By Nauck D.
Ailing this thesis neuro-fuzzy tools for information research are mentioned. We think of facts research as a procedure that's exploratory to some degree. If a fuzzy version is to be created in an information research strategy you will need to have studying algorithms to be had that help this exploratory nature. This thesis systematically offers such studying algorithms, which are used to create fuzzy structures from info. The algorithms are particularly designed for his or her potential to supply interpretable fuzzy structures. it's important that in studying the most benefits of a fuzzy procedure - its simplicity and interpretability - don't get misplaced. The algorithms are provided in this sort of approach that they could easily be used for implementations. for example for neuro-fuzzv facts analvsis the category svstem NEFCLASS is mentioned.
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Extra resources for Data Analysis with Neuro-Fuzzy Methods
Such a node-oriented representation of a fuzzy system is therefore often used for deﬁning a neural network-like learning algorithm based on adaptive weights that are attached to some of the connections. 1. The layers of the network are not fully connected, but the connections are selected such that they represent the rule base of the fuzzy system. 2(a) is as follows: • input layer: input variables, 32 CHAPTER 3. 2: Two fuzzy systems represented as a 5-layer feedforward network (a) and as a 3-layer feedforward network with shared weights (b) • antecedent layer: fuzzy sets used in linguistic terms of antecedents, • rule layer: fuzzy rules, • consequent layer: fuzzy sets used in linguistic terms of consequents, • output layer: ouput variables.
But if a pruning algorithm is applied to such a large rule base it is often possible to reduce the rule base and to retain the performance. In the following we will therefore concentrate on structure-oriented learning algorithms that use all variables in the beginning. 5). Compared to fuzzy clustering or hyperbox-oriented approaches, structure-oriented approaches to fuzzy rule generation have the following advantages: • they can create rule bases that can easily be interpreted linguistically • they are very fast and computationally inexpensive, • they are very easy to implement, • they can be used if the data contains numeric and non-numeric attributes, • they can be used if the data contains missing values.
No conﬂicts: there must be no rules in the rule base that have identical antecedents but diﬀerent consequents (complete contradiction). Only partial contradiction is acceptable. • No redundancy: no rule may appear more than once in the rule base. There must also be no rule whose antecedent is a subset of the antecedent of another rule. • Characteristics of fuzzy sets: fuzzy sets should be “meaningful” to the user of the fuzzy system. After training, the fuzzy partition of a variable should still be reasonably similar to the partition provided by the user.