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In this paper, a novel dynamic recurrent functional link neural network (DRFLNN) is proposed for the identification of unknown dynamics of the nonlinear systems. The proposed structure contains a self-feedback loop(s) as well as the adjustable weighted feed-through of the input signals to the output neuron(s). A learning algorithm is developed using the combination of Lyapunov stability and dynamic back-propagation method and is applied to derive the stable parameter adjustment equations. The performance evaluation of the proposed DRFLNN model is done by comparing it with the multi-layer perceptron (consisting of a single hidden layer), radial basis function network, Elman recurrent neural network (ERNN), nonlinear auto-regressive moving average, and the conventional functional link neural network. Three benchmark systems have been used on which all these models are applied. From the results, it is found that ERNN provided better prediction accuracy as compared to the remaining models and the second-best accuracy is obtained from the proposed model. Further, the ERNN model is more complex and offers more parameters to be tuned as compared to the DRFLNN model. Thus, the training of the ERNN model is quite difficult as compared to the DRFLNN. [ABSTRACT FROM AUTHOR]

Radial basis function network (RBFN) is used in this paper for predefined trajectory control of both one-link and two-link robotic manipulators. The updating equations for the RBFN parameters were derived using the gradient descent principle. The other advantage of using this principle is that it shows the clustering effect in distributing the radial centres. To increase the complexity, the dynamics of robotic manipulator is assumed to be unknown, and hence, simultaneous control and identification steps were performed using the RBFNs. The performance of the RBFN is compared with the multilayer feed-forward neural network (MLFFNN) in terms of mean square error, tolerance to disturbance and parameter variations in the system. The efficacy of RBFN as a controller and identification tool is verified by performing the simulation study, and the results obtained reveal the superior performance of RBFN over MLFFNN in both identification and control aspects for one-link and two-link robotic manipulators. [ABSTRACT FROM AUTHOR]