forecasting of temperature is a very important in meteorology. air temperature prediction is of a concern in environment, industry and agriculture. temperature with precipitation are important factors in meteorology and are used in classification of climate. in this paper we want to predict average monthly temperature for chosen station of isfahan province. an artificial neural network is a pow...

The subject of this thesis is the original study of the application of the multi-layer perceptron architecture to channel equalization in digital communications systems. Both theoretical analyses and simulations were performed to explore the performance of the perceptron-based equalizer (including the decision feedback equalizer). Topics covered include the factors that affect performance of th...

PURPOSE To evaluate the performance of support vector machine, multi-layer perceptron and radial basis function neural network as auxiliary tools to identify keratoconus from Orbscan II maps. METHODS A total of 318 maps were selected and classified into four categories: normal (n = 172), astigmatism (n = 89), keratoconus (n = 46) and photorefractive keratectomy (n = 11). For each map, 11 attr...

The application of data mining and machine learning in directing clinical research into possible hidden knowledge is becoming greatly influencial in cancer research. This research presents a comparison of three data mining classification models: multi-layer perceptron neural networks, C4.5 decision trees and Naive Bayes. The classification models are built for breast cancer survivability predic...

A novel speech separation structure which simulates the cocktail party e ect using a modi ed iterative Wiener lter and a multi-layer perceptron neural network is presented. The neural network is used as a speaker recognition system to control the iterative Wiener lter. The neural network is a modi ed perceptron with a hidden layer using feature data extracted from LPC cepstral analysis. The pro...

The work presents the results of an investigation conducted to compare the performances of the Multi Layer Perceptron (MLP) and the Nearest Neighbor (NN) classifier for handwritten numeral recognition problem. The comparison is drawn in terms of the recognition performance and the computational requirements of the individual classifiers. The results show that a two-layer perceptron performs com...

Difficult non-linear problems can be mapped to a set of localised linear problems. A self-organising map (SOM) is used as a gating function to a localised mixture of experts classifier and is shown to find solutions equivalent to those learned by a multi-layer perceptron while retaining the simplicity and resilience of a single-layer perceptron. Modifications to the traditional softmax gate fun...

A hierarchical neural network model for the identiication of arbitrary contour shapes is presented. Tolerance towards translation, rotation and scaling is achieved far more cost-eeectively than for a fully connected multi-layer perceptron.

This study applied a prediction-based portfolio optimization model to explore the results of portfolio predicament in the Tehran Stock Exchange. To this aim, first, the data mining approach was used to predict the petroleum products and chemical industry using clustering stock market data. Then, some effective factors, such as crude oil price, exchange rate, global interest rate, gold price, an...

We extend the Bayesian framework to Multi-Layer Perceptron models of Non-linear Auto-Regressive time-series. The approach is evaluated on an artificial time-series and some common simplifications are discussed.