THE FLORIDA STATE UNIVERSITY ARTS AND SCIENCES ...

CHAPTER FIVE
5. ANALYSIS USING NEURAL NETWORKS
Attempts to establish relationships between the denitrification rate and the various
controlling parameters using conventional statistical methods have met with limited
success. It is clear that any attempt to develop a generalized set of equations to estimate
the denitrification rate requires a method that is capable of dealing with multiple
variables and the complex non-linear relationships that are characteristic to
denitrification. Artificial neural networks (ANNs) possess such a capability.
Logistic regression models can also be used to model complex nonlinear relationships
between independent and dependent variables; however this requires an explicit search
for these relationships and may require complex transformations of predictor or outcome
variables (Tu, 1996). Appropriate transformations may not always be available for
improving model fit, and significant nonlinear relationships may go unrecognized by
model developers (Tu, 1996). Neural networks have the ability to detect all possible
interactions between predictor variables. The hidden layer of a neural network gives it the
power to detect interactions or inter-relationships between all of the input variables (Tu,
1996).
5.1. Introduction
Artificial neural networks (ANNs), sometimes simply called a neural networks are a
mathematical model or a computational model that simulates the structure or functional
aspect of a biological neural network. In many cases an ANN is an adaptive system that
changes its structure based on information that flows through the network during the
training phase (Fausett, 1994).
An artificial neural network is specified by: (Meyer-Baese, 2009):
• An architecture: this is a set of neurons and links connecting the neurons, with
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