Showing papers by "Robert Babuska published in 1996"
TL;DR: Methods for constructing fuzzy models from process data are reviewed, and attention is paid to the choice of a suitable fuzzy model structure for the identification task.
288 citations
TL;DR: Direct fuzzy control of the Mamdani type, fuzzy supervised PI control and predictive control based on fuzzy and neural models were applied to a nonlinear pressure process to compare the development time, type and amount of prior information needed for the controller design, the tuning requirements and the closed-loop performance.
51 citations
08 Sep 1996
TL;DR: An automated method is proposed that reduces the number of fuzzy sets in the model using a similarity measure, and a comprehensive linguistic description is obtained by linguistic approximation.
Abstract: In fuzzy rule based models, redundancy may be present in the form of similar fuzzy sets, especially if the models are acquired from data by using techniques like fuzzy clustering or gradient learning. The result is an unnecessarily complex and a less effective linguistic description of the system. An automated method is proposed that reduces the number of fuzzy sets in the model using a similarity measure. A comprehensive linguistic description is obtained by linguistic approximation. A numerical example demonstrates the approach.
28 citations
08 Sep 1996
TL;DR: An experimental comparison of different cost functions is presented, using an example of container crane control, and the conventional quadratic criterion is compared with a conjunctive aggregation of fuzzy goals.
Abstract: The use of fuzzy goals and fuzzy constraints in predictive control allows for a more flexible aggregation of the control objectives than the usual weighted sum of squared errors. A multistage decision making algorithm is applied to compute the optimal control action. Compared to the standard quadratic objective function, with the fuzzy decision making approach, the designer has more freedom in specifying the desired process behavior. This paper presents an experimental comparison of different cost functions, using an example of container crane control. The conventional quadratic criterion is compared with a conjunctive aggregation of fuzzy goals. The results show that a better performance can be achieved by using fuzzy goals. On the other hand, the optimization problem associated with the multistage decision making procedure has higher computational demands.
23 citations
08 Sep 1996
TL;DR: A new inference method based on a smoothing maximum function is proposed, which guarantees smoothness of the model output to a desired degree, boundedness ofThe output gradient at each point by the local gradients of the rule consequences and also improves the accuracy of the TS model.
Abstract: Some undesirable properties of the standard Takagi-Sugeno (TS) inference method are discussed in relation to an analysis of the TS model and its approximation accuracy. A new inference method based on a smoothing maximum function is proposed. This method guarantees smoothness of the model output to a desired degree, boundedness of the output gradient at each point by the local gradients of the rule consequences and also improves the accuracy of the TS model, as demonstrated in the given numerical examples.
19 citations
Proceedings Article•
01 Jan 1996
16 citations
TL;DR: A fuzzy model of the kinetics of enzymatic Penicillin-G conversion has been developed from data using If-Then rules that describe qualitatively how the enzyme kinetics depend on the concentrations of the components involved in the conversion.
9 citations
TL;DR: A novel predictive control scheme is proposed that circumvents the real-time problems by combining a predictive control strategy based on discrete branch-and-bound optimization with a fast control law derived by inverting a fuzzy model of the plant.
5 citations
TL;DR: The main goal of this study was to compare the three different fuzzy control concepts in terms of the development time, type and amount of prior information needed for the controller design, the tuning requirements and the closed loop performance.
5 citations
28 Jan 1996
TL;DR: A new method based on fuzzy logic is proposed for steady-state security analysis that can classify the power system's operating condition into normal/alert state and provides information about the severity and pertinent contingencies that may cause insecurity.
Abstract: A new method based on fuzzy logic is proposed for steady-state security analysis. The method has been developed for the Dutch 380 kV transmission system. The security status of the power system's operating condition is recognized from data from a SCADA system, from global knowledge and some operation characteristics of the power system. The method can classify the power system's operating condition into normal/alert state. For the alert state, it provides information about the severity and pertinent contingencies that may cause insecurity. The security assessment can follow different strategies such as risk awareness. A risk index shows the risk.
4 citations