In recent years the Probabilistic Neural Network (PPN) has been used in a large number of applications due to its simplicity and efficiency. PNN assigns the test data to the class with maximum likelihood compared with other classes. Likelihood of the test data to each training data is computed in the pattern layer through a kernel density estimation using a simple Bayesian rule. The kernel is u...

â â â â â â â  this paper proposes a new forecasting model for investigating relationship between the price of crude oil, as an important energy source and gdp of the us, as the largest oil consumer, and the uk, as the oil producer. gmdh neural network and mlff neural network approaches, which are both non-linear models, are employed to forecast gdp responses to the oil price changes. the resul...

Investment theory has as a major tenet the concept of efficient markets. In an efficient market all information is fully reflected in the price of a stock. As such, there is no trading strategy based on known data that can earn an abnormal profit. Using price and volume data, this design and subsequent model produces a weak form test of the efficient markets hypothesis. First, an Object-Oriente...

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...

This study comprises developing a more appropriate hybrid wavelet-modified GMDH model for forecasting the monthly crude palm oil (CPO) price of Malaysia. In proposed model, complex data CPO is decomposed into different sub series using discrete wavelet transform (DWT) and then it has been linked with modified model. Sigmoid, radial basis, tangent polynomial functions are selected as transfer in...

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,...