O. Yagishita, O. Itoh, and M. Sugeno. Application of fuzzy reasoning to the water purification process, in: M. Sugeno (editor). Industrial applications of fuzzy control, North Holland, Amsterdam, 1985, pp. 19--40. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
Fuzzy Logic Control - A Taxonomy of Demonstrated Benefits - Thomas, Armstrong-Helouvry (1995) (2 citations) (Correct)
....All these plants were well controlled by the associated FLC. Of the four, the Sludge Plant is the only pure simulation, the rest have been physically realized. The designer knowledge approach is typified in [46] in which a model car is controlled on an obstacle course, and to a lesser extent in [48], in which the feed rate of precipitating agents in a waste water treatment plant is regulated. The Model Car controller is a compound static maximizer with constraints, implementing a control policy which requires fle car to go as fast as possible without striking an obstacle or entering ....
....to the right (left) wall and increase speed if the forward direction is clear, the Model Car control system designers were able to constmct a working controller in short order. Here the designer is seen as artisan, applying his own experience to the control problem. In the waste water plant [48], the designers obtained a statistical regression relationship between rainfall and water turbidity, and used the regression to chose a candidate feed rate of precipitate agent. An FLC constructed with operator knowledge about measured and observed plant states fine tunes the feed rate to reduce ....
O. Yagishita, O. Itoh, and M. Sugeno, "Application of fuzzy reasoning to the water purification process," in IndustrialApplications of Fuz2y Control, M. Sugeno, Ed. Amsterdam: NoahHolland, pp. 19-39.
Application Of Knowledge Based Systems For Supervision.. - Haber, Haber, Alique.. (Correct)
.... control (type 2) 54] FLD for controlling the parameters of dynamic systems (type 3) 58] FLD for choosing the best compensator (type 4) 53,78] FLD derived from a multiple performance index (type 5) 114] FLD as a mathematical model for unknown or complex system dynamics (type 6) [113], Hierarchical FLD (type 7) 69] We must select the appropriate control and modeling schemes for accomplishing our goal. In this chapter we will focus on hierarchical fuzzy control schemes. Hierarchical control aims at achieving better system performance even in the case of unexpected changes ....
O.Yagishita, O. Itoh, M. Sugeno, Application of Fuzzy Reasoning to the water purification process, Industrial Applications of Fuzzy Control (M. Sugeno Ed.), pp. 19-38, Elsevier, 1985.
Fuzzy Path Tracking Control of a Vehicle - Bentalba, Hajjaji, Rachid (1998) (Correct)
....that conditions (23) and (24) in Theorem 1 are satisfied. To check the stability of the fuzzy control system, it has long been considered difficult to find a common positive definite matrix P satisfying the conditions of (23) and (24) Most of the time, a trial and error type of procedure is used [9,10]. In [13] a procedure to construct a common P is given for second order fuzzy systems. We pointed out in [14] that the common P problem for fuzzy control systems can be solved numerically. To do this, a very important observation is that the stability condition of Theorem 1 is expressed in LMI ....
O. Yagishita, O. Itoh and M. sugeno, (1985), ' Applications of Fuzzy reasoning to the water purification process', Industrial applications of fuzzy control North-Holland P 19-40.
A Stable Neuro-Fuzzy Controller for Output Tracking in.. - Tsai, Liu, Tseng, Lin (Correct)
....way to cope with uncertainties and to encode and approximate numerical functions. This methodology has received more recognition recently and there have been a number of successful applications of fuzzy methods to a wide variety of practical problems. For example, industrial process control [4] [5], robot control [6] and automobile transmission control [7] However, the majority of fuzzy systems developed so far are static and are designed in an iterative open loop fashion. Usually, the designer specifies a fuzzy rule base, and then enters an evaluation editing design loop [8] Both the ....
O. Yagishita, O. Ito, and M. Sugeno, "Application of fuzzy reasoning to the water purification process," in Industrial Application of Fuzzy Control, pp. 19--40, M. Sugeno Ed., Amsterdam: North-Holland, 1985.
Fuzzy Dynamic System and Fuzzy Linguistic Controller.. - Wang, Tyan (1994) (1 citation) (Correct)
....control theory because of the time lag between heat up and melting point. A water purification plant in Sagamihara, Japan employs a fuzzy rule based modeling and control strategy by incorporating a total of 47 fuzzy rules for controlling the amount of chlorine added to the raw water purified there [22]. R k Engine k Y Inference System Model Rule Base Figure 6: Architecture of Type 6 fuzzy linguistic system. HPIs Fuzzy Linguistic Sensoring Inputs k Y k Y k Y 2 n 1 Process1 Process2 Processn Inference Engine Rule Base Controller Figure 7: Architecture of Type 7 fuzzy linguistic ....
O. Yagishita, O. Itoh, and M. Sugeno, Application of Fuzzy Reasoning to the Water Purification Process, pp. 19--39. Elsevier Science Publishers, Amsterdam, 1985.
Gaussian Membership Functions Are Most Adequate - In Representing Uncertainty (1992) (Correct)
No context found.
O. Yagishita, O. Itoh, and M. Sugeno. Application of fuzzy reasoning to the water purification process, in: M. Sugeno (editor). Industrial applications of fuzzy control, North Holland, Amsterdam, 1985, pp. 19--40.
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC