In this article the neural network speciication language EpsiloNN is presented. From an abstract speciication that is independent of the target computer architecture, a simulation source program for a workstation or a parallel computer can be generated. Neurocomputers requiring xed-point data types and arithmetic are supported too. The language design is based on an uniied neural network model ...

This paper introduces a neural network approach to the problem of ore grade estimation. The system under consideration consists of three neural network modules each responsible for a different area of the deposit, depending on the sampling density. Octant and quadrant search is used as a way of presenting input patterns to the modules. Both radial basis function networks and multi-layered perce...

In this report we will review some density problems in multivariate approximation. Some of these problems are related to approximation by radial basis functions and ridge functions. Others are motivated by various mathematical models in the theory of (artificial) neural networks and computerized tomography. §

Abstract In this paper, a trajectory tracking control for a nonholonomic mobile robot subjected to kinematic disturbances is proposed. A variable structure controller based on the sliding mode theory is used, and applied to compensate these disturbances. To minimize the problems found in practical implementations of the classical variable structure controllers, and eliminate the chattering phen...

This paper presents a novel approach for classifying the sales data using neural networks, whose result may be helpful in making sales data analysis and optimizing the sales. Radial Basis Function neural networks are widely used for classification problems with multi-class attributes because of their gradient-descent feature. Our objective is to classify the sales data into three classes: high ...

In this paper we present an extensive comparison between several feedforward neural network types in the context of a clinical diagnostic task, namely the detection of coronary artery disease (CAD) using planar thallium-201 dipyridamole stress-redistribution scintigrams. We introduce results from well-known (e.g. multilayer perceptrons or MLPs, and radial basis function networks or RBFNs) as we...

Since its emergence from the early 1980s, the field of Human Computer Interaction has moved on and advanced in many significant ways. It has opened up a world in which communication between human and computer has become easier and richer. Among the different modes of interaction, Gestures provide the most natural and convenient way of communication. Hence gesture recognition has been extensivel...

The application of artificial neural network to compressor performance map prediction is investigated. Different types of artificial neural networks such as general regression neural network, rotated general regression neural network proposed by the authors, radial basis function network, and multilayer perceptron network are considered. Two different models are utilized in simulating the perfo...

The most common transfer functions in neural networks are of the sigmoidal type. In this article other transfer functions are considered. Advantages of simple gaussians, giving hyperelliptical densities, and gaussian bar functions (sums of one-dimensional gaussians) are discussed. Bi-radial functions are formed from products of two sigmoids. Product of M bi-radial functions in N -dimensional pa...

Prediction of the production rate of the cutting dimensional stone process is crucial, especially when chain saw machines are used. The cutting dimensional rock process is generally a complex issue with numerous effective factors including variable and unreliable conditions of the rocks and cutting machines. The Group Method of Data Handling (GMDH) type of neural network and Radial Basis Functi...