Research On Analytical Method With Grey System And Neural Network And Their Application

System science is one of the most grandeur achievements of the 20th century. General system theory proposed by L.V.Bertranffy, Klir is the basic theory of system science. Mesarovic, Takahara and Lin developed general system theory and got many instructive results. The mathematical general system theory proposed by Mesarovic and Takahara does commit to the control and decision-making problems of input-output system and gives a series of concepts, structures and theorems in quit perfect and precision format. Meaning while, as an important part of general system theory, the gray system theory is widespread availability, especially the trend associate analyzing theory and system cloud modeling theory proposed by Prof. M.Y. Chen gives a novel way to apply general system theory in "poor" information system. This dissertation is based on the both theories, by using neural network methods, researches on the modeling, forecasting, clustering, intelligent control and distributed computing problems of control system. The main results of the dissertation is as follows:By summarizing present state and perspectives of the researches on general system theory and "poor" information system, this dissertation discusses the relationship of general system theory, "poor" information system and control system. More over, the development status of artificial neural networks, especially the dynamic neural networks is introduced, as well the possibility and necessity of the combination of "poor" information system and neural network is proofed. These works give the feasibility of the application of general system theory, "poor" information system theory and neural network in control system.The gray forecast and neural network control mode on lag system is proposed. Firstly, an error-forecast method for little lag servo systems is given in order to improve the tracing performance. Secondly, we discuss the features of prediction models GM(1,1) and SCGM(1,1), and indicate that both are modeling methods for energy system, in which GM(1,1) model requires a tiny develop coefficient while SCGM(1,1) model not, while both are sensitive to disturbances and mutations. The article proposes several gray predicted methods for great lag systems in variable circumstance, especially the step-varying gray predicted control model that can abate prediction errors, and the neural controller that can improve the adaptability of control system.By indicating the superiority of high-order neural networks, this dissertation proposes a modeling method for nonlinear dynamical system based on high-order neural networks and discusses its convergence property. More over, three training algorithms including filter regression algorithm, filter error algorithm and dynamical BP algorithm are given based on this network. With further discussing, the dynamical system is divided into dynamic part and static part using mathematic general system theory. The modeling method for general dynamical system is proposed with combined neural network with its merits indicated. Two training methods, partial learning method and global learning method, are discussed with certain circumstance.Several features of trend relational grade are studied, which satisfied the four axioms of gray correlation, and externalized the integer correlation of approachability and comparability. A parameter identification method of nonlinear system based on genetic algorithm is proposed using trend relational grade as indication. A clustering method of temporal series is proposed by applying distance formula of trend relational grade, including the trend relational K-Means clustering for constant categories, trend relational clustering for constant categories and constant dimensions based on Partheno-Genetic algorithm.The methods of coalescence of gray system and neural network are discussed. First of all, the complementation of gray predicted method and neural network is discussed, and proposes a gray-neural network prediction model combined both gray predicted method and neural netwo...