改進(jìn)BP神經(jīng)網(wǎng)絡(luò)在水質(zhì)評價中的應(yīng)用研究.pdf

1、重慶理工大學(xué)碩士學(xué)位論文改進(jìn)BP神經(jīng)網(wǎng)絡(luò)在水質(zhì)評價中的應(yīng)用研究姓名：李文娟申請學(xué)位級別：碩士專業(yè)：測試計量技術(shù)及儀器指導(dǎo)教師：張蓮2011-05-26ABSTRAT III Abstract Water is the source of life, and water environmental management has direct impact on humanity’s survival and development. Wa

2、ter environmental quality assessment is the basis of water environmental management. The traditional evaluation methods, such as single-factor evaluation and comprehensive pollution evaluation, are questioned because of

3、their application limitations. Therefore, it’s very important for us to find an objective and universal water quality evaluation method. In recent years, the outstanding performance of BP neural network in pattern recogn

4、ition makes it possible. The BP neural network used in water quality evaluation can overcome the shortcomings of traditional evaluation methods, and makes it possible for all kinds of rivers to compare water quality long

5、itudinally. Because of the BP network’s defects and the particularity of water quality assessment, the problems of work efficiency and recognition accuracy have not well resolved to the water quality assessment model bas

6、ed on BP network. To solve these problems, this paper researched into water quality assessment model based on improved BP neural network. Main works are as follows: （1） The basic theory of BP neural network was introduce

7、d. Taking the BP network’s defects and its problems met in water quality assessment into account, the Golden Section Algorithm was improved to get the reasonable number of BP neural network's hidden nodes. Then the B

8、P network was improved by the LM algorithm, and a water quality assessment model based on LM-BP network was established. The optimal model was used to evaluate the water quality degree of Xindu area of Chengdu, which wer

9、e compared with the results derived from comprehensive pollution evaluation method. The feasibility of the water quality assessment model, founded by LM-BP neural network, was proved. （2）In order to further improve the r

10、ecognition accuracy of the network, the Genetic Algorithm and BP network were combined. By using genetic algorithm’s global search capability to find the optimal weights and threshold for the BP network, and the Water Qu

11、ality Assessment based on GA-BP Network was established. Experiments indicate that the model’s network performance (convergence speed and the mean square error of test samples) are better than the LM-BP network model’s.