Biomedical Engineering Reference
In-Depth Information
Chapter XIX
A Genetic Algorithm-Artificial
Neural Network Method for the
Prediction of Longitudinal
Dispersion Coefficient in Rivers
Jianhua Yang
University of Warwick, UK
Mark S. Leeson
University of Warwick, UK
Evor L. Hines
University of Warwick, UK
Gregory P. King
University of Warwick, UK
Ian Guymer
University of Warwick, UK
XuQin Li
University of Warwick, UK
Daciana D. Iliescu
University of Warwick, UK
AbSTRACT
In this chapter a novel method, the Genetic Neural Mathematical Method (GNMM), for the prediction
of longitudinal dispersion coefficient is presented. This hybrid method utilizes Genetic Algorithms (GAs)
to identify variables that are being input into a Multi-Layer Perceptron (MLP) Artificial Neural Network
(ANN), which simplifies the neural network structure and makes the training process more efficient.
Once input variables are determined, GNMM processes the data using an MLP with the back-propaga-
tion algorithm. The MLP is presented with a series of training examples and the internal weights are
adjusted in an attempt to model the input/output relationship. GNMM is able to extract regression rules
from the trained neural network. The effectiveness of GNMM is demonstrated by means of case study
data, which has previously been explored by other authors using various methods. By comparing the
results generated by GNMM to those presented in the literature, the effectiveness of this methodology
is demonstrated.
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