A Hybrid Optimization Method for Neural Tree Network Model

Neural tree network model has been successfully applied to solving large numbers of complex nonlinear problems in control area. The optimization of neural tree model contains: structure and parameter, the major problem in evolving structure without parameter information was noisy fitness evaluation...

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Vydáno v:Applied Mechanics and Materials Ročník 273; s. 820 - 825
Hlavní autoři: Qi, Feng, Ma, Ying Hong, Liu, Xi Yu
Médium: Journal Article
Jazyk:angličtina
Vydáno: Zurich Trans Tech Publications Ltd 2013
Témata:
Neural Tree Network Model
Improved Hybrid Breeder Genetic Programming
Fitness Evaluation
Exponential Mutation
Time Series Forecasting
ISBN:9783037855881, 3037855886
ISSN:1660-9336, 1662-7482, 1662-7482
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Shrnutí:Neural tree network model has been successfully applied to solving large numbers of complex nonlinear problems in control area. The optimization of neural tree model contains: structure and parameter, the major problem in evolving structure without parameter information was noisy fitness evaluation problem, so an improved genetic programming algorithm is proposed to synthesize the optimization process. Simulation results on two time series prediction problems show that the proposed strategy is a potential method with better performance and effectiveness.
Bibliografie:Selected, peer reviewed papers from the 2nd International Conference on Innovation Manufacturing and Engineering Management (IMEM 2012), December 14-16, 2012, Chongqing, China
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ISBN:9783037855881
3037855886
ISSN:1660-9336
1662-7482
1662-7482
DOI:10.4028/www.scientific.net/AMM.273.820