This paper discusses the use of artificial neural networks, trained with patterns extracted from trail data, as recommender systems. Feed-forward Multilayer-Perceptrons trained with the Backpropagation Algorithm were used to assign a rating to pairs of domains, based on the number of people that had traversed between them. The artificial neural network constructed in this project was capable of...

Welcome to SouthEastCon 2001! This years project will be the implementation of a parallel Feed-Forward Artificial Neural Network (FFANN) using first order gradient descent for the purpose of image recognition. This document contains information regarding the itinerary, software requirements, the parallel programming environment, evaluation guidelines, and background information on neural networ...

The aim of this paper is to develop the classification system using Artificial Neural Network for Electroencephalogram (EEG) signals. A good standard traditional method is to use Electroencephalogram for diagnosing patients brain functioning that corresponds to epilepsy and different brain disorders. This research focused on designing new classification techniques for single channel EEG recordi...

Investigation of soil properties like Cation Exchange Capacity (CEC) plays important roles in study of environmental reaserches as the spatial and temporal variability of this property have been led to development of indirect methods in estimation of this soil characteristic. Pedotransfer functions (PTFs) provide an alternative by estimating soil parameters from more readily available soil data...

Applications of multi-layer feed-forward artificial neural networks (ANN) to spectroscopy are reviewed. Network architecture and training algorithms are discussed. Backpropagation, the most commonly used training algorithm, is analyzed in greater detail. The following types of applications are considered: data reduction by means of neural networks, pattern recognition, multivariate regression, ...

Since artificial neural networks (ANNs) can approximate any function, they have been applied in many fields including hydrology. In hydrology, there are important issues such as flood estimation and predicting rainfall-runoff in a certain area. In this presentation, we briefly introduce a popular feed-forward neural network model, so called “multi-layer perceptron (MLP)”, and review its applica...

An artificial neural network (ANN) model is presented for the prediction of kinematic viscosity of binary mixtures of poly (ethylene glycol) (PEG) in water as a function of temperature, number-average molecular weight and mass fraction. Kinematic viscosities data of aqueous solutions for PEG (0.55419×10 – 9.875×10 m2/s) were obtained from the literature for a wide range of temperatures (277.15 ...

This paper proposes a novel approach for an optimal multi-objective optimization for VLSI implementation of Artificial Neural Network (ANN) which is area-power-speed efficient and has high degree of accuracy and dynamic range. A VLSI implementation of feed forward neural network in floating point arithmetic IEEE-754 single precision 32 bit format is presented that makes the use of digital weigh...

In this research, it is concerned with constructing software effort estimation model based on artificial neural networks. The model is designed accordingly to improve the performance of the network that suits to the COCOMO model. In this paper, it is proposed to use multi layer feed forward neural network to accommodate the model and its parameters to estimate software development effort. The n...