This paper develops a novel approach to incorporate the contributions of both quantitative validation metrics and qualitative subject matter expert (SME) evaluation criteria in model validation assessment. The relationship between validation metrics (input) and SME scores (output) is formulated as a classification problem, and a probabilistic neural network (PNN) is constructed to execute this ...

Fault diagnosis is essential for the reliable, safe, and efficient operation of the plant and for maintaining quality of the products in industrial system. This paper presents an ensemble fault diagnosis algorithm based on fuzzy c-means algorithm (FCM) with the Optimal Number of Clusters (ONC) and probabilistic neural network (PNN), called FCM-ONC-PNN. In clustering methods, the estimation of t...

Accurate identification of lithology is the basis and key process fine logging interpretation evaluation. However, reservoirs formed by different sedimentary environments tectonic movements generally have characteristics complex diverse strong heterogeneity, which brings great difficulty to reservoir lithology. This paper proposes an automatic technology for based on GWO-SVM algorithm model. Th...

Various criteria are proposed to select the number of hidden neurons in artificial neural network (ANN) models and based on the criterion evolved an intelligent ensemble neural network model is proposed to predict wind speed in renewable energy applications. The intelligent ensemble neural model based wind speed forecasting is designed by averaging the forecasted values from multiple neural net...

We report on a comparative evaluation of three evolutionary methods for training the probabilistic neural network (PNN). The specific focus here is on an investigation of the acceptable tradeoffs, in terms of accuracy vs. computational and memory demands, depending on the population size. An empirical evaluation is carried out on the well-known Parkinson Speech Dataset with Multiple Types of So...

This chapter describes a control method for a multi-joint robotic manipulator using Electromyogram (EMG) signals for operating a glovebox. The system uses a Probabilistic Neural Network (PNN) to estimate the user’s intended motion from EMG patterns, and generates a control command for the glovebox and robotic manipulator corresponding to the estimated motions. The user can therefore control the...

This study uses data mining techniques to examine the effect of various demographic, cognitive and psychographic factors on Egyptian citizens’ use of e-government services. Multi-layer perceptron neural network (MLP), probabilistic neural network (PNN), classification and regression trees (CART), and multivariate adaptive regression splines (MARS) are compared to a standard statistical method (...

In this paper, the texture classification problem is projected as a constraint satisfaction problem. The focus is on the use of a probabilistic neural network (PNN) for representing the distribution of feature vectors of each texture class in order to generate a feature-label interaction constraint. This distribution of features for each class is assumed as a Gaussian mixture model. The feature...

The majority of computer systems employ a login ID and password as the principal method for access security. In stand-alone situations, this level of security may be adequate, but when computers are connected to the internet, the vulnerability to a security breach is increased. In order to reduce vulnerability to attack, biometric solutions have been employed. In this paper, we investigate the ...

The optical character recognition (OCR) is the process of converting textual scanned image into a computer editable format. The proposed OCR system is for complex handwritten Kannada characters. One of the major challenges faced by Kannada OCR system is recognition of handwritten text from an image. The input text image is subjected to preprocessing and then converted into binary image. Segment...