Research On Artificial Neural Network And Genetic Algorithm For The Optimizing Of Schemes Of The Water Pollution Control Planning

Innovative work is done on the applying of discrete hopfield neural networks (DHNN) and genetic algorithms (GA) to the optimization of the planning scheme of water pollution control in this dissertation.Artificial neural network and genetic algorithms play the leading role in the sciences for complex non-linear phenomena and artificial intelligence. Researches on its application in the water pollution control are still in the preliminary stage in the world. On the basis of a comprehensive evaluation and analysis of the present situation of the researches in the way of water pollution control, and on the basis of a careful exposition of the basic principles and the optimal algorithm of DHNN and GA, this dissertation gives an application of DHNN and GA approach in the scheme of water pollution control, which, as the first attempt of its kind, can help to achieve a higher level in the application of artificial intelligence in this field.Based on the features of integrated water pollution control and the astringency of the energy function of DHNN at certain condition, by the rational design of the energy function, the total cost of the scheme is transformed into the energy function of the DHNN, that is, the optimal scheme is corresponding to the point whose energy function is the lowest, the optimal model of DHNN of the water pollution control is put forwarded, and, at the same time, a strict mathematical deduction of the astringency of the energy function of DHNN is given. Case study reveals that: the optimal scheme of DHNN can make the use of environmental capacity sufficiently, and, when the concentration of a polluter in certain section gains over the aim in the optimizing process, the scheme that the cost of reducing per contribution concentration at this section is chosen, so it can get the optimal scheme quickly.Genetic algorithms is a widely used amount method, which almost has no requirement for the object function, even almost has no requirement for obvious object function. When it is used to optimizing calculation, it can't deal with the matter's parameter directly, but can only deal with the individual population in gene chain code. The gene chain code of the GA is combined to the scheme of water pollution control trickly, that is, using one, two, three to mean for the nontreatment, primary treatment and second treatment.At the same time, the total cost of the optimal scheme is corresponding to the adaptation function of GA,which corresponds the higheradaptation function to the optimal scheme, thus, the excellent individual is the optimal scheme. Case study reveals that: the result of the GA is unsteadiness, because the original is chosen freely, and the GA is finished by certain iterative time. Althogh the results are unsteadiness, they both are the neighborhood of the optimal. Decision-maker can get the best one through the comparison of the feasibility > the economical and the scientific.This research demonstrates that with its theoretical feasibility and great practical utility, the applying of DHNN and GA to the optimization of the scheme of water pollution control has good prospects for further development and application. This research proposes a new way of thinking for studies of he optimization of the scheme of water pollution control and adds to the fields of application of ANN.