| Research Abstract |
To develop the effective control system of buildings, it should be necessary to take account of special features such as complexity, uncertainty of future loading. In this research, optimal adaptive and predictive control simulation systems of buildings against earthquake loading are developed based on intelligent systems such as fuzzy theory, neural network, genetic algorithm and so on. These systems have following features ; (1) Target responses and control variables are described with membership function, (2) Real time prediction of earthquake input and structural identification are performed, and (3) Optimization is performed by fuzzy maximizing decision.
In this research, following two systems are developed, i.e., ; (1) Simulation system of fuzzy optimal active control of building structures by intelligent systems, and (2) Optimal design method of active control system of buildings by genetic algorithm. These two systems are developed individually by object oriented programming lan
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guage under windows environment.
In the next step, there are discussed developing methods of an integrated simulation system based on imtelligent active control under distributed environment. Here, following two methods are discussed, i.e. ; (1) Application of testing system of fuzzy optimal control to distributed simulation system and (2) Distributed simulation systems by internet browser. In the former method, it is easy to develop distributed simulation systems based on a real testing system. However, this testing system is developed under DOS environment, so it is difficult to develop user-friendly systems by graphical presentation. In the latter method, it is easy to develop user-friendly system as stand-alone one. However, it is difficult to control component objects, so authors can not translate stand-alone systems into ones which can execute on internet browser. Through this research, the authors can get a lot of basic data and systems on the intelligent active control simulation system of buildings by fuzzy optimal logic under distributed environments. Less
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