This paper proposes a universal entertainment interface for operation of amusement machines, such as video game machines and radio control toys. In the proposed interface system, biological signals are used as input, where users can choose some specific biological signal and configuration of signal measurement in accordance with their preference, physical condition (disabled or not), and degree...

Automation of power system fault identification using information conveyed by the wavelet analysis of power system transients is proposed. Probabilistic Neural Network (PNN) for detecting the type of fault is used. The work presented in this paper is focused on identification of simple power system faults. Wavelet Transform (WT) of the transient disturbance caused as a result of occurrence of f...

In recent years, many attempts have been made to predict the behavior of bonds, currencies, stocks, or stock markets. In this paper, the StandardlkPoors 500 Index is modeled using different neural network classification architectures. Most previous experiments used multilayer perceptrons for stock market forecasting. In this paper, a multilayer perceptron architecture and ZL probabilistic neura...

In this paper we discuss the use of the Probabilistic Neural Network (PNN) for the classification of the defects detected via the Remote Field Eddy Current (RFEC) inspection technique. The neural network is employed in order to associate each defect to one of the predefined classes. Each defect is represented by means of the phase response of the probe system. The reported results show that the...

A text-independent Probabilistic Neural Network (PNN)-based speaker verification system, referred to as WCL-1, is presented. A modular structure, with a distinct PNN for each enrolled speaker, is employed. A gender-dependent universal background model is created to represent the world of possible impostor speakers. Beside portrayal of the WCL-1 system, a comprehensive description of the results...

A technique for parameterising the macromodels of analogue circuit blocks under fault conditions is described. The technique uses a Robust Heteroscedastic Probabilistic Neural Network to classify simulation data. A large reduction in the number of fault classes can be obtained. The classification process is fast and the macromodels generated are accurate.