State Prediction of Algae Reproduction Based on PCA-Fuzzy BP Method

Abstract

Abstract: Principal component analysis (PCA) method combined with fuzzy back propagation (BP) network, called as PCA-fuzzy BP method, was proposed to predict the state of algae reproduction, and the prediction model of chlorophyll-a concentration was established. PCA method was used to preprocess various acquisition data, it reduces the dimensionality of the input data of the system. The physicalchemical factors produced by PCA processing can be regarded as the input variables of the fuzzy BP network, the concentration of chlorophylla can be regarded as the output of fuzzy BP network, and the state prediction model of algae reproduction can be obtained by learning and training for this network. The experimental result indicates that PCA method reduces the correlation for the factors of input sample data, also reduces the dimension of the model system, accelerates the speed of modelling convergence for fuzzy BP network. The PCA-fuzzy BP algorithm has a faster speed of calculation and more accuracy of prediction than the typical BP network. This kind of model can give a better state prediction for algae reproduction.

Key words: principal component analysis (PCA), fuzzy back propagation (BP) network model, chlorophyll-a, state prediction

CLC Number:

X 55

ZHANG Ying-1, LI Cai-Juan-1, SHAO Hui-He-2. State Prediction of Algae Reproduction Based on PCA-Fuzzy BP Method[J]. Journal of Shanghai Jiaotong University, 2012, 46(05): 785-789.

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