ESTIMATION OF CATALYTIC PERFORMANCE BY USING NEURAL NETWORKS

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In this investigation, the methods of promoting estimation reliability of neural network for the case of catalytic performance are experimentally discussed from the information science viewpoints. The problem of function value overshooting encountered in the process of learning with error back-propagation neural networks is computationally investigated for the specific case of two-variable nonlinear functions. The experimental results suggests that the allowance of as large as magnitude of calculated function values should be reserved between those values and the outputs from output-layer units in order to make that learning as complete as possible. It is also concluded that the difference in application manners of training data to input layer is not critical for learning performance of those neural networks.