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Application Research on Improved BP Neural Network for Water Quality Evaluation

Author: LiWenJuan
Tutor: ZhangLian
School: Chongqing University of Technology
Course: Measuring Technology and Instruments
Keywords: Water quality assessment BP network Genetic Algorithms LM algorithm Gold segmentation algorithm
CLC: TP183
Type: Master's thesis
Year: 2011
Downloads: 173
Quote: 5
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Abstract


Water is the source of all life, water environmental management will have a direct impact on human survival and development. Water environmental quality assessment is the basis of all the work of the water environment management, the traditional evaluation methods such as single-factor evaluation method and the Integrated Pollution Act, is being questioned because of the limitations of the application. Therefore, seek an objective, common water quality evaluation method is particularly important. In recent years, BP neural network in the outstanding performance of the pattern recognition whom may be brought. BP neural network applications in water quality assessment, can overcome the shortcomings of the traditional evaluation methods, might provide a longitudinal comparison for various categories of river water quality. But because of the particularity of the BP network defects, water quality assessment, water quality evaluation model makes BP network facing two major problems - efficiency and recognition accuracy problems, has not yet been satisfactorily resolved. This paper focuses on the two major issues explored in-depth research on improved BP neural network applications in water quality assessment. Of this research work is mainly divided into the following sections: (1) introduce the basic theoretical knowledge of BP neural network, for the three defects of BP network, as well as the problems encountered in the water quality assessment of the existing gold The segmentation algorithm will be used to find the optimal number of nodes of the hidden layer, to achieve the purpose of optimizing network. Followed by the LM algorithm for BP network has been improved, the establishment of a water quality evaluation model based on the LM-BP network, the use of the model made Xindu within the watershed and water quality evaluation, compared with the results of the evaluation of the comprehensive pollution index method to prove the feasibility of the network model. (2) In order to further enhance the accuracy of the identification of the network, the genetic algorithm with BP network combined use of global optimization ability of genetic algorithms to find the optimal weights threshold for BP network, in order to establish a water quality evaluation model of GA-BP network. Experiments show that the network performance (the convergence speed test sample mean square error) of the model are better than the LM-BP network model. Finally, the GA-BP network model to detect the same instance, and, respectively, compared with the LM-BP network evaluation model and the evaluation results of the Integrated Pollution Act, the GA-BP network water quality evaluation model is more reasonable and practical. (3) To explore the relationship between water quality indicators and categories contains special, replaced with a linear interpolation the random interpolation generated sample of GA-BP network has been established to train, through instances test results compared, indicating that the results of the evaluation of the linear interpolation not reflect water pollution. Thus proving that the training samples are generated using random interpolation can best embody the complex non-linear relationship between water quality indicators and categories. (4) The above study shows that this generated based on stochastic interpolation samples to establish water quality evaluation model of GA-BP network recognition accuracy, practicality and versatility. Finally, MATLAB R2009a another step from theoretical research to practical application, the BP network water quality evaluation model of water quality evaluation model based on improved BP network of human-computer interaction interface (GUI).

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CLC: > Industrial Technology > Automation technology,computer technology > Automated basic theory > Artificial intelligence theory > Artificial Neural Networks and Computing
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