On-line Radial Basis Function Research Articles

An online self-adaptive RBF network algorithm based on the Levenberg-Marquardt algorithm

ABSTRACT Aiming at the problem that the Levenberg-Marquardt (LM) algorithm can not train online radial basis function (RBF) neural network and the deficiency in the RBF network structure design methods, this paper proposes an online self-adaptive algorithm for constructing RBF neural network (OSA-RBFNN) based on LM algorithm. Thus, the ideas of the sliding window method and online structure optimization methods are adopted to solve the proposed problems. On the one hand, the sliding window method enables the RBF network to be trained online by the LM algorithm making the RBF network more robust to the changes in the learning parameters and faster convergence compared with the other investigated algorithms. On the other hand, online structure optimization can adjust the structure of the RBF network based on the information of training errors and hidden nodes to track the non-linear time-varying systems, which helps to maintain a compact network and satisfactory generalization ability. Finally, verified by simulation analysis, it is demonstrated that OSA-RBFNN exhibits a compact RBF network.

Artificial Intelligence and Neural Network-Based Shooting Accuracy Prediction Analysis in Basketball

In order to improve the accuracy of shooting in basketball. A shooting accuracy prediction method based on the convergent improved resource allocating network (CIRAN) online radial basis function neural network (RBFNN) is proposed, and the RBFNN learning algorithm is improved. Through the collection of shooting motion images, feature point extraction, and edge contour feature extraction, the shooting motion trajectory is obtained. Using the online neural network based on the CIRAN learning algorithm to predict the accuracy of shooting, this method analyzes the radial basis function (RBF) network. Based on the RBF analysis, the number of network layers and the number of hidden layer neurons are adjusted and optimized. In order to improve the prediction accuracy of shooting in basketball, a method based on. Through the analysis, it can be known that the accuracy of both the traditional RBFNN and the CIRAN-based online neural network for the prediction of shooting accuracy is above 70%. The prediction accuracy of the online neural network for shooting is higher than that of the traditional one. This is mainly because the online update function of the learning algorithm can better adjust the corresponding structure with the development of the game and has a better generalization ability. In addition, because the CIRAN learning algorithm introduces the hidden layer neuron deletion strategy, its network structure is simpler than that of the traditional one, the number of hidden layer neurons is less, and the running time required is less, which can better meet the real-time requirements and provide a more scientific method for basketball training.

Adaptive neural network control for nonlinear output-feedback systems under disturbances with unknown bounds

Abstract This paper is concerned with the problem of adaptive backstepping neural network tracking control for a class of output feedback systems with unknown functions under bounded disturbances whose boundaries is unknown. Unknown functions are approximated via online radial basis function (RBF) neural network, high order continuous differentiable functions are introduced into Lyapunov function to realize the estimation of unknown parameters and unknown boundary, and a new dead zone function is designed to replace symbolic function to realize the continuity of virtual control. During the design process, the backstepping design method is applied to deal with the cross terms generated by the tuning function. Barbalat’s lemma proves that all the signals of closed-loop system are bounded and the output tracking error converges to an arbitrarily small neighborhood of the origin. A simulation example are given to illustrate the effectiveness of the control scheme.

Online identification using radial basis function neural network coupled with KPCA

In this paper, we are going to propose an online radial basis function (RBF) neural network algorithm without any preprocessing step. Then a kernel principal component analysis (KPCA) is coupled with the proposed online RBF neural network algorithm. Indeed, the KPCA method is used as a preprocessing step to reduce the feature dimension which fed to the RBF neural network. Reducing memory requirements of the models makes RBF neural network training efficient and fast. These two proposed algorithms are applied, with success, for identification of a mobile robot position. The simulation results present that the used sigmoid function as a kernel, compared to other kernel functions, which gives an excellent model and a minimum mean square error.

A Neural Network Parallel Adaptive Controller for Fighter Aircraft Pitch-Rate Tracking

A fighter aircraft pitch-rate command-tracking controller based on a neural network parallel controller is proposed. The scheme consists of an online radial basis function neural network (RBFNN) in parallel with a model reference adaptive controller (MRAC) and uses a growing dynamic RBFNN to augment MRAC. Updating the RBFNN width, the center and weight characteristics are performed such that the error reduction and improved tracking accuracy are accomplished. The RBFNN architecture adapts its centers and radii and tunes the relevant parameters, dynamically addressing the issues related to initial error and dimensional growth inherent in static neural network design. The total control signal is used to change the elevator deflection, keeping the other control surface deflections at random values, even when the aircraft operates at different maneuvers. Moreover, a suitable reference model structure is used for all aircraft operating modes, and the system is then fully tuned by the parallel controller. The strength of the proposed scheme is in its ability to effectively perform, even when plant mode swings and functional changes occur. Theoretical results are validated by conducting simulation studies on a nonlinear F16 fighter aircraft model operating at different modes created by a randomly changing parameter set.

TEXT-INDEPENDENT SPEAKER VERIFICATION USING MINIMAL RESOURCE ALLOCATION NETWORKS

This paper presents a text-independent speaker verification system based on an online Radial Basis Function (RBF) network referred to as Minimal Resource Allocation Network (MRAN). MRAN is a sequential learning RBF, in which hidden neurons are added or removed as training progresses. LP-derived cepstral coefficients are used as feature vectors during training and verification phases. The performance of MRAN is compared with other well-known RBF and Elliptical Basis Function (EBF) based speaker verification methods in terms of error rates and computational complexity on a series of speaker verification experiments. The experiments use data from 258 speakers from the phonetically balancedcontinuous speech corpus TIMIT. The results show that MRAN produces comparable error rates to other methods with much less computational complexity.