Evolutionary Artificial Neural Networks for Hydrological Systems Forecasting
Date Issued
2009
Date
2009
Author(s)
Chen, Yung-Hsiang
Abstract
Intelligent optimal systems should have the ability of self-adaptation in order to adjust to various problems. Learning and evolution are two fundemental forms of the adaptation ability. However, the common optimal systems only have one of the two above abilities, for example, artificial neural networks (ANNs) with excellent learning and evolutionary algorithms (EAs) with admirable evolution. The conventional ways of constructing ANNs for a problem generally presume a specific architecture and do not automatically discover network modules appropriate for specific training data. EAs are used to automatically adapt the network architecture or connection weights according to the problem environment without substantial human intervention. To improve on the drawbacks of the conventional optimal process, this study presents a novel intelligent evolutionary artificial neural network, so-called hybrid-encoding evolutionary artificial neural network (HEEANN), for time series forecasting. The HEEANN has a hybrid encoding and optimization procedure, including the genetic algorithm, the scaled conjugate gradient algorithm, and the gradient descent, where the feed-forward ANN architecture (including the inputs and the neurons in hidden layers) and its connection weights of neurons are simultaneously identified and optimized. he proposed HEEANN has the abilities to: (a) automatically optimize architecture of feedforward ANNs; (b) automatically optimize input variables among all possible ones; (c) evolve network architecture with different-length hidden layers by performing crossover operation between ecoded architecture chromosome; (d) automatically deal with linear and non-linear optimization problems based on whether the evolved network architecture has hidden layer when prior information of the data is insufficient.e first explored the performance of the proposed HEEANN for the Mackey-Glass chaotic time series. The performances of the different networks were evaluated. The excellent performance in forecasting of the chaotic series shows that the proposed algorithm concurrently possesses efficiency, effectiveness, and robustness. We further explored the applicability and reliability of the HEEANN in several real hydrological time series. Again, the results indicate the HEEANN is supeior to backpropagation neural network (BPN), AR(1) and ARMAX models, or Modified Penman model with over 10% improvement.
Subjects
Evolutionary artificial neural network (EANN)
Genetic algorithm (GA)
Hybrid-encoding
Time series
Forecasting
Hydrology
Water resources
Type
thesis
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