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a particular domain element can simultaneously belong to several sets, with different degrees of membership. For instance, t = 20 o C belongs to the set of High temperatures with membership 0.4 and to the set of Medium temperatures with membership 0.2. This gradual transition from membership to non-membership facilitates a smooth outcome of the reasoning (deduction) with fuzzy if-then rules; in fact a kind of interpolation. Figure 1: Partitioning of the temperature domain into three fuzzy sets. Fuzzy logic systems are a suitable framework for representing qualitative knowledge, either provided by human experts (knowledge-based fuzzy control) or automatically acquired from data (rule induction, learning). In the latter case, fuzzy clustering algorithms are often used to partition data into groups of similar objects. Fuzzy sets and if-then rules are then induced from the obtained partitioning, see Figure 2. In this way, a compact, qualitative summary of a large amount of possibly high-dimensional data is generated. To increase the flexibility and representational power, local regression models can be used in the conclusion part of the rules (the so-called Takagi-Sugeno fuzzy system). Figure 2: Fuzzy clustering can be used to extract qualitative if-then rules from numerical data. Artificial Neural Networks are simple models imitating the function of biological neural systems. While in fuzzy logic systems, information is represented explicitly in 8
the form of if-then rules, in neural networks, it is implicitly ‘coded’ in the network and its parameters. In contrast to knowledge-based techniques, no explicit knowledge is needed for the application of neural nets. Their main strength is the ability to learn complex functional relations by generalizing from a limited amount of training data. Neural nets can be used, for instance, as (black-box) models for nonlinear, multivariable static and dynamic systems and can be trained by using input-output data observed on the system. Figure 3: Multi-layer artificial neural network. Figure 3 shows the most common artificial feedforward neural network, which consists of several layers of simple nonlinear processing elements called neurons, inter-connected through adjustable weights. The information relevant to the inputoutput mapping of the net is stored in these weights. There are many other network architectures, such as multi-layer recurrent nets, Hopfield networks, or self-organizing maps. Neural networks and fuzzy systems are often combined in neuro-fuzzy systems, which effectively integrate qualitative rule-based techniques with datadriven learning. Genetic algorithms are randomized optimization techniques inspired by the principles of natural evolution and survival of the fittest. Candidate solutions to the problem at hand are coded as strings of binary or real numbers. The fitness (quality, performance) of the individual solutions is evaluated by means of a fitness function, which is defined externally by the user or another higher-level algorithm. The fittest individuals in the population of solutions are reproduced, using genetic operators like the crossover and mutation. In this way, a new, generation of fitter individuals is obtained and the whole cycle starts again (Figure 4). Genetic algorithms proved to be effective in searching high-dimensional spaces and have found applications in a large number of domains, including the optimization of model or controller structures, the tuning of parameters in nonlinear systems, etc. Current Research and Applications at Regeltechniek Research in the area of intelligent control has a long tradition at Regeltechniek. The pioneering work prof. van Nauta Lemke and his co-workers was focused on knowledge-based fuzzy control and fuzzy decision making. More recently, the 9
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Page 3 and 4: Van het bestuur en redactie Na de a
Page 5 and 6: Oprichting Delft Center for Systems
Page 7: What is intelligent control? door R
Page 11 and 12: nonlinear identification and patter
Page 13 and 14: Introductie Rob Kemper door Rob Kem
Page 15 and 16: meer plaats nemen in een simulator
Page 17 and 18: door Hans Roubos Abstract - Dit art
Page 19 and 20: maken voor de beschikbare data, ech
Page 21 and 22: als ook CA degradatie een belangrij
Page 23 and 24: Internet Links: Download proefschri
Page 25 and 26: elektriciteit geeft. Niet alle afne
Page 27 and 28: In de praktijk wordt de programmave
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