State Analysis of Discrete-time Singular Nonlinear Systems Using a Fuzzy Neural Network

Singular systems have been the subject of interest over the last two decades due to their many practical applications. On the other hand, there has been considerable interest in the application of intelligent technologies such as Artificial Neural Networks (ANN) and Fuzzy Logic in modeling complex phenomena, due to their innate nonlinear structures. This paper proposed an alternative neuro-fuzzy architecture for application to state analysis of singular nonlinear systems. The architecture employs an approximation to the fuzzy reasoning system to considerably reduce the dimension of the network as compared to similar approaches. Results not only demonstrate the advantages of the neuro-fuzzy approach, but it also highlight the advantages of the architecture for hardware realizations

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