نتایج جستجو برای: layer perceptron model mlp and multiple regression model
تعداد نتایج: 17305524 فیلتر نتایج به سال:
The data model of independent component analysis (ICA) gives a multivariate probability density that describes many kinds of sensory data better than classical models like Gaussian densities or Gaussian mixtures. When only a subset of the random variables is observed, ICA can be used for regression, i.e. to predict the missing observations. In this paper, we show that the resulting regression i...
one of the most important factors in study of project’s , is the study of environmental opportunities factors the permanent study of environmental opportunity factors . the study of permanent environmental changing and knowing golden threats’ cooperate with possible threats and at the result in structure forming and future skeleton of organization has a vital effects . the petrochemical industr...
In this paper, we propose a way to apply Multi Layer Perceptron (MLP) to Functional Data Analysis. We introduce a computation model for functional input data and we show that this model is a well behaving extension of MLP: we show that the proposed model has the universal approximation property. Moreover, parameter estimation for this model is consistent. As a conclusion, we demonstrate functio...
the purpose of this study is to investigate the relationships between teachers’ immediacy behaviors and iranian students’ willingness to talk in english classes. analysis of the results from willingness to talk scale represents a relatively high level of willingness to talk in english classrooms among iranian language learners. the total mean score of students’ willingness to talk was 66.3 ou...
Usually in multivariate regression problem it is assumed that residuals of outputs are independent of each other. In many applications a more realistic model would allow dependencies between the outputs. In this paper we show how a Bayesian treatment using Markov Chain Monte Carlo (MCMC) method can allow for a full covariance matrix with Multi Layer Perceptron (MLP) neural networks.
over the past decades a number of approaches have been applied for forecasting mortality. in 1992, a new method for long-run forecast of the level and age pattern of mortality was published by lee and carter. this method was welcomed by many authors so it was extended through a wider class of generalized, parametric and nonlinear model. this model represents one of the most influential recent d...
Accurate and reliable simulation and prediction of the groundwater level variation is significant and essential in water resources management of a basin. Models such as ANNs and Support Vector Regression (SVR) have proved to be effective in modeling nonlinear function with a greater degree of accuracy. In this respect, an attempt is made to predict monthly groundwater level fluctuations using M...
energy management is one of the main ways of the efficient use of energy resources. the prediction of crop yields based on energy inputs can help farmers and policymakers to estimate the level of production. required data for study were randomly collected from 70 broiler farms in north west of iran. the input energies were included human labour, machinery, fuel, feed and electricity and the out...
A multilayer perceptron is a feed forward artificial neural network model that maps sets of input data onto a set of appropriate output. It is a modification of the standard linear perceptron in that it uses three or more layers of neurons (nodes) with nonlinear activation functions and is more powerful than the perceptron in that it can distinguish data that is not linearly separable, or separ...
Background: Pregnancy in women with systemic lupus erythematosus (SLE) is still introduced as a major challenge. Consulting before pregnancy in these patients is essential in order to estimating the risk of undesirable maternal and fetal outcomes by using appropriate information. The purpose of this study was to develop an artificial neural network for prediction of pregnancy outcomes including...
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