Wahid Ahmed

Original Contribution: On the training of radial basis function classifiers

An approach for the implementation of the Radial Basis Function (RBF) technique is presented and applied to a network of the appropriate architecture. The paper explores a methodology for selecting kernel function parameters and the extent to which the number of RBF nodes can be reduced without significantly affecting the overall training error. These objectives are accomplished through algorithms that shall be described in detail. Emphasis is also placed on the problems faced by a technique that has been proved superior to the more traditional training algorithms, particularly in terms of processing speed and solvability of nonlinear patterns. Solutions are consequently proposed in view of making RBF a more efficient method for interpolation and classification purposes.

A probabilistic model for evaluation of neural network classifiers

Abstract A technique for evaluation of the generalization ability in artificial neural network (ANN) classifiers is presented. A probabilistic input model is proposed to account for all possible input ranges. The expected value of a square error function over the defined input range is taken as a measure of generalization ability. The minimization of the error function outlines the boundary of the decision region for a minimum error neural network (MENN) classifier. Two essential elements for carrying out the proposed technique are the estimation of the input density and numerical integration. A non-parametric method is used to locally estimate the distribution around each training pattern. The Monte Carlo method has been used for numerical integration. The evaluation technique was tested for measuring the generalization ability of back propagation (BP), radial basis function (RBF), probabilistic neural network (PNN) and MENN classifiers for different problems.

Adaptive detection of small sinusoidal signals in non-Gaussian noise using an RBF neural network

This paper addresses the application of locally optimum (LO) signal detection techniques to environments in which the noise density is not known a priori. For small signal levels, the LO detection rule is shown to involve a nonlinearity which depends on the noise density. The estimation of the noise density is a major part of the computational burden of LO detection rules. In this paper, adaptive estimation of the noise density is implemented using a radial basis function neural network. Unlike existing algorithms, the present technique places few assumptions on the properties of the noise, and performs well under a wide variety of circumstances. Experimental results are shown which illustrate the system performance as a variety of noise densities are encountered.

A minimum error neural network (MNN)

Abstract A minimum error neural network (MNN) model is presented and applied to a network of the appropriate architecture. The associated one-pass learning rule involves the estimation of input densities. This is accomplished by utilizing local Gaussian functions. A major distinction between this network and other Gaussian based estimators is in the selection of covariance matrices. In MNN, every single local function has its own covariance matrix. The Gram-Schmidt orthogonalization process is used to obtain these matrices. In comparison with the well known probabilistic neural network (PNN), the proposed network has shown improved performance.

Measurement of random sample time jitter for ADCs

This paper addresses the measurement of random sample-time jitter in the characterization of ADCs. A straightforward test is developed which allows for measurement of both additive noise power and RMS sample-time jitter. Simulations are used to assess the accuracy of the technique. Experimental results are also given for a commercially available ADC.

Improving the performance of probabilistic neural networks

A methodology for selection of appropriate widths or covariance matrices of the Gaussian functions in implementations of PNN (probabilistic neural network) classifiers is presented. The Gram-Schmidt orthogonalization process is employed to find these matrices. It has been shown that the proposed technique improves the generalization ability of the PNN classifiers over the standard approach. The result can be applied to other Gaussian-based classifiers such as the radial basis functions.<<ETX>>

Application of fast orthogonal search for the design of RBFNN

This paper addresses the use of a fast orthogonalization process to find the nodes of an RBF network which produce the best match to a target function. Several applications of RBF networks using this fast orthogonal search technique have been investigated and a classification problem is presented. The problem involves classification of human chromosomes, which is a highly complicated 30 dimensional and 24 class problem. Experimental results show that the fast orthogonal search technique not only outperforms the traditional technique, but it also uses much less time and effort.

On the implementation of RBF technique in neural networks

An approach for the implementation of the Radial Basis Function (RBF) technique is presented and applied to a net work of the appropriate architecture. The paper explores the extent to which the number of RBF nodes can be reduced without significantly affecting the overall training error. This is accomplished through an effective clustering algorithm that shall be described in detail. Emphasis is also placed on the problems faced by a technique that has been proved superior to the more traditional training algorithms, particularly in terms of processing speed and solvability of nonlinear patterns. Solutions are consequently proposed in view of making RBF a more efficient method for interpolation and classification purposes.

Adaptive locally optimal detection using RBF neural network

This paper addresses the application of locally optimum (LO) signal detection techniques to environments in which the noise density is not known a priori. For small signal levels, the LO detection rule is shown to involve a nonlinearity which depends on the noise density. The estimation of the noise density is a major part of the computational burden of LO detection rules. In this paper, adaptive estimation of the noise density is implemented using a radial basis function (RBF) neural network. Unlike existing algorithms, the present technique places few assumptions on the properties of the noise, and performs well under a wide variety of circumstances. Experimental results are shown which illustrate the system performance as a variety of noise densities are encountered.<<ETX>>

Adaptive RBF neural network in signal detection

This paper addresses the application of locally optimum (LO) signal detection techniques to environments in which the noise density is not known a-priori. For small signal levels, the LO detection rule is shown to involve a nonlinearity which depends on the noise density. The estimation of the noise density is a major part of the computational burden of LO detection rules. In this paper, adaptive estimation of the noise density is implemented using a radial basis function neural network. The technique places few assumptions on the properties of the noise, and performs well under a wide variety of circumstances. Experimental results illustrate the system performance as a variety of noise densities are encountered.<<ETX>>