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...

large water distribution systems can be highly vulnerable to penetration of contaminant factors caused by different means including deliberate contamination injections. as contaminants quickly spread into a water distribution network, rapid characterization of the pollution source has a high measure of importance for early warning assessment and disaster management. in this paper, a methodology...

This paper introduces Locally Recurrent Probabilistic Neural Networks (LRPNN) as an extension of the well-known Probabilistic Neural Networks (PNN). A LRPNN, in contrast to a PNN, is sensitive to the context in which events occur, and therefore, identification of time or spatial correlations is attainable. Besides the definition of the LRPNN architecture a fast three-step training method is pro...

To improve speaker verification performance, we extend the wellknown Probabilistic Neural Networks (PNN) to Locally Recurrent Probabilistic Neural Networks (LRPNN). In contrast to PNNs that possess no feedbacks, LRPNNs incorporate internal connections to the past outputs of all recurrent neurons, which render them sensitive to the context in which events occur. Thus, LRPNNs are capable of ident...

Large water distribution systems can be highly vulnerable to penetration of contaminant factors caused by different means including deliberate contamination injections. As contaminants quickly spread into a water distribution network, rapid characterization of the pollution source has a high measure of importance for early warning assessment and disaster management. In this paper, a methodology...

Because of their good generalization properties, Probabilistic Neural Networks (PNNs) were chosen as classifiers for the Speaker Verification system presented here. Their design is straightforward and does not depend on the training, and they are built only for a fraction of the back propagation ANNs training time [1]. The PNNs need much more neurons, compared to back propagation ANNs, which le...

This research effort deals with the application of Artificial Neural Networks (ANNs) in order to help the diagnosis of cases with an orthopaedic disease, namely osteoporosis. Probabilistic Neural Networks (PNNs) and Learning Vector Quantization (LVQ) ANNs, were developed for the estimation of osteoporosis risk. PNNs and LVQ ANNs are both feed-forward networks; however they are diversified in te...

The aim of this paper is to investigate the performance of time delay neural networks (TDNNs) and the probabilistic neural networks (PNNs) trained with nonlinear features (Lyapunov exponents and Entropy) on electroencephalogram signals (EEG) in a specific pathological state. For this purpose, two types of EEG signals (normal and partial epilepsy) are analyzed. To evaluate the performance of the...

This paper proposes a system to support diagnosis for quantitative evaluation of motility function based on finger tapping movements using probabilistic neural networks (PNNs). Finger tapping movements are measured using magnetic sensors and evaluated by computing 11 indices. These indices are standardized based on those of normal subjects, and are then input to PNNs to assess motility function...

Two classes of dynamical recurrent neural networks, Continuous Time Recurrent Neural Networks (CTRNNs) (Yamauchi and Beer, 1994) and Plastic Neural Networks (PNNs) (Floreano and Urzelai, 2000) are compared on two behavioral tasks aimed at exploring their capabilities to display reinforcement-learning like behaviors and adaptation to unpredictable environmental changes. The networks report simil...