Sensitivity Property of Generalized CMAC Neural Network

Generalized CMAC (GCMAC) is a type of neural network known to be fast in learning. The network may be useful in structural engineering applications such as the identification and the control of structures. The derivatives of a trained GCMAC is relatively poor in accuracy. Therefore to improve the accuracy, a new algorithm is proposed. If GCMAC is directly differentiated, the accuracy of the derivative is not satisfactory. This is due to the quantization of input space and the shape of basis function used. Using the periodicity of the predicted output by GCMAC, the derivative can be improved to the extent of having almost no error. Numerical examples are considered to show the accuracy of the proposed algorithm.