|
|
|
|
|
|
［主要論文要旨］
"Measurement Optimization with Sensitivity Criteria for Distributed Parameter Systems" IEEE Trans. on Automatic Control, Vol.25, No.5, pp.889-901, October 1980. We consider the problem of optimally designing sensors for observation of a class of distributed parameter systems. The design of sensors concerns the choice of measurement conditions so that the information provided by measurements is maximal. This problem has been posed as a deterministic optimal control problem for a system equation of the Riccati type that governs a filter covariance. In this paper we introduce a function called a sensitivity criterion by extending the Fisher information matrix to function spaces. It is shown that maximizing this criterion leads to a suboptimal solution of the sensor design problem associated with an infinite-dimensional state estimation problem. The existence theorem for a type of measurement optimization problem is proved and some numerical results are presented.
"Interactive Design of Urban Level Air Quality Monitoring Network" International Journal of Atmospheric Environment, Pergamon Press, Vol.18, No.4, pp.793-799, 1984. An interactive optimization method for designing an air quality-monitoring network in an urban area is proposed. The main purpose is to determine representative areas of monitoring stations rather than their precise locations. Two topologies are introduced to define similarities among pre-divided uniform meshes. The first is derived from the differences of long-term averages of pollutant concentrations between every two meshes and used in the Ward method clustering. The second is obtained by the cross-impacts between pair of meshes and used as a constraint in the clustering process. Participation of specialists in the optimization process is allowed in such a way that they can modify the second topology by taking account of economical and physical conditions as well as inaccuracy of simulation models. This technique is applied to the NOx monitoring network of Kyoto, Japan.
"Development and Application of an Interactive Modeling Support System" AUTOMATICA, International Federation of Automatic Control, Pergamon Press, Vol.25, No.2, pp.185-206, March 1989. This paper outlines an interactive modeling support system that helps model building for complex, large-scale systems involving human behavioral aspects. The system consists of combined modeling techniques using statistical and graph-theoretical approaches, and multi-stage person-computer dialogues. It assists flexible determination and clear definition of the model objective with conviction. Three examples are presented to show its applicability to the wide range of concrete problems.
"Integrated Decision Support System for Environmental Planning" IEEE Trans. on Systems, Man and Cybernetics, Vol.20, No.4, pp.777-790, 1990. This paper presents the processes to use the intelligent decision support system: identification process, modeling process and simulation process. The identification process consists of collecting knowledge as well as numerical data, identifying the structure of the problem and analyzing environmental conditions through linguistic fuzzy simulation. The modeling process consists of building computer simulation models by combining expertsﾕ judgments and numerical data. The simulation process consists of predicting future environmental conditions by assuming some policy scenarios. As an example, we analyzed environmental problems and obtained a fuzzy model for predicting NO2 concentration based on several future scenarios about Tokyo Bay development program in Japan.
"Identification of Fuzzy Prediction Models through Hyperellipsoidal Clustering" IEEE Trans. on Systems, Man and Cybernetics, Vol.24, No.8, pp.1153-1173, 1994. To build a fuzzy model, as proposed by Takagi and Sugeno, we emphasize an interactive approach in which our knowledge or intuition can play an important role. It is impossible in principle, due to the nature of the data, to specify a criterion and procedure to obtain an ideal fuzzy model. Instead of such a normative approach, our effort should be directed to develop a method which makes it possible to observe data scattered in a multi-dimensional space. The main subject of fuzzy modeling is how to analyze data in order to summarize it to a certain extent so that we can judge quality of our model by intuition. The main proposal in this paper is a clustering technique which takes into account a balance between continuity and linearity of the data distribution. We call this technique the hyperellipsoidal clustering method, which assists modelers in finding fuzzy subsets suitable for building a fuzzy model. We will deal with other problems in fuzzy modeling as well, such as the effect of data standardization, the selection of conditional and explanatory variables, the shape of a membership function and its tuning problem, the manner of evaluating weights of rules, and the simulation technique for verifying a fuzzy model.
"Methodology and Systems for Environmental Decision Support" Annual Reviews in Control, Vol.20, pp.143-154, 1997. This paper emphasizes a soft approach which uses both mathematics and adaptive formation of a problem solving process. It stresses the dynamics of the process, adaptive learning and stimulation of intuition and creativity of people. It requires an instrument for interaction between analysts and computers. In order to embody this methodology, we are developing an interactive and intelligent decision support system to handle human decision-making within environmental problem solving. The latter half of this paper is devoted to describe an interactive modeling technique with computer assistance.
"Complex Systems Analysis and Environmental Modeling" European Journal of Operational Research, Vol.122, No.2, pp.178-189, April 16, 2000. Most studies of complex systems take the way of comparing a developed artificial world in the computer with the real world and trying to explore the principles of complex real world. But, this is not enough to understand complex phenomena and make decisions. This paper emphasizes a soft approach that uses both logic and educated intuition of people. Our methodology originates in Sawaragi's shinayakana systems approach that is based on Japanese intellectual tradition. This paper introduces our systems methodology together with our trial of applying it to the global environmental problems.
Home Profile Books Research Laboratory