GMDH Network Model Based on Simulated Annealing and Genetic Algorithm

Abstract:

According to the traditional UMDH network modeling with the least square method to recognize parameters ,it's easy to fall into local minimum ,and with the result that the prediction effect is not ideal. This paper puts forward to combine the simulated annealing algorithm and genetic algorithm, and introduces the combined algorithm to the UMDH network which is used to identify some of its description type coefficient. In this paper ,it describes the simulated annealing genetic algorithm ,and constructs the UMDH network model based on this algorithm, and the model is applied to the simulation of debris flow prediction research ,forecast average relative error reached 3. 54%. The results show that the algorithm not only ensuring the global optimization but also preventing premature convergence, improve the UMDH network model of global and local searching optimal ability further.

• Periodical: Applied Mechanics and Materials (Volumes 380-384)
• Pages: 1370-1373
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.380-384.1370
• Online since: August 2013
• Authors: Xiao Ling Zhang, Li Kun Zou
• Keywords: Genetic Algorithm (GA), Prediction, Self-Organization, Simulated Annealing Algorithm, Uroup Method of Data Handling

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