Neural Net

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Neural Net

A computerized system designed to imitate human thought. Specifically, neural nets are designed to make or recommend investment decisions in light of changing circumstances in a market. Using neural nets has become more common as computers have become more accessible.
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The data collected for nine independent variables was re-coded with 1 = "strongly disagree", 2 = "Disagree", 3 = "Neutral" as 1 = "Dissatisfied" and 4 = "Agree", 5 = "Strongly Agree" as 2 = "satisfied" for the purpose of analysis using neural networks.
Table 3 shows the results obtained by neural networks with two, four, six and twelve hidden layers and it is concluded that the best neural network configuration is probably four hidden neurons, which has higher correlation and decreased error between actual output and MLP predicted output.
The viability of neural network for modeling the impact of individual job satisfiers on work commitment in Indian manufacturing unit
Most models of artificial neural network are nonlinear dynamic system.
Linear programming problem for nonlinear convex set constraints based on nonlinear neural network
Neural networks consist of a minimum of two layers of neurons which each has an input, a body and an output part.
Neural networks for smart homes and energy efficiency
Abstract: Purpose of this paper is to design a nonlinear recurrent neural network model of a laboratory device AMIRA DR300 made by a company AMIRA in Germany.
Nonlinear modeling of laboratory model AMIRA DR300 by recurrent neural network
Neural Networks is unique in its range and provides a forum for developing and nurturing an international community of scholars and practitioners who are interested in all aspects of neural networks and related approaches to computational intelligence.
Elsevier Announces New Co-Editor-In-Chief for Neural Networks
One of the main limitations of neural network models may occur especially when complex neural networks are trained with limited amount of data.
Predesign cost estimation of urban railway projects with parametric modeling/Miestu gelezinkeliu projektu priesprojektiniu islaidu skaiciavimas, taikant parametrini modeliavima
Artificial neural networks (ANNs) are defined as computational models inspired by the biological neural network of the human brain (Dayhoff 1990).
A neural network-based method for solving "nested hierarchy" areal interpolation problems
In this simulation examples of a control system with MPC using neural networks is given.
Model based predictive control using neural network and fuzzy logic
Neural networks so as fuzzy logic [7] are dealing with important aspects of knowledge representation, reasoning and learning, but in different approaches with their advantages and weaknesses.
Classification of fuzzy-based information using improved backpropagation algorithm of artificial neural networks
Patil and Smith obtained data for generating a computerized neural network from a nomogram previously published by Sarah de Ferranti from the Department of Cardiology at Children's Hospital in Boston and Harvard Medical School, and by Nader Rifai from the Department of Laboratory Medicine at Children's Hospital and Harvard Medical School's Department of Pathology.
Neural network analysis speeds disease risk predictions: innovative clinical models transform cardiovascular assessment algorithm
As Hardin (2002) observes in his essay, "Indeterminacy and Basic Rationality," statistical methods, such as neural networks, were developed partly as the product of the ordinal revolution in economics and choice theory.
Business applications of emulative neural networks

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