Projection Pursuit (PP) model is a technique of falling high dimension. Real coding based on Accelerating Genetic Algorithm (RAGA) is a method of optimum. Through combining the PP model and RAGA, the paper applies the...Projection Pursuit (PP) model is a technique of falling high dimension. Real coding based on Accelerating Genetic Algorithm (RAGA) is a method of optimum. Through combining the PP model and RAGA, the paper applies the model in the water environment quality evaluation. The writer takes the water quality evaluated indexes of each sample as projection direction and turns high dimension data into low dimension projection value. Thus, the writer achieves on evaluating the grade of water samples and its optimum order. Based on this, the writer overcomes the jamming of weights calculated on fuzzy synthesize judge and gray system valuation. The paper can provide a new thought for water environment quality evaluation and other falling high dimension and optimum issue.展开更多
文摘Projection Pursuit (PP) model is a technique of falling high dimension. Real coding based on Accelerating Genetic Algorithm (RAGA) is a method of optimum. Through combining the PP model and RAGA, the paper applies the model in the water environment quality evaluation. The writer takes the water quality evaluated indexes of each sample as projection direction and turns high dimension data into low dimension projection value. Thus, the writer achieves on evaluating the grade of water samples and its optimum order. Based on this, the writer overcomes the jamming of weights calculated on fuzzy synthesize judge and gray system valuation. The paper can provide a new thought for water environment quality evaluation and other falling high dimension and optimum issue.
基金The project partly supported by National Natural Science Foundation of China under Grant Nos. 10225525 and 10435080 and Knowledge Innovation Project of the Chinese Academy of Sciences under Grant No. KJCX2-SW-N02. We thank H.C. Chiang, G.M. Jin, X.G. Li, J.Y. Liu, P.N. Shen, J.J. Xie, H.S. Xu, and W.L. Zhan for useful discussions.