The purpose of this article is to demonstrate the application of probabilistic neural networks (PNNs) as a classification tool in the slope stability estimation. PNNs are applied to estimate slope stability according to the slope geometric shapes and soil mechanical parameters. Unlike other neural network training paradigms, PNNs are characterized by high training speed and their ability to pro...

Neural network learning is the main essence of ANN. There are many problems associated with the multiple local minima in neural networks. Global optimization methods are capable of finding global optimal solution. In this paper we investigate and present a comparative study for the effects of probabilistic and deterministic global search method for artificial neural network using fully connecte...

Neural networks are a popular tool in the area of pattern recognition. However, since a very large number of neural network architectures exist, it has not been established which one is the most efficient. In this paper we compare the performance of three neural network architectures: Kohonen’s self-organizing network, probabilistic neural network, and a modified backpropagation applied to a si...

A study is presented to compare the performance of multilayer perceptrons, radial basis function networks, and probabilistic neural networks for classification. In many classification problems, probabilistic neural networks have outperformed other neural classifiers. Unfortunately, with this kind of networks, the number of required operations to classify one pattern directly depends on the numb...

This paper describes, and discusses Bayesian Neural Network (BNN). The paper showcases a few different applications of them for classification and regression problems. BNNs are comprised of a Probabilistic Model and a Neural Network. The intent of such a design is to combine the strengths of Neural Networks and Stochastic modeling. Neural Networks exhibit universal continuous function approxima...

---It was pointed out in this paper that the planar topology of current backpropagation neural network (BPNN) sets limits to solve the slow convergence rate problem, local minima, incapability of learning etc. problems associated with BPNN. The parallel probabilistic neural network (PPNN) using a new neural network topology---stereotopology----was proposed to overcome these problems. The learni...

In this paper a network comprising alternative branching nodes with probabilistic outcomes is considered. In other words, network nodes are probabilistic with exclusive-or receiver and exclusive-or emitter. First, an analytical approach is proposed to simplify the structure of network. Then, it is assumed that the duration of activities is positive trapezoidal fuzzy number (TFN). This paper com...

In this work, we investigate the effect of texture features for the classification of flower images. A flower image is segmented by eliminating the background using a thresholdbased method. The texture features, namely the color texture moments, gray-level co-occurrencematrix, and Gabor responses, are extracted, and combinations of these three are considered in the classification of flowers. In...

Several methods to identify plants have been proposed by several researchers. Commonly, the methods did not capture color information, because color was not recognized as an important aspect to the identification. In this research, shape and vein, color, and texture features were incorporated to classify a leaf. In this case, a neural network called Probabilistic Neural network (PNN) was used a...

This paper demonstrates classification of PQ events utilizing wavelet transform (WT) energy features by artificial neural network (ANN) and SVM classifiers. The proposed scheme utilizes wavelet based feature extraction to be used for the artificial neural networks in the classification. Six different PQ events are considered in this study. Three types of neural network classifiers such as feed ...