Y. Nakamori

Shinayakana Systems Approach in Environmental Management

Abstract Socio-economic systems are so complex and ambiguous that new frameworks must be developed to solve modeling, control and decision making problems involved. One such framework, we are developing, is the ShinayakanaSystemsApproach in which mathematical models and formal tools provide the problem solving support only, and therefore, the essential parts of the problem solving are the issues of human-computer interaction. This paper presents the basic ideas of the ShinayakanaSystemsApproach as well as a practical implementation of this concept in the design of a system for environmental management.

Adaptive fuzzy clustering and fuzzy prediction models

This paper proposes a new fuzzy clustering technique for identification of fuzzy prediction models. An existing approach to the simultaneous determination of data partition and regression equations is modified in such a way that the shapes of clusters are changed dynamically and adaptively in the clustering process. After introducing a type of membership function, a technique for the integration of fuzzy rules is discussed. As a concrete example, a fuzzy operator model to control a rotary kiln process which treats excess sludge from a municipal wastewater treatment plant is presented.<<ETX>>

Factor analysis for fuzzy data

This paper proposes a factor analysis technique for fuzzy data which are rating scores measured by adjectives. Fuzzy correlation coefficients are introduced and factor loadings are determined as fuzzy numbers. Thus, adjectives are identified as fuzzy objects in the factor space. After fuzzy distances between adjectives in the factor space are defined, a covering problem is formulated as an integer programming problem to determine a set of representative adjectives and an overlapped partition of adjectives simultaneously. This provides a useful information to study the relation between adjectives and designs.

Development and Application of an Interactive Modeling Support System

Abstract A computer-assisted mathematical modeling system based on an advanced interactive methodology is presented. The system aims at the extraction of a trade-off between human mental models and regression-type models based on the use of numerical data, by using a flexible combination of statistical methods and graphtheoretical analysis. One of the main advantages of using this system is the facility for the structuring of both mental and mathematical models. An application of this interactive system to the development of simplified models of complex groundwater-crop growth systems for their subsequent incorporation in the decision support system is reported.

Decision Support System for Environmental Planning

Abstract Environmental assessment of mixed urban planning is getting difficult increasingly because of the complexity of their consequences on one hand and the diversity of value judgment on the other. Development of information systems is therefore strongly expected in order to integrate the results of domain-oriented researches and the methodology of decision-making. This paper proposes a direction of system development from the viewpoint of decision support based on an actual implementation of a system.

Computer Aided Modeling and Simulation in Process Control Systems

Abstract We have developed an interactive computer software for the systematic support to analysis and control of process systems. The support system have a universal application in data analysis, system structuring, statistical modeling and simulation. We have developed human-computer interfaces to acquire knowledge or judgment of the domain experts as well as techniques for storage and retrieval of knowledge or numerical data. After storing knowledge and numerical data, one can easily carry out the tasks of data analysis, system design and simulation in process control problems that are usually quite difficult without the support system.

Interactive Modeling Environment for Adaptive Process Control

Abstract This paper introduces some techniques with an interactive computer software that realize the interaction between modeling and simulation of the process control. With this software, the fuzzy auto-regressive exogenous models are built interactively and convincingly, and the simulation of predictive control iscarried out in such a way that the user can determine the design parameters and the reference trajectories freely and if necessary, the user can modify the parameters in the model.

Implementation of a Human-Friendly Systems Methodology for Intelligent Control System Modelling and Simulation

Abstract We have been developing an interactive computer software for the systematic support to modeling and simulation of intelligent control systems, based on a human-friendy systems methodology. The support system has a universal application in data analysis, system structuring, statistical and fuzzy modeling, and simulation, with the aid of human-computer interfaces to acquire knowledge or judgment of the domain experts. This paper presents our soft systems methodology and its implementation into the computer to develop intelligent process control systems. New technical proposals include a modeling method of fuzzy implication inference models and a design method of model predictive controllers.

Integrated Model Based System for Large Scale System Development

Abstract Soft systems methodologies together with highly developed computer technologies facilitate the realization of building effective decision support systems dealing with large and complex problems. Here, the development of the integrated model-based system is very important because most methodologies of decision-making depend on mathematical models. Using the environmental problem as an example, we propose a concept of the model-based system and introduce a prototype system under development.

Fuzzy Modeling and Simulation for an Environmental System

Abstract In model building of complex, large-scale systems, we often encounter the difficulty of structure identification and the lack of homogeneity in data. To cope with these problems, we are developing an interactive modeling support system to utilize objective information and subjective knowledge effectively. In this paper we propose a fuzzy modeling technique with a visualized stepwise clustering method, and a fuzzy simulation technique for reasonable scenario input and interpretation of the model behavior. We present an application of fuzzy modeling and simulation to an urban environmental system.