Based on radial basis function neural network (RBF NN),the paper proposed a new algorithm for strip shape recognition. Compared with back propagation (BP) algorithm and improved least squares method (LSM), RBF NN shows excellent overall performance, such as learning speed, recognition precision and anti-interference capability. Copyright©2005IFAC Keyword: Strip Shape, Pattern Recognition, RBF,B...

This paper proposes a new approach to real time economic and emission dispatch by using orthogonal least-squares (OLS) and modified particle swarm optimization (MPSO) algorithms to construct the radial basis function (RBF) network. The objectives considered are fuel cost and NOx/CO2 emissions. The RBF network is composed of input, hidden, and output layers. The OLS algorithm provides a simple a...

tthe uncertainty estimation and compensation are challenging problems for the robust control of robot manipulators which are complex systems. this paper presents a novel decentralized model-free robust controller for electrically driven robot manipulators. as a novelty, the proposed controller employs a simple gaussian radial-basis-function network as an uncertainty estimator. the proposed netw...

The circuit-field coupled model is very accurate but it is computationally inefficient in studying the output performance of brushless dc motors. In order to resolve the problem, an estimation strategy based on an integrated radial basis function (RBF) network is proposed in this paper. The strategy introduces new conceptions of the network group that are being realized by three steps, namely: ...

In this paper we propose a strategy to shape adaptive radial basis functions through potential functions. DYPOF (DYnamic POtential Functions) neural network (NN) is designed based on radial basis functions (RBF) NN with a two-stage training procedure. Static (fixed number of RBF) and dynamic (ability to add or delete one or more RBF) versions of our learning algorithm are introduced. We investi...

Ratio pulse arithmetic represents a signal with a random pulse stream and its value is defined as the ratio of ones and zeroes in its random pulse stream. We propose circuits for the exponential function and subtraction, and explain how to implement a radial basis function(RBF) network. We applied the RBF network to a classifier example. Simulation results show that its performance is comparabl...

introduction: with using multiple linear regression (mlr), can simultaneously analyses several different variables, but to get the desirable results from the mlr, the samples must be much and accurate. therefore, this method has high sensitivity and may cause errors in results. in addition, to use this method, the variable must have normal distribution and modification follow from a linear rela...

Function approximation has been found in many applications. The radial basis function (RBF) network is one approach which has shown a great promise in this sort of problems because of its faster learning capacity. A traditional RBF network takes Gaussian functions as its basis functions and adopts the least-squares criterion as the objective function, However, it still suffers from two major pr...

In this contribution, one and two-stage neural networks methods for face recognition are presented. For two-stage systems, the Kohonen self-organizing map is used as a feature extractor and multiplayer perceptron (MLP) or radial basis function (RBF) network are used as classifiers. The results of such recognition are compared with face recognition using a one-stage multilayer perceptron and rad...

Among the neural network models RBF(Radial Basis Function) network seems to be quite effective for a pattern recognition task such as handwritten numeral recognition since it is extremely flexible to accommodate various and minute variations in data. Recently we obtained a good recognition rate for handwritten numerals by using an RBF network. In this paper we show how to design an RBF network ...