By Nauck D.
Ailing this thesis neuro-fuzzy equipment for info research are mentioned. We examine info research as a method that's exploratory to a point. If a fuzzy version is to be created in an information research strategy it is very important have studying algorithms to be had that aid this exploratory nature. This thesis systematically offers such studying algorithms, that are used to create fuzzy structures from facts. The algorithms are specially designed for his or her strength to provide interpretable fuzzy structures. it is vital that in studying the most benefits of a fuzzy procedure - its simplicity and interpretability - don't get misplaced. The algorithms are provided in this type of approach that they could simply be used for implementations. for example for neuro-fuzzv facts analvsis the class svstem NEFCLASS is mentioned.
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Additional resources for Data Analysis with Neuro-Fuzzy Methods
Usually it is not important that, for example, the term approximately zero be represented by a symmetrical triangular fuzzy set with support [−1, 1]. Interpretability means that the users of the model can accept the representation of the linguistic terms, more or less. The representation must roughly correspond to their intuitive understanding of the linguistic terms. It is more important that the rule base is small and thus comprehensible. It is also useful to note that interpretability itself is a fuzzy and subjective concept.
In this thesis we are interested in creating an interpretable fuzzy system for data analysis. 4). 12. 4 Interpretable Fuzzy Systems for Data Analysis This thesis is about neuro-fuzzy systems in data analysis. As we have seen in the previous chapter, neuro-fuzzy systems are essentially fuzzy systems endowed with learning capabilities inspired by neural networks. We must therefore consider what advantages there are in using fuzzy systems for data analysis? Fuzzy systems conveniently allow us to model a partially known dependency between independent and dependent variables by using linguistic rules.
Ym }. (ii) W , the network structure, is a partial mapping from U × U → F(IR) and is given by (i) if u = xi , v = Rj µj (k) W (u, v) = νj if u = Rj , v = yk undeﬁned otherwise where 1 ≤ i ≤ n, 1 ≤ j ≤ r and 1 ≤ k ≤ m. In addition every two connections with weights W (u, v) and W (u , v ) become coupled connections, if W (u, v) = W (u , v ) and (u = u , u, u ∈ U1 ∧ v = v , v, v ∈ U2 ) or (u = u , u, u ∈ U2 ∧ v = v , v, v ∈ U3 ) holds. If W (u, v) and W (u , v ) are coupled, then if W (u, v) is modiﬁed by a learning algorithm, W (u , v ) is modiﬁed in the same way and vice versa.
Data Analysis with Neuro-Fuzzy Methods by Nauck D.