Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.478916
Title: Stochastic methods and genetic algorithms for neural network learning
Author: Georgieva, Antoniya
ISNI:       0000 0001 3494 8543
Awarding Body: University of Portsmouth
Current Institution: University of Portsmouth
Date of Award: 2008
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Abstract:
This thesis presents results from the developemnt, investigation, testing and evaluation of novel meta-heuristic techniques aiming to further improve the state-of-the-art of algorithms for local minima free Neural Network supervised learning. Several approaches for solving Global Optimisation problems that make use of novel meta-heuristic techniques, so called Low-discrepancy Sequences, and hybrid Evolutionary Algorithms are proposed here, investigated and critically discussed. Furthermore, the novel methods are tested on a number of multimodal mathematical function optimisation problems, as well as on a variety of Neural Network learning tasks, including real-world benchmark datasets. Comparison of the results from the investigated methods with such from standard Backpropagation, Evolutionary Algorithms, and other stochastic approaches (Simulated Annealing, Tabu Search, etc.) is conducted in order to demonstrate their competitiveness in terms of number of function evaluations, learning speed and Neural Network generalisation abilities. Finally, the investigated techniques are applied and tested on real-world problems for the intelligent recognition and classification of cork tiles. An Intelligent Computer Vision system is built. The system includes the following stages: image acquisition; image processing (feature extraction and statistical data processing); Neural Network architecture design; supervised learning utilising the proposed Global Optimisation techniques; and finally, extensive system evaluation. The presented examples and case studies demonstrate that the proposed techniques can be effectively applied for the optimisation of mathematical multimodal functions. The investigated methods are successful in local minima free Neural Network learning, and they can be used for solving real-world industrial problems.
Supervisor: Jordanov, Ivan Nikolov ; Gegov, Alexander Emilov ; Perez-Fontan, Fernando ; Sanders, David Adrian Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.478916  DOI: Not available
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