To simulate the broiler growth the input variables were: day of year, vents opening, wind velocity, external temperature and absolute humidity, the maximum, average and minimum of the internal temperature and absolute humidity. For that purpose, two techniques were applied, a multi-layer perceptron (MLP) static Neural Network (NN) and the Layered Digital Dynamic Network (LDDN) which were applie...

The single-layer perceptron, introduced by Rosenblatt in 1958, is one of the earliest and simplest neural network models. However, it incapable classifying linearly inseparable patterns. A new era research started 1986, when backpropagation (BP) algorithm was rediscovered for training multilayer perceptron (MLP) model. An MLP with a large number hidden nodes can function as universal approximat...

This paper combines a conventional method of multivariablesystem identification with a dynamic multi-layer perceptron (MLP) toachieve a constructive method of nonlinear system identification. Theclassof nonlinear systems is assumed to operate nominally around anequilibrium point in the neighborhood of which a linearized model existsto represent the system, although norma...

In this paper, we present our recent progress on multi-layer perceptron (MLP) based data-driven feature extraction using improved MLP structures. Four-layer MLPs are used in this study. Different signal processing methods are applied before the input layer of the MLP. We show that the first hidden layer of a four-layer MLP is able to detect some basic patterns from the time-frequency plane. KLT...

In this paper, an empirical comparison of two multilayer perceptron (MLP)-based techniques for keyword speech recognition (wordspotting) is described. The techniques are the predictive neural model (PNM)-based wordspotting, in which the MLP is applied as a speech pattern predictor to compute a local distance between the acoustic vector and the phone model, and the hybrid HMM/MLP-based wordspott...

this study shows the usefulness of artificial neural network (ann) in maintenance planning and man-agement. an ann model based on the multi-layer perceptron having three hidden layers and four processing elements per layer was built to predict the expected downtime resulting from a breakdown or a maintenance activity. the model achieved an accuracy of over 70% in predicting the expected downtime.

background: although experiments on water quality are time consuming and expensive, models are often employed as supplement to simulate water quality. artificial neural network (ann) is an efficient tool in hydrologic studies, yet it cannot predetermine its results in the forms of maps and geo-referenced data. methods: in this study, ann was applied to simulate groundwater quality and geographi...

Maximal oxygen uptake (VO2max) is one of the most distinguished parameters in endurance sports and plays an important role, for instance, in predicting endurance performance. Different models have been used to estimate VO2max or performance based on VO2max. These models can use linear or nonlinear approaches for modeling endurance performance. The aim of this study was to estimate VO2max in hea...

The feature of HIV genome is in a wide range because of it is highly heterogeneous. Hence, the infection ability of the virus changes related with different chemokine receptors. From this point, R5 and X4 HIV viruses use CCR5 and CXCR5 coreceptors respectively while R5X4 viruses can utilize both coreceptors. Recently, in Bioinformatics, R5X4 viruses have been studied to classify by using the co...

Cox regression model serves as a statistical method for analyzing the survival data, which requires some options such as hazard proportionality. In recent decades, artificial neural network model has been increasingly applied to predict survival data. This research was conducted to compare Cox regression and artificial neural network models in prediction of kidney transplant survival. The prese...