Feed forward neural networks (FFNN) with an unconstrained random number of hidden neurons deene exible non-parametric regression models. In M uller and Rios Insua (1998) we have argued that variable architecture models with random size hidden layer signiicantly reduce posterior mul-timodality typical for posterior distributions in neural network models. In this chapter we review the model propo...

A new hybrid clustering algorithm based on a three-layer feed forward neural network (FFNN), a distribution density function, and a cluster validity index, is presented in this paper. In this algorithm, both feature weighting and sample weighting are considered, and an optimal cluster number is automatically determined by the cluster validity index. Feature weights are learnt via FFNN based on ...

Production of highly viscous tar sand bitumen using Steam Assisted Gravity Drainage (SAGD) with a pair of horizontal wells has advantages over conventional steam flooding. This paper explores the use of Artificial Neural Networks (ANNs) as an alternative to the traditional SAGD simulation approach. Feed forward, multi-layered neural network meta-models are trained through the Back-...

Patients age has been estimated in healthy population by means of the heart rate variability (HRV) parameters to assess the potentiality of HRV indexes as a biomarker of age. A long-term analysis of HRV has been performed, computing linear time and frequency domain parameters as well as non-linear metrics, in a dataset of 113 healthy subjects (age range 20-85 years old). The principal component...

The present paper estimates for the first time State of Charge (SoC) a high capacity grid-scale lithium-ion battery storage system used to improve power profile in distribution network. proposed long short-term memory (LSTM) neural network model can overcome problems associated with nonlinear and adapt complexity uncertainty estimation process. accuracy developed was compared results obtained f...

the main purpose of this paper is prediction of tse corporate financial bankruptcy using artificial neural networks. the mean values of key ratios reported in past bankruptcy studies were selected for neural network inputs (working capital to total assets, net income to total assets, total debt to total assets, current assets to current liabilities, quick assets to current liabilities). the neu...

In this paper we present a Feed-Foward Neural Networks Autoregressive (FFNN-AR) model with genetic algorithms training optimization in order to predict the gross domestic product growth of six countries. Specifically we propose a kind of weighted regression, which can be used for econometric purposes, where the initial inputs are multiplied by the neural networks final optimum weights from inpu...

life style and life expectancy of inhabitants have been affected by the increase of particulate matter 10 micrometers or less in diameter (pm10) in cities and this is why maximum pm10 concentrations have received extensive attention. an early notice system for pm10 concentrations necessitates an accurate forecasting of the pollutant. in the current study an artificial neural network was used t...

Handwriting recognition has been one of the active and challenging research areas in the field of image processing and pattern recognition. It has numerous applications that includes, reading aid for blind, bank cheques and conversion of any hand written document into structural text form. Neural Network (NN) with its inherent learning ability offers promising solutions for handwritten characte...