Dynamic Systems Identification and Control by Means of Complex-Valued Recurrent Neural Networks

The present article gives an extension of the real-valued recurrent neural network topology and its Back-Propagation (BP) learning to the complex-valued one. The BP learning is achieved by the use of diagrammatic rules to obtain the adjoint recurrent neural network topology aimed to propagate the output learning error through it so to learn the neural network weights. Then, this BP learning methodology is applied to the Recurrent Complex-Valued Neural Network (RCVNN) BP-learning using two type RCVNN topologies considering two different kinds of activation functions. After that, the second system identification scheme is incorporated in a total direct complex value control scheme of nonlinear oscillatory plants, introducing also an I-term. The total control scheme contained tree RCVNNs. Furthermore, comparative simulation results of one degree of freedom flexible-joint robot model illustrating system identification and control are obtained. The obtained comparative simulation results confirmed the good quality of the proposed control methodology.