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28 July 1997 Comparative analysis of two evolved neural networks used for the identification and control of a nonlinear plant
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Abstract
This paper presents a comparative analysis of two evolved neural networks for control. Traditionally, the structure of Radial Basis Functions Networks (RBFNs) and Multilayer Feedforward Networks (MFNs) are found by a trial-and-error process. This process consists on finding an appropriate network structure such that the unknown nonlinearities of the plant can be estimated to some desired accuracy. In general, a neural network is composed of two elements: structural and learning parameters. The structural parameters are all those elements that determine the size of the network. The learning parameters are all those elements that determine learning and convergence of the network. The approach presented in this work uses a Genetic Algorithm (GA) to evolve the structure, and uses a gradient descent algorithm to adjust the weights in the network. An analysis of the evolution of RBFNs and MFNs by means of a GA is examined in detail. It is shown that the networks can be encoded in a chromosome for their evolution. Experimental results show the performance of Evolved Radial Basis Functions Networks and Evolved Multilayer Feedforward Networks in the identification and control of a nonlinear plant.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hector Erives and Wiley E. Thompson "Comparative analysis of two evolved neural networks used for the identification and control of a nonlinear plant", Proc. SPIE 3068, Signal Processing, Sensor Fusion, and Target Recognition VI, (28 July 1997); https://doi.org/10.1117/12.280816
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