Constructions of fuzzy rules and a fuzzy simulator based on the control techniques of Hiroshima Toji (Experts)

Tsuchiya, Yoshinobu; Koizumi, Jun–ichi; Suenari, Kazuo; Teshima, Yoshiharu; Nagai, Shiro

doi:10.1016/0922-338x(90)90232-l

Cited by 11 publications

(2 citation statements)

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“…An interesting feature of this study was that the membership functions were modified as the fermentation progressed, reflecting changes in cell morphology and physiology, which in turn affected productivity. Temperature control with varying membership functions has been supported by other investigators of sake brewing Tsuchiya et al 1990), thus underlining the pivotal role this has.…”

Section: Fuzzy Modeling and Controlmentioning

confidence: 97%

Intelligent Models of the Quantitative Behavior of Microbial Systems

Patnaik

2008

Food Bioprocess Technol

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Under different environmental conditions, microbial systems display complex behavioral patterns that are difficult to express quantitatively by mechanistic methods. Therefore, two alternate approaches based on different forms of intelligence have emerged. One approach uses methods of artificial intelligence (AI) such as neural networks, expert systems, and genetic algorithms to describe cellular behavior. The second methodology, which leads to the class of cybernetic models, relies on intelligence postulated to be possessed by the cells themselves. While both AI and cybernetic methods have been effective in many applications where mechanistic models are inadequate, all three methods have strengths and weaknesses. This recognition has led to the development of hybrid systems that combine two or more approaches for different aspects of a microbial system. However, the optimum design of hybrid systems still remains heuristic. The rationale and the developments from mechanistic to hybrid models are discussed here, and it is suggested that eventually a truly intelligent system should be self-evolving to maintain itself at the optimum configuration at all times.

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Section: Fuzzy Modeling and Controlmentioning

confidence: 97%

Intelligent Models of the Quantitative Behavior of Microbial Systems

Patnaik

2008

Food Bioprocess Technol

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“…One of the most typical examples of the effective use of fuzzy theory on process control is fuzzy control of sake mashing processes (1)(2)(3)(4)8). Recently Kobayashis group (5-7) has extensively studied fuzzy control of mashing process of a sake, of particularly high quality and traditionally appreciated.…”

Section: Process Diagnosis and Controlmentioning

confidence: 99%

Computer Application in Bio-Process Engineering in Japan

Yoshida

1998

IFAC Proceedings Volumes

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Recent development of research conducted in Japan into computer applications in bio-pro cess engineering is summarized, specially focusing on the application of knowledge engineering approaches to bio-process control. The issues of methodological investigation, their applications in process development, and examples of industrial applications of expert systems, artificial biotechnologia 4 (47) '99 11ÓToshiomi Yoshida neural networks, fuzzy theory, and genetic algorithm are thoroughly reviewed, and some repre sentative recent works are introduced more extensively.

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