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Different techniques to automatically evolve fuzzy systems from data are described in Angelov and Filev, ; Angelov, ; Angelov and Zhou, ; Angelov, Because the evolving TS eTS fuzzy system can be represented as a neural network Angelov and Filev, as illustrated in Figure 3 eTS can also be considered as a self-developing or evolving neuro-fuzzy system. It is the structural framework that can be used to solve a range of problems offering flexibility, adaptation, robustness, and improved precision with small computational efforts due to the recursive algorithms.

The main group of problems that can be solved by eTS include, but are not limited to:. Fuzzy rule-based classifiers with rules that are evolved from streaming data are called evolving fuzzy classifiers EFC. EFC does not need to know beforehand in how many classes the data will be classified — new classes can be introduced during the learning process Angelov and Zhou, Generally, there can be three types of EFC:. One possible EFC which has class labels as outputs is eClass0 Angelov and Zhou, which follow the conventional structure of fuzzy classifiers Kuncheva, The main difference of eClass0 from a conventional fuzzy classifier is its evolving structure.

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It is computationally very light and is suitable for robotics or as a first step of a more complex classification scheme. Additional advantage of eClass0 is its high interpretability.

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This scheme combines several single-model eClass1 classifiers one for each class. It provides a decoupling of the antecedent part of the fuzzy rule-based classifier if compared to eClass1 MIMO and thus can bring better performance higher classification rates for problems with more than two classes.


Fuzzy control has proven its viability through numerous applications ranging from nuclear power plants safety DaRuan, to home appliances such as washing machines Driankov et al. Adaptive control is another well established and recognised area that is widely used in industry for over three decades Astrom and Wittenmark, One of the problems of controllers and in particular fuzzy controllers is their design and tuning.

Because the environment in which industrial plants and controllers operate are dynamically changing off-line design and tuning cannot provide the flexibility that is required. Adaptive control theory and adaptive systems in general provides a way out of this problem that is based on constant adaptation of the parameters of the usually linear systems controllers.

Real problems, however, are usually highly non-linear, non-stationary and, therefore, adaptive control theory has obvious limitations. Evolving Fuzzy Rule-based Controllers Angelov, ; Angelov, combine the flexibility and non-linear nature of fuzzy TS systems with the advantages of well established adaptive control schemes such as:. Indirect learning was proposed by Psaltis et al. The NN was proposed to be pre-trained off line in such a way that to model the inverse dynamics of the plant.

Then this NN is used in an adaptive control scheme to produce control action that should bring the output of the plant to the required reference value if the inverse model of the dynamic of the plant is perfect. If use an EFS instead of the NN in the same scheme the resulting scheme can be re-trained automatically on-line during the control process.

Training procedure

Moreover, not only parameters of the controller can be adapted, but also its structure can evolve. An alternative for the use of EFS in control schemes is to model the plant dynamics Zhou and Angelov, An important issue of the controllers design is the stability. The problem of stability of the on line structure and parameters learning algorithms when using an evolving neuro-fuzzy recurrent network has been studied in Rubio, One very important area of application of EFS is the development of evolving intelligent sensors.

Another real application of eSensor is for monitoring quality of products in an oil refinery courtesy of Dr. Macias Hernandez et al. Another big area of application is robotics and autonomous systems. Autonomous systems by the virtue of their definition should be able to act without human intervention in unknown possibly harsh environments.

It is therefore essential that they are able to learn and to acquire new knowledge on-line. EFS are well suited to address this task. Some examples of application of EFS include:. Another emerging area of application of EFS is the Internet-based information mining.

Limitations of contemporary search engines are well known — the result is usually an unstructured list of items that often does not include the item that is most needed by the user. An area of constant interest for the industry is the fault detection and prognostics. EFS provide a powerful tool for on-line monitoring analysis and prediction of the health of machines and systems in general Filev and Tseng, Plamen Angelov's website.

Fuzzy neural systems , Fuzzy systems , Genetic algorithms. Plamen Angelov , Scholarpedia, 3 2 Jump to: navigation , search.

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Post-publication activity Curator: Plamen Angelov Contributors:. Nick Orbeck. Benjamin Bronner. Sponsored by: Eugene M. Category : Fuzzy systems. Namespaces Page Discussion. Views Read View source View history. This book comprises several evolving fuzzy systems approaches which have emerged during the last decade and highlights the most important incremental learning methods used.

The second part is dedicated to advanced concepts for increasing performance, robustness, process-safety and reliability, for enhancing user-friendliness and enlarging the field of applicability of EFS and for improving the interpretability and understandability of the evolved models. The third part underlines the usefulness and necessity of evolving fuzzy systems in several online real-world application scenarios, provides an outline of potential future applications and raises open problems and new challenges for the next generation evolving systems , including human-inspired evolving machines.

The book includes basic principles, concepts, algorithms and theoretic results underlined by illustrations. It is dedicated to researchers from the field of fuzzy systems, machine learning, data mining and system identification as well as engineers and technicians who apply data-driven modeling techniques in real-world systems. JavaScript is currently disabled, this site works much better if you enable JavaScript in your browser.

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  • Engineering Computational Intelligence and Complexity. Complete overview of Methodologies and Applications of Evolving Fuzzy Systems Presents potential new applications and open problems Written by a leading expert in the field see more benefits. Buy eBook. Buy Hardcover.