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Real-coded genetic algorithm based variable feed-forward neural networks and their applications

Posted on:2008-07-20Degree:Ph.DType:Thesis
University:Hong Kong Polytechnic University (People's Republic of China)Candidate:Ling, Sai HoFull Text:PDF
This thesis focuses on the real-coded genetic algorithm and different topologies of feed-forward neural networks. Results in the following areas will be reported: (1) real-coded genetic algorithm with new crossover and mutation operations, and its applications; (2) three different topologies of variable feed-forward neural networks, and their applications on short-term electric load forecasting and hand-written graffiti recognition.; In this thesis, RCGA with new genetic operations called the average-bound crossover (ABX) and wavelet mutation (WM) will be presented. On realizing the ABX operation, the offspring spreads over the domain so that a higher chance of reaching the global optimum can be obtained. Taking advantage of the wavelet theory, the performance of the mutation operation in terms of the cost function value, solution stability (standard deviation of solutions) and the convergence rate is improved. A suite of benchmark test functions are used to evaluate the performance of the proposed algorithm. Application examples on economic load dispatch and tuning parameters of neural networks are used to show the merits of the proposed RCGA.; The three proposed topologies of variable feed-forward network networks are: (1) the variable-structure neural network (VSNN), (2) the variable-parameter neural network (VPNN), and (3) the variable-node-to-node-link neural network (VN2NN). By taking advantage of these networks' structures, we can increase the learning and generalization abilities of the networks. All the network parameters are tuned by the proposed RCGA with ABX and WM.; Application examples on short-term electric load forecasting and hand-written graffiti recognition are given to illustrate the merits of the proposed neural networks. Based on the results from the two application examples, the performance characteristics of the three proposed networks will be investigated and explained. Comparisons among the networks will be conducted in order to let the user have an idea on how to choose which network to use for different kinds of problems.
Keywords/Search Tags:Network, Real-coded genetic algorithm, Different, Application
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