| Water quality evaluation is the key foundation of calculating water environmental capacity and implementing the planning of water pollution control. On the basis of a comprehensive exposition and analysis of the basic principles, the algorithm and the varied pattern features of Artificial neural network (ANN), especially Back-Propagation(BP) network, and on the basis of a exposition of the basic principles of Fuzzy Mathematics, according to the demand and trait of water quality evaluation, this thesis combines Fuzzy Mathematics and ANN and develops Back-Propagation network in series with degree of membership (BPDM) model and Fuzzy neural network(FNN) model .The algorithm and the pattern features of BPDM model that is composed by ANN in series with degree of membership. BP network only need to learn five kinds of water quality standards and is able to master the rational rules among the water quality parameters automatically. The evaluation results are objective. Different from a normal BP network, BPDM model regards the output of BP network as the input of Fuzzy System and calculates the degrees of membership of which the testing samples belong to each water quality standards. The final output results of BPDM are accurate and specific water quality categories of testing samplesThe algorithm and the pattern features of FNN model that is composed by ANN and Fuzzy System according to learning integrated. FNN model can not only direct expresses the logic meaning of people's customs and be fit for direct or advanced expression of knowledge but also have the merits of ANN self-adaptation learning and non-linear expression. Researches on FNN application in the water quality evaluation are preliminary exploration of author's. Case studies show that FNN is able to precisely evaluate other samples besides the training samples after learning, thus has better objectiveness reliability and expression.The having learning models of BPDM and FNN are tested with the data from the rivers in Anhui province. The applications of BPDM and FNN in water quality evaluation have a bright future and it is not only feasible in theory but also has huge significance in practice .Especially FNN, if more researches can be done in the ascertaining of its fuzzy partition and rules, the better effect of evaluation can be gained. It is well worth exploiting and researching for researchers. |
