The Study Of Evaporative Type Air-condition System Performance Experiment And Optimum Seeking Based On Intelligent Method

Based on analysis and summary of predecessors. research results on evaporative type air-condition(ETAC)technology,a novel research theory and method(RT&M) is presented in this paper. With the aid of experiment, and starting with RT&M. contrast selection and experiment demonstration, further theory and experiment study is processed and some significant conclusions and innovative points are obtained, whichestablishes foundation for the application of the novel RT&M. Main conclusions are as following: (1).Utilizing uniform experiment design can obviously lessen experiment time and errors, as well as save experimental fee and time. (2).Principal component analytical method and fuzzy classification method can be used to composeand sortthe discretive and nonlinear data obtained from the uniform experiment,and the results are as anticipated. (3).BP neural network can be used to predict the output performance of ETAC system, and all relative errors are within 4%, can entirely meet the common engineering needs. (4).The BP neural network comprehensive evaluation(CE) model can avoid man-made invalidation during the evaluation process,and have strong error tolerance capability. (5).Satisfying solution can be attained by using the fuzzyCE method for comparing and optimum seeking the filler. Main innovative points are as following: 1 Initially put forward and apply uniform experiment method in the study of ETAC performance experiment. 2 Initiallyput forward and apply novel methods of principal component 4 第一章 绪论analysis method and fuzzyclassification method duringdiscretive andnonlinear experimental data processing and analysis. 3 Initiallyput forward and applyBP neural network in predicting the output performance of ETAC system, and the results are satisfying. 4 Initiallyput forward and applyBP neural network in establishing the CE model of the output performance of ETAC system. 5 Initiallyput forward and applythe fuzzy CE method to comparing and optimum seeking the filler.