Probabilistic Neural Network approach used for mobile adhoc network is more efficient way to estimate the network security. In this paper, we are using an Adhoc On Demand Distance Vector (AODV) protocol based mobile adhoc network. In our Proposed Method we are considering the multiple characteristics of nodes. In this we use all the parameter that is necessary in AODV. For simulation purpose we...

This paper presents the application of radial basis neural networks to the development of a novel method for the condition monitoring and fault diagnosis of synchronous generators. In the proposed scheme, flux linkage analysis is used to reach a decision. Probabilistic neural network (PNN) and discrete wavelet transform (DWT) are used in design of fault diagnosis system. PNN as main part of thi...

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...

Probabilistic Neural Networks (PNN) typically learn more quickly than many neural network models and have had success on a variety of applications. However, in their basic form, they tend to have a large number of hidden nodes. One common solution to this problem is to keep only a randomly-selected subset of the original training data in building the network. This paper presents an algorithm ca...

In this paper, a wavelet-based neural network classifier for recognizing power quality disturbances is implemented and tested under various transient events. The discrete wavelet transform (DWT) technique is integrated with the probabilistic neural network (PNN) model to construct the classifier. First, the multi-resolution analysis (MRA) technique of DWT and the Parseval’s theorem are employed...

−This paper proposes a method for cardiac arrhythmias recognition using fractal transformation (FT) and neural network based classifier. Iterated function system (IFS) uses the non-linear interpolation in the map and FT with fractal dimension (FD) is used to construct various fractal patterns, including supra-ventricular ectopic beat, bundle branch ectopic beat, and ventricular ectopic beat. Pr...

Development of intelligent systems for classifying marine vessels based on their acoustic radiated noise is of major importance in the sonar systems. This paper focuses on three topics. The first topic is applying some modifications to the conventional Probabilistic Neural Network (PNN), as a common classifier in supervised pattern recognition, and suggesting a new configuration of PNN which we...

In this study, a neural network based methodology is proposed for power transmission line faults. The proposed method uses Probabilistic Neural Network (PNN) for classifying fault types and Resilient Propagation algorithm (RPROP) for detecting fault locations. Wavelet Transform is also proposed for feature selection and analysis. The hybrid system proposed in this study is tested using a simula...

In this paper we discuss the use of the Probabilistic Neural Network (PNN) for the classification of the defects detected via the Remote Field Eddy Current (RFEC) inspection technique. The neural network is employed in order to associate each defect to one of the predefined classes. Each defect is represented by means of the phase response of the probe system. The reported results show that the...