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.
Most models of artificial neural network
are nonlinear dynamic system.
consist of a minimum of two layers of neurons which each has an input, a body and an output part.
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.
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.
One of the main limitations of neural network
models may occur especially when complex neural networks
are trained with limited amount of data.
Artificial neural networks
(ANNs) are defined as computational models inspired by the biological neural network
of the human brain (Dayhoff 1990).
In this simulation examples of a control system with MPC using neural networks
so as fuzzy logic  are dealing with important aspects of knowledge representation, reasoning and learning, but in different approaches with their advantages and weaknesses.
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.
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.