yan et al. has pointed out that the combination of orcement learning and Sigmoid-based neural ork sometimes leads to instability of the learning. is paper, it is proposed that a Gauss-Sigmoid neural ork, in which continuous input signals are put into a oid-based neural network through a RBF network, ilized for reinforcement learning. It is confirmed simulation of the same task as in Boyan et al...

Radial Basis Function Neural Networks (RBFNNs) are well known because, among other applications, they present a good performance when approximating functions. The function approximation problem arises in the construction of a control system to optimize the process of the mineral reduction. In order to regulate the temperature of the ovens and other parameters, it is necessary a module to predic...

This paper focuses on the analysis of prediction of wind speed in the wind farms. The performance of modeling can be analyzed by using the real time data with different heights of the wind mill. Artificial Neural Network is used here to develop models for predicting wind speed in wind farms. The models are mainly based on back propagation neural network and radial basis function neural network....

In this paper we use a “uniformity” property of Riemann integration to obtain a single-hidden-layer neural network of fixed translates of a (not necessarily radial) basis function with a fixed “width” that approximates a (possibly infinite) set of target functions arbitrarily well in the supremum norm over a compact set. The conditions on the set of target functions are simple and intuitive: un...

This paper introduces a method for the recognition of human faces in 2-Dimensional digital images using a new Hybrid Learning Algorithm (HLA) for Radial Basis Function (RBF) neural network as classifier and Pseudo Zernike Moment Invariant (PZMI) as face feature. Also we evaluate the effect of orders of The PZMI on recognition rate, in the proposed technique. Simulation has been carried out on t...

A novel method based on shape and texture information is proposed in this paper for facial expression recognition from video sequences. The Discriminant Non-negative Matrix Factorization (DNMF) algorithm is applied at the image corresponding to the greatest intensity of the facial expression (last frame of the video sequence), extracting that way the texture information. A Support Vector Machin...

This paper presents a new method for on-board fault diagnosis for the air-path of spark ignition (SI) engines. The method uses an adaptive radial basis function (RBF) neural network to classify pre-defined possible faults from engine measurements to report the type and size of the fault. The RBF fault classifier adapts its widths and weights to model the time-varying dynamics of the engine and ...

We propose here a new evolutionary algorithm, the RBFGene algorithm, to optimize Radial Basis Function Neural Networks. Unlike other works on this subject, our algorithm can evolve both the structure and the numerical parameters of the network: it is able to evolve the number of neurons and their weights. The RBF-Gene algorithm’s behavior is shown on a simple toy problem, the 2D sine wave. Resu...

In this paper, the combination of RBF (Radial Basis Function) neural network and sliding mode control, which is used for vibration control, is examined. The approach is based on a sliding mode control methodology which drives the system towards a sliding surface by tuning the parameters of the controller using Gaussian radial basis function neural network. The input and output of RBF neural net...

Machine learning has been successfully applied to various fields of scientific computing in recent years. In this work, we propose a sparse radial basis function neural network method solve elliptic partial differential equations (PDEs) with multiscale coefficients. Inspired by the deep mixed residual method, rewrite second-order problem into first-order system and employ multiple networks (RBF...