cost function and using NNs with parallel learning, and on-line processing, together with flexibility in structure, to operate in a way to minimize the cost function. Self-tuning control algorithms lack an intelligent ability to choose time varying parameters. It has been shown that the application of optimal approaches makes effective utilization of NNs for sensory, recognitory, and forecasting capabilities necessary in the robotic control. There are many examples of applications of optimization problems in literature [1]–[3]. From this chapter, we describe several learning models for NNs as controllers.
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Teshnehlab, M., Watanabe, K. (1999). Self-Tuning PID Control. In: Intelligent Control Based on Flexible Neural Networks. International Series on Microprocessor-Based and Intelligent Systems Engineering, vol 19. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-9187-4_4
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DOI: https://doi.org/10.1007/978-94-015-9187-4_4
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