By Nauck D.
Unwell this thesis neuro-fuzzy equipment for info research are mentioned. We give some thought to facts research as a approach that's exploratory to a point. If a fuzzy version is to be created in a knowledge research strategy it is very important have studying algorithms to be had that help this exploratory nature. This thesis systematically offers such studying algorithms, that are used to create fuzzy platforms from information. The algorithms are specially designed for his or her power to provide interpretable fuzzy structures. it's important that in studying the most benefits of a fuzzy approach - its simplicity and interpretability - don't get misplaced. The algorithms are provided in the sort of manner that they could without problems be used for implementations. for instance for neuro-fuzzv facts analvsis the type svstem NEFCLASS is mentioned.
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Extra info for Data Analysis with Neuro-Fuzzy Methods
The rules are created on the ﬂy by processing the training data set twice. In the beginning, the rule base is either empty, or contains some rules provided as prior knowledge. In the ﬁrst cycle all the required antecedents are created. For each 52 CHAPTER 4. LEARNING FUZZY RULES FROM DATA point of the data set that is used for rule creation a combination of fuzzy sets is selected. This is done by ﬁnding, for each variable, the membership function that yields the highest degree of membership for the current input value.
The learning result should also be interpreted, and the insights gained by this should be used to restart the learning procedure to obtain better results if necessary. A neuro-fuzzy system supports the user in ﬁnding a desired fuzzy system based on training data, but it cannot do all the work. 1). Semantical problems will occur if neuro-fuzzy systems do not have mechanisms to make sure that all changes caused by the learning procedure are interpretable in terms of a fuzzy system. The learning algorithms should be constrained such that adjacent membership functions do not exchange positions, do not move from positive to negative parts of the domains or vice versa, have a certain degree of overlapping, etc.
The problem with solution (a) is that the models are sometimes not as easy to interpret as for example Mamdani-type fuzzy systems. , 1999], NEFCLASS [Nauck and Kruse, [Nauck, 1994b, Nu 1997b] and NEFPROX [Nauck and Kruse, 1999a] use solution (b) – they are Mamdani-type fuzzy systems and use special learning algorithms. , 1992, Vuorimaa, 1994], or fuzzy associative memories [Kosko, 1992]. Some approaches refrain from representing a fuzzy system in a network architecture. They just apply a learning procedure to the parameters of the fuzzy system, or explicitly use a neural network to determine them.
Data Analysis with Neuro-Fuzzy Methods by Nauck D.