PROPOSED METHODOLOGY FOR OPTIMIZING THE TRAINING PARAMETERS OF A MULTILAYER FEEDFORWARD ARTIFICIAL NEURAL NETWORKS USING A GENETIC ALGORITHM
An artificial neural network (ANN), or shortly "neural network" (NN), is a powerful mathematical or computational model that is inspired by the structure and/or functional characteristics of biological neural networks. Despite the fact that ANN has been developing rapidly for many years...
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| Format: | Thesis |
| Language: | English |
| Published: |
2011
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| Online Access: | http://utpedia.utp.edu.my/2846/1/my_thesis_osman30-1-2001...last7.pdf http://utpedia.utp.edu.my/2846/ |
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| Summary: | An artificial neural network (ANN), or shortly "neural network" (NN), is a powerful
mathematical or computational model that is inspired by the structure and/or
functional characteristics of biological neural networks. Despite the fact that ANN has
been developing rapidly for many years, there are still some challenges concerning
the development of an ANN model that performs effectively for the problem at hand.
ANN can be categorized into three main types: single layer, recurrent network and
multilayer feed-forward network. In multilayer feed-forward ANN, the actual
performance is highly dependent on the selection of architecture and training
parameters. However, a systematic method for optimizing these parameters is still an
active research area. This work focuses on multilayer feed-forward ANNs due to their
generalization capability, simplicity from the viewpoint of structure, and ease of
mathematical analysis. Even though, several rules for the optimization of multilayer
feed-forward ANN parameters are available in the literature, most networks are still
calibrated via a trial-and-error procedure, which depends mainly on the type of
problem, and past experience and intuition of the expert. To overcome these
limitations, there have been attempts to use genetic algorithm (GA) to optimize some
of these parameters. However most, if not all, of the existing approaches are focused
partially on the part of architecture and training parameters. On the contrary, the GAANN
approach presented here has covered most aspects of multilayer feed-forward
ANN in a more comprehensive way. This research focuses on the use of binaryencoded
genetic algorithm (GA) to implement efficient search strategies for the
optimal architecture and training parameters of a multilayer feed-forward ANN.
Particularly, GA is utilized to determine the optimal number of hidden layers, number
of neurons in each hidden layer, type of training algorithm, type of activation function
of hidden and output neurons, initial weight, learning rate, momentum term, and
epoch size of a multilayer feed-forward ANN. In this thesis, the approach has been
analyzed and algorithms that simulate the new approach have been mapped out. The approach has been tested in three actual operations, in addition to standard XOR
problem; where the results have shown the applicability of the proposed approach in
those applications. The proposed method is considered novel as it has proven that
GA-based method can be comprehensively utilized to determine multilayer feedforward
ANN architecture and training parameters. This method is more effective and
gives a more precise performance than existing approaches, in addition to being less
human dependent. It also has a better generalization capability and training stability.
In summary, the main contributions of this research are: demonstrates the strength of
genetic algorithm (GA), auto designing of multilayer feed-forward ANN, and
demonstrates the hybridization capability of GA with multilayer feed-forward ANN. |
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