A hybrid multimodel neural network for nonlinear systems identification
نویسندگان
چکیده
An improved universal parallel recurrent neural network canonical architecture, named Recurrent Trainable Neural Network (RTNN), suited for state-space systems identification, and an improved dynamic back-propagation method of its learning, are proposed. The proposed RTNN is studied with various representative examples and the results of its learning are compared with other results,, given in the literature. For a complex non-linear plants identification, a fuzzy-rule-based system and a fuzzyneural multimodel, are used. The fuzzy-neural multimodel is applied for mechanical system with friction identification.
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A Hybrid Multimode1 Neural Network for Nonlinear Systems Identification
An improved universal parallel recurrent neural network canonical architecture, named Recurrent Trainable Neural Network (RTNN), suited for state-space systems identification, and an improved dynamic back-propagation method of its leaming, are proposed. The proposed R T " is studied with various representative examples and the results of its learning are compared with other results,, given in t...
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