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This paper proposes a novel steam valving controller using radial basis function (RBF) networks-based approximate model method. Approximate model method is a kind of direct linearization approach that is derived based on the approximation of the plant's input-output model via Taylor expansion. RBF networks are used to identify the plant to implement the approximate model control law. In order to improve the performance of the approximate model controller, RBF networks weights are adjusted online using BP algorithms with an adaptive learning rate. Several simulations results demonstrate the effectiveness of the proposed controller for team valving control. [ABSTRACT FROM AUTHOR]

The focus in this paper embraces the hybrid models whose components are nonparametric regression and artificial neural networks. Smoothing spline, regression spline and additive regression models are considered as the nonparametric regression components. Furthermore, various multilayer perceptron algorithms and radial basis function network model are regarded as the artificial neural networks components. The performances of these models are compared by forecasting three real Turkish data sets: Domestic product per capita (GDP), the number of cars produced and the number of tourist arrivals. The results obtained by experimental evaluations show that hybrid models proposed in this paper have performed much better in comparison to hybrid models discussed in literature. [ABSTRACT FROM AUTHOR]

In classical training methods for node open fault, we need to consider many potential faulty networks. When the multinode fault situation is considered, the space of potential faulty networks is very large. Hence, the objective function and the corresponding learning algorithm would be computationally complicated. This paper uses the Kuliback--Leibler divergence to define an objective function for improving the fault tolerance of radial basis function (RBF) networks. With the assumption that there is a Gaussian distributed noise term in the output data, a regularizer in the objective function is identified. Finally, the corresponding learning algorithm is developed. In our approach, the objective function and the learning algorithm are computationally simple. Compared with some conventional approaches, including weight-decay-based regularizers, our approach has a better fault-tolerant ability. Besides, our empirical study shows that our approach can improve the generalization ability of a fault-free RBF network. [ABSTRACT FROM AUTHOR]

Abstract: Neural networks have been successfully used to model nonlinear dynamic systems. However, when a static neural network model is used in system fault detection and the model prediction error is used as the residual, the residual is insensitive to the fault if the neural network used is in dependent mode. This paper proposed the use of a radial basis function network in independent mode as the system model for fault detection, and it was found that the residual is sensitive to the fault. To enhance the signal to noise ratio of the detection the recursive orthogonal least squares algorithm is employed to train the network weights. Another radial basis function network is used to isolate fault using the information in the residual signal. The developed method is applied to a benchmark simulation model of the proton exchange membrane fuel cell stacks developed at the Michigan University. One component fault, one actuator fault and three sensor faults were simulated on the benchmark model. The simulation results show that the developed approach is able to detect and isolate the faults to a fault size of ±10% of nominal values. These results are promising and indicate the potential of the method to be applied to the real world of fuel cell stacks for dynamic monitoring and reliable operations. [Copyright &y& Elsevier]

Abstract: In this paper, a novel self-organizing radial basis function (SORBF) neural network is proposed for nonlinear identification and modeling. The proposed SORBF consists of simultaneous network construction and parameter optimization. It offers two important advantages. First, the hidden neurons in the SORBF neural network can be added or removed, based on the neuron activity and mutual information (MI), to achieve the appropriate network complexity and maintain overall computational efficiency for identification and modeling. Second, the model performance can be significantly improved through the parameter optimization. The proposed parameter-adjustment-based optimization algorithm, utilizing the forward-only computation (FOC) algorithm instead of the traditionally forward-and-backward computation, simplifies neural network training, and thereby significantly reduces computational complexity. Additionally, the convergence of the SORBF is analyzed in both the structure organizing process phase and the phase following the modification. Lastly, the proposed approach is applied to model and identify the nonlinear dynamical systems. Simulation results demonstrate its effectiveness. [Copyright &y& Elsevier]

Abstract: This paper presents a new adaptive critic controller to achieve precise position-tracking performance of induction motors using a radial basis function neural network (RBFNN). The adaptive controller consists of an associative search network (ASN), an adaptive critic network (ACN), a feedback controller and a robust controller. Due to the mechanical parameter drift, unmodelled dynamics, actuator saturation, and external disturbances, the exact model of an induction motor is difficult to be obtained. The ASN, which can approximate nonlinear functions, is employed to develop an RBFNN-based feedback control law to deal with the unknown dynamics. The ACN receives a reward from credit-assignment unit to generate an internal reinforcement signal to tune the ASN. Due to the inevitable approximation errors and uncertainties, a robust control technique is developed to reject the effects of the uncertainties. Moreover, the weight updating laws with projection algorithm can tune all parameters of the RBFNN and ensure the localized learning capability. By Lyapunov theory, the stability of the closed-loop system can be guaranteed. In addition, the effectiveness of the proposed RBFNN-based induction motor controller is verified by experimental results. [Copyright &y& Elsevier]

In this letter, we develop the wavelet basis function neural networks (WBFNNs). It is analogous to radial basis function neural networks (RBFNNs) and to wavelet neural networks (WNN5). In WBFNNs, both the scaling function and the wavelet function of a multiresolution approximation (MRA) are adopted as the basis for approximating functions. A sequential learning algorithm for WBFNNs is presented and compared to the sequential learning algorithm of RBFNNs. Experimental results show that WBFNNs have better generalization property and require shorter training time than RBFNNs. [ABSTRACT FROM AUTHOR]