Two difficulties are involved with traditional RBF networks: the initial configuration of an RBF network needs to be determined by a trial-and-error method, and the performance suffers degradation when the desired locations of the center of the RBF are not suitable. A novel RBF network is proposed to overcome these difficulties. A new radial basis function is used for hidden nodes, and the numb...

A self tuning PID control strategy using reinforcement learning is proposed in this paper to deal with the control of wind energy conversion systems (WECS). Actor-Critic learning is used to tune PID parameters in an adaptive way by taking advantage of the model-free and on-line learning properties of reinforcement learning effectively. In order to reduce the demand of storage space and to impro...

We establish versions of Descartes' rule of signs for radial basis function (RBF) neural networks. The RBF rules of signs provide tight bounds for the number of zeros of univariate networks with certain parameter restrictions. Moreover, they can be used to infer that the Vapnik-Chervonenkis (VC) dimension and pseudodimension of these networks are no more than linear. This contrasts with previou...

. This paper devoted to an iris recognition system (IRS) designed using 2D-Discrete Cosine Transform (DCT) features and Self Organizing Map (SOM) and Radial Basis Function (RBF) which are an Artificial Neural Network (ANN) used as classifier. DCT is used for feature extraction to capture essential details. SOM and RBF are applied for classification with different functional paradigms. With resp...

In this paper, a novel hybrid Radial Basis Function Neural Network (RBF–NN) ensemble model is proposed for rainfall forecasting based on Kernel Partial Least Squares Regression (K–PLSR). In the process of ensemble modeling, the first stage the initial data set is divided into different training sets by used Bagging and Boosting technology. In the second stage, these training sets are input to t...

Abstract Aiming at the critical flutter problem of wind turbine blades, which is between classical and stall flutter, a suppression scheme based on radial basis function (RBF) neural network friction compensation backstepping presented. The structure model typical 2D section bending twist spring-mass-damper, rotor variable exciter second-order with disturbance incorporated to control blade. A m...

This work presents a methodology that relies on the application of radial basis functions network (RBF)-based feedback control algorithms to pharmaceutical crystallization process. Within scope model-based evaluation proposed strategy, firstly strategies for data treatment, structure and training methods reflecting possible scenarios in industry (Moving Window, Growing Window Golden Batch strat...

A RBF network was used as an empirical modeling tool. Results on simulated processes show that such a network can learn the shape of the function reasonably well with very limited experimental data. The use of the RBF network model is also illustrated with real experimental data from a soil extraction process.

Purpose This paper aimed a fractional-order sliding mode-based lateral lane-change control method that was proposed to improve the path-tracking accuracy of vehicle motion. Design/methodology/approach In this presighting and kinematic models were established, new mode isokinetic convergence law devised based on fractional order calculus make front wheel turning angle approach desired value quic...