AbstrAct In the field of pattern recognition, probabilistic neural networks (PNNs) have been proven as an important classifier. For pattern recognition of EMG signals, the characteristics usually used are: (1) amplitude , (2) frequency, and (3) space. However, significant temporal characteristic exists in the transient and non-stationary EMG signals, which cannot be considered by traditional PN...

In this paper, we propose to use probabilistic neural networks (PNNs) for classification of bacterial growth/no-growth data and modeling the probability of growth. The PNN approach combines both Bayes theorem of conditional probability and Parzen's method for estimating the probability density functions of the random variables. Unlike other neural network training paradigms, PNNs are characteri...

Plastic Neural Networks (PNNs) are known for their ability to adapt to environmental changes. It is generally believed that PNNs cannot solve timing tasks which require a predefined delay before execution of an action. In this study we investigate the ability of PNNs to solve timing tasks. Our experiments evolve PNNs to perform successfully on a task requiring the delayed execution of an action...

time series forecasting is an active research area that has drawn considerable attention for applications in a variety of areas. forecasting accuracy is one of the most important features of forecasting models. nowadays, despite the numerous time series forecasting models which have been proposed in several past decades, it is widely recognized that financial markets are extremely difficult to ...

In this paper we study the applicability of Probabilistic Neural Networks (PNNs) as core classifiers to medium scale speaker recognition over fixed telephone networks. In particular, banking applications with up to 400 enrolled speakers and short training times are targeted. Two PNN-based open-set text-independent systems for Speaker Identification and Speaker Verification correspondingly are p...

This paper proposes a new learning method for process neural networks (PNNs) based on the Gaussian mixture functions and particle swarm optimization (PSO), called PSO-LM. First, the weight functions of the PNNs are specified as the generalized Gaussian mixture functions (GGMFs). Second, a PSO algorithm is used to optimize the parameters, such as the order of GGMFs, the number of hidden neurons,...

Probabilistic Neural Networks (PNNs) constitute a promisingmethodology for classification and prediction tasks. Their performance depends heavily on several factors, such as their spread parameters, kernels, and prior probabilities. Recently, Evolutionary Bayesian PNNs were proposed to address this problem by incorporating Bayesian models for estimation of spread parameters, as well as Particle...

A well-known and widely used model for classification and prediction is the Probabilistic Neural Network (PNN). PNN’s performance is influenced by the kernels’ spread parameters so recently several approaches have been proposed to tackle this problem. The proposed approach is a combination of two well known methods applied to PNNs. First, it incorporates a Bayesian model for the estimation of P...

Artificial neural networks; Sentiment analysis; Text classification; Opinion mining; Support vector machine Abstract The aim of sentiment classification is to efficiently identify the emotions expressed in the form of text messages. Machine learning methods for sentiment classification have been extensively studied, due to their predominant classification performance. Recent studies suggest tha...

Transmission network expansion planning (TNEP) is a basic part of power network planning that determines where, when and how many new transmission lines should be added to the network. So, the TNEP is an optimizing problem in which the expansion purposes are optimized. The Artificial Intelligence (AI) tools such as Genetic Algorithm (GA), Simulated Annealing (SA), Tabu Search (TS), Artificial n...