In this paper we propose a novel approach for feature subset selection by the Polynomial Neural Network (PNN) using Genetic Algorithm (GA). A randomly selected subset of features of a d ataset is passed to the PNN as input. The classification accuracy of PNN is taken as the fitness function of GA. In the conventional PNN approaches, published in literature so far, the processing by PNN takes la...

The parallel-type neuron network (PNN) is researched to improve on the decrease in capabilities of the neuron network by the interference of the learning caused between the outputs of BP network (BPN) of two outputs or more and the difficulty of the common achievement of the middle layer used for each output. The research to compare prediction accuracies of nonlinear time series signals predict...

In order to realize the fault diagnosis of the polyvinyl chloride (PVC) polymerization kettle reactor, a rough set (RS)–probabilistic neural networks (PNN) fault diagnosis strategy is proposed. Firstly, through analysing the technique of the PVC polymerization reactor, the mapping between the polymerization process data and the fault modes is established. Then, the rough set theory is used to t...

The pairwise nearest neighbor (PNN) method, also known as Ward's method belongs to the class of agglomerative clustering methods. The PNN method generates hierarchical clustering using a sequence of merge operations until the desired number of clusters is obtained. This method selects the cluster pair to be merged so that it increases the given objective function value least. The main drawback ...

A novel technique for speaker independent automated speech recognition is proposed. We take a segment model approach to Automated Speech Recognition (ASR), considering the trajectory of an utterance in vector space, then classify using a modified Probabilistic Neural Network (PNN) and maximum likelihood rule. The system performs favourably with established techniques. Our system achieves in exc...

In this paper, we propose a new method for face detection from cluttered images. We use a polynomial neural network (PNN) for separation of face and non-face patterns while the complexity of the PNN is reduced by principal component analysis (PCA). In face detection, the PNN is used to classify sliding windows in multiple scales and label the windows that contain a face. The PNN is shown to be ...

In this paper, a novel and simple iris feature extraction technique is proposed for iris recognition of high performance. We use one dimensional circular ring to represent iris features. The reduced and significant features afterward are extracted by Sobel operator and 1D wavelet transform. So as to improve the accuracy, this paper combines Probabilistic Neural Network (PNN) and Particle Swarm ...

Dependency of explicit costs index of mining opening on its parameters is modeled. In addition, the problem of structural identification is solved. Models enumeration is realized with the help of group methods of data handling (GMDH). Least-squares and least-modules methods are used for evaluating model parameters. The quality of resulting models is evaluated by the following criteria: (a) rema...

This paper presents transient stability assessment of electrical power system using probabilistic neural network (PNN) and principle component analysis. Transient stability of a power system is first determined based on the generator relative rotor angles obtained from time domain simulation outputs. Simulations were carried out on the IEEE 9-bus test system considering three phase faults on th...

Probabilistic Neural Network has received considerable attention nowadays and obtained many successful application. This type of neural system has shown marvelous higher recognition capability compare with that of Back-Propagation neural system. However, this neural has shown some drawbacks, especially on determining the value of its smoothing parameter and its neural structure optimization whe...