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- Lucian Busoniu, Robert Babuska, Bart De Schutter
- IEEE Trans. Systems, Man, and Cybernetics, Part C
- 2008

— Multi-agent systems are rapidly finding applications in a variety of domains, including robotics, distributed control, telecommunications, and economics. The complexity of many tasks arising in these domains makes them difficult to solve with preprogrammed agent behaviors. in the field is the formal statement of the multi-agent learning goal. Different… (More)

- Magne Setnes, Robert Babuska
- IEEE Trans. Systems, Man, and Cybernetics, Part B
- 1999

A novel approach to nonlinear classification is presented, in the training phase of the classifier, the training data is first clustered in an unsupervised way by fuzzy c-means or a similar algorithm. The class labels are not used in this step. Then, a fuzzy relation between the clusters and the class identifiers is computed. This approach allows the number… (More)

- János Abonyi, Robert Babuska, Ferenc Szeifert
- IEEE Trans. Systems, Man, and Cybernetics, Part B
- 2002

The construction of interpretable Takagi-Sugeno (TS) fuzzy models by means of clustering is addressed. First, it is shown how the antecedent fuzzy sets and the corresponding consequent parameters of the TS model can be derived from clusters obtained by the Gath-Geva (GG) algorithm. To preserve the partitioning of the antecedent space, linearly transformed… (More)

- A. Hegyi, D. Girimonte, R. Babuska, B. De Schutter
- 2006 IEEE Intelligent Transportation Systems…
- 2006

We present a comparison for several filter configurations for freeway traffic state estimation. Since the environmental conditions on a freeway may change over time (e.g., changing weather conditions), parameter estimation is also considered. We compare the performance of the extended Kalman filter and the unscented Kalman filter for state estimation,… (More)

- Magne Setnes, Robert Babuska, Henk B. Verbruggen
- IEEE Trans. Systems, Man, and Cybernetics, Part C
- 1998

— This article is a reaction to recent publications on rule-based modeling using fuzzy set theory and fuzzy logic. The interest in fuzzy systems has recently shifted from the seminal ideas about complexity reduction toward data-driven construction of fuzzy systems. Many algorithms have been introduced that aim at numerical approximation of functions by… (More)

The figure omits the goal and obstacle mentioned in the text (these elements are shown later, in Figure 1.5). The correct figure is given here: state (position) x k action (step) u k nexttstate x k+1

- Jelmer van Ast, Robert Babuska, Bart De Schutter
- 2008 IEEE Congress on Evolutionary Computation…
- 2008

Ant Colony Optimization (ACO) has proven to be a very powerful optimization heuristic for Combinatorial Optimization Problems (COPs). It has been demonstrated to work well when applied to various NP-complete problems, such as the traveling salesman problem. In this paper, an ACO approach to optimal control is proposed. This approach requires that a… (More)

- Johannes A. Roubos, Stanimir Mollov, Robert Babuska, Henk B. Verbruggen
- Int. J. Approx. Reasoning
- 1999

Nonlinear model-based predictive control (MBPC) in multi-input multi-output (MIMO) process control is attractive for industry. However, two main problems need to be considered: (i) obtaining a good nonlinear model of the process, and (ii) applying the model for control purposes. In this paper, recent work focusing on the use of Takagi±Sugeno fuzzy models in… (More)

- Robert Babuska, Henk B. Verbruggen
- Annual Reviews in Control
- 2003

Most processes in industry are characterized by nonlinear and time-varying behavior. Nonlinear system identification is becoming an important tool which can be used to improve control performance and achieve robust fault-tolerant behavior. Among the different nonlinear identification techniques, methods based on neuro-fuzzy models are gradually becoming… (More)

- Lucian Busoniu, Robert Babuska, Bart De Schutter
- 2006 9th International Conference on Control…
- 2006

Multi-agent systems are rapidly finding applications in a variety of domains, including robotics, distributed control, telecommunications, economics. Many tasks arising in these domains require that the agents learn behaviors online. A significant part of the research on multi-agent learning concerns reinforcement learning techniques. However, due to… (More)