A systematic method for the diagnosis of planar antenna arrays from far field radiation pattern using neural networks is presented. Two types of neural networks, Radial basis function (RBF) and Probabilistic neural network (PNN) are considered for the performance comparison. Deviation pattern is used as input to the neural network to determine the location of the faulty element and error in exc...

The basic idea of boosting is to increase the pattern recognition accuracy by combining classifiers which have been derived from differently weighted versions of the original training data. It has been verified in practical experiments that the resulting classification performance can be improved by increasing the weights of misclassified training samples. However, in statistical pattern recogn...

This paper presents the application of Probabilistic Neural Networks (PNN) for the classification automatic detection of QRS-complexes in Electrocardiogram (ECG). Raw ECG signal contains the power line interference and baseline wander. This can be removed by using Digital filtering techniques. For the QRS-detection, Probabilistic Neural Networks is used as pattern classifier. MATLAB is applied ...

The striatum is the primary input nucleus of the basal ganglia, a collection of nuclei that play important roles in motor control and associative learning. We have previously reported that perineuronal nets (PNNs), aggregations of chondroitin-sulfate proteoglycans (CSPGs), form in the matrix compartment of the mouse striatum during the second postnatal week. This period overlaps with important ...

One of the most frequently used models for classification tasks is the Probabilistic Neural Network. Several improvements of the Probabilistic Neural Network have been proposed such as the Evolutionary Probabilistic Neural Network that employs the Particle Swarm Optimization stochastic algorithm for the proper selection of its spread (smoothing) parameters and the prior probabilities. To furthe...

One of the important challenges in Graphical models is the problem of dealing with the uncertainties in the problem. Among graphical networks, fuzzy cognitive map is only capable of modeling fuzzy uncertainty and the Bayesian network is only capable of modeling probabilistic uncertainty. In many real issues, we are faced with both fuzzy and probabilistic uncertainties. In these cases, the propo...

Perineuronal nets (PNNs) are distributed primarily around inhibitory interneurons in the hippocampus, such as parvalbumin-positive interneurons. PNNs are also present around excitatory neurons in some brain regions and prevent plasticity in these neurons. A recent study demonstrated that PNNs also exist around mouse hippocampal pyramidal cells, which are the principle type of excitatory neurons...

A general framework for hybrids of hidden Markov models (HMMs) and neural networks (NNs) called hidden neural networks (HNNs) is described. The article begins by reviewing standard HMMs and estimation by conditional maximum likelihood, which is used by the HNN. In the HNN, the usual HMM probability parameters are replaced by the outputs of state-specific neural networks. As opposed to many othe...

In this paper, a text-independent Probabilistic Neural Network (PNN)-based Speaker Verification system is presented. Modular structure with a distinct PNN for each enrolled speaker is used. A gender-dependent universal background model is built to represent the impostor speakers. A detailed description of the system, as well as the time required for training and processing all the test trials i...