Showing papers by "Robert Babuska published in 2000"

Identification and Control of Nonlinear Systems Using Fuzzy Hammerstein Models

Abstract: This paper addresses the identification and control of nonlinear systems by means of Fuzzy Hammerstein (FH) models, which consist of a static fuzzy model connected in series with a linear dynamic m...

Virtual sensor for fault detection and isolation in flight control systems - fuzzy modeling approach

Abstract: A virtual sensor for normal acceleration has been developed and implemented in the flight control system of a small commercial aircraft. The inputs of the virtual sensor are the consolidated outputs of dissimilar sensor signals. The virtual sensor is a fuzzy model of the Takagi-Sugeno type and it has been identified from simulated data, using a detailed, realistic Matlab/Simulink/sup TM/ model used by the aircraft manufacturer. This virtual sensor can be applied to identify a failed sensor in the case that only two real sensors are available and even to detect a failure of the last available sensor.

Incorporating prior knowledge in fuzzy model identification

Abstract: This paper presents an algorithm for incorporating a priori knowledge into data-driven identification of dynamic fuzzy models of the Takagi-Sugeno type. Knowledge about the modelled process such as its stability, minimal or maximal static gain, or the settling time of its step response can be translated into inequality constraints on the consequent parameters. By using input-output data, optimal parameter values are then found by means of quadratic programming. The proposed approach has been applied to the identification of a laboratory liquid level process. The obtained fuzzy model has been used in model-based predictive control. Real-time control results show that, when the proposed identification algorithm is applied, not only are physically justified models obtained but also the performance of the model-based controller improves with regard to the case where no prior knowledge is involved.

Construction of Fuzzy Systems - Interplay between Precision and Transparency

Abstract: In recent years, we have witnessed a strong emphasis on high performance and precision of fuzzy sys- tems. Many publications are focused on data driven approaches, i.e., the construction of fuzzy systems from data and applying them in areas like data mining, pattern recognition, prediction or control. In such applications, fuzzy system inevitably must be compared with other inductive methods, like neural networks, machine learning or statistical tech- niques. The most prominent feature that distinguishes fuzzy systems from many other techniques is their transparency and interpretability. Fuzzy models are ideally suited for explaining solutions to users. In the current literature, however, surprisingly little attention has been devoted to the study of the interplay between interpretability and precision. These objectives are to a certain degree conflicting and attention must be paid to both of them. In this paper, the interpretation and transparency issues are first discussed with regard to the various parameters (degrees of freedom) in the Mamdani and Takagi-Sugeno models. An overview is also given of methods for improving the transparency and interpretability of fuzzy systems induced from data. These include the use of similarity measures, semantic constraints and multi-objective optimization.

Fuzzy Clustering Algorithms with Applications to Rule Extraction

Abstract: An overview of fuzzy clustering is given. The chapter starts with the definition of the basic notions of clustering and with a brief review of different approaches. Then, the focus is on fuzzy clustering based on the minimization of an objective function of the c-means type. Different algorithms are presented, including the Gustafson-Kessel algorithm, maximum-likelihood clustering, fuzzy c-varieties, c-regression models and possibilistic c-means. The choice of the different user-defined parameters is discussed and illustratives examples are given. Finally, the use of fuzzy clustering for rule extraction is addressed.

Hybrid modeling of fed-batch bioprocesses: Combination of physical equations with metabolic networks and black-box kinetics

Abstract: Ahybrid (grey-box) modeling technique is presented that facilitates effective development of models for fed-batch bioprocess. In the hybrid model, differential equations based on mass balances are combined with a metabolic network describing the chemical reactions inside the cell (white-box part of the model) with fuzzy or neural models that represent the dynamics kinetics (black-box part of the model). Parameters of these black-box models are tuned by using experimental data. The approach has been ap-plied to the clavulanic acid production by Streptomyces clavuligerus and results obtained with fuzzy logic, neural networks and empirical (Monod type) kinetic models are compared.

Fuzzy modeling and model based control with use of a priori knowledge

Abstract: In order to solve the problem of model based control arising from the process model has to be obtained by using small amount and different type of available information, a fuzzy modeling framework has been developed for the utilization of a priori knowledge. The proposed modeling approach transforms the different types of information into the structure of the model (fuzzy rule base), constraints defined on the parameters and variables, dynamic local model or data, and steady-state data or model. This modeling step is followed by an optimization procedure based on these transformed information. The paper describes one element of this framework that was developed to use prior knowledge in constrained adaptation of the rule consequences of Takagi-Sugeno fuzzy models. Experimental results have been obtained for a laboratory setup consisting of two cascaded tanks. It has been shown that by using constrained adaptation, good control performance can be achieved for a nonlinear, time-varying process.

Design and Application of Block-Oriented Fuzzy Models — Fuzzy Hammerstein Model

Abstract: Recently, data driven fuzzy modelling has drawn a great deal of attention not only from academia but also from industry. However, a dynamic fuzzy model might be difficult to develop when training data do not contain sufficient information about the process nonlinearity. In order to avoid this problem and to reduce the model complexity, this paper presents a fuzzy Hammerstein (FH) modelling approach, where a static fuzzy model is integrated with a linear dynamic model in series. A constrained recursive least-squares algorithm is used for the simultaneous identification of the steady-state and the dynamic part of the FH model. To demonstrate the proposed modelling approach, an electrical water-heater process is used as an example.