The identification of nonlinear systems by artificial neural networks has been successfully applied in many applications. In this context, the radial basis function neural network (RBF-NN) is a powerful approach for nonlinear identification. A RBF neural network has an input layer, a hidden layer and an output layer. The neurons in the hidden layer contain Gaussian transfer functions whose outp...

A radial basis function ( RBF ) neural network depends mainly upon an adequate choice of the number and positions of its basis function centers. In this paper we have proposed an algorithm for RBF neural network and the results may be reduced for artificial neural networks as particular cases.

This paper presents the performance comparison of two architectures of neural networks: multi-layer perceptron (MLP) neural networks and radial basis function (RBF) neural networks on face recognition system (FRS). We are training MLP using different variants of back-propagation (BP) algorithm. AT&T database has been used for performance comparison. The BP is gradient descent based iterative al...

This paper describes an application of Artificial Neural Networks (ANN) to Load Frequency Control (LFC) of nonlinear power systems. Power systems, such as other industrial processes, have parametric uncertainties that for controller design had to take the uncertainties in to account. For this reason, in the design of LFC controller the idea of robust control theories are being used. To improve ...

Noisy distance measurements are a pervasive problem in localization in wireless sensor networks. Neural networks are not commonly used in localization, however, our experiments in this paper indicate neural networks are a viable option for solving localization problems. In this paper we qualitatively compare the performance of three different families of neural networks: Multi-Layer Perceptron ...

Often, in real-world situations no actual data is available for training neural networks but the domain expert has a good idea of what to expect in terms of input and output parameter values. If the expert can express these relationships in the form of rules, this would provide a resource too valuable to ignore. Fuzzy logic is used to handle the imprecision and vagueness of natural language and...

this paper presents a comparison study between the multilayer perceptron (mlp) and radial basis function (rbf) neural networks with supervised learning and back propagation algorithm to track hand gestures. both networks have two output classes which are hand and face. skin is detected by a regional based algorithm in the image, and then networks are applied on video sequences frame by frame in...

Neural networks are still very important part of artificial intelligence. RBF networks seems to be more powerfull than that based on sigmoid function. Error Correction is second order training algorithm dedicated for RBF networks. The paper proposes method for improvement this algorithm by elimination of inconsistent patterns. The approach is also experimentally confirmed.

In this paper, we present a ‘smart’ neural control scheme for uncertain non-linear systems using the localized radical basis function (RBF) networks. This scheme is designed such that the current control action can utilize the knowledge that the NN learned from the past control process. Compared with most existing adaptive neural controllers, which are in general very-high-order dynamic control...