Image segmentation is one of the most important methods for extracting information of interest from remote sensing image data, but it still remains some problems, leading to low quality segmentation. The research focuses on image segmentation based on PNNs and MLPNs. It presents to construct a PNN model and tunes a satisfied PNN for hyper-spectral image segmentation. Furthermore, the paper give...

Introduction: Since human health is the issue of Medical Research, correct prediction of results is of a high importance. This study applies probabilistic neural network (PNN) for predicting coronary artery disease (CAD), because the PNN is stronger than other methods. Methods: In this descriptive-analytic study, The PNN method was implemented on 150 patients admitted to the Mazandaran Heart...

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...

A probabilistic neural network (PNN) is presented for predicting the magnitude of the largest earthquake in a pre-defined future time period in a seismic region using eight mathematically computed parameters known as seismicity indicators. The indicators considered are the time elapsed during a particular number (n) of significant seismic events before the month in question, the slope of the Gu...

In this paper, we introduce a new soft computing technique that dwells on the ideas of combining fuzzy rules in a fuzzy system with polynomial neural networks (PNN). The PNN is a flexible neural architecture whose structure is developed through the modeling process. Unfortunately, the PNN has a fatal drawback in that it cannot be constructed for nonlinear systems with only a small amount of inp...

The perineuronal net (PNN) surrounds neurons in the central nervous system and is thought to regulate developmental plasticity. A few studies have shown an involvement of the PNN in hippocampal plasticity and memory storage in adult animals. In addition to the hippocampus, plasticity in the medial prefrontal cortex (mPFC) has been demonstrated to be critical for the storage of long-term memory,...

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 ...