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Author Berthold, Michael R. ♦ Huber, Klaus-Peter
Source CiteSeerX
Content type Text
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Hidden Unit ♦ Special Type ♦ Constructive Algorithm ♦ Considerable Interest ♦ Rectangular Basis Function Network ♦ Fuzzy Graph ♦ Understandable Knowledge ♦ Fuzzification Module ♦ Function Approximation ♦ Example Data ♦ Corresponding Membership Function ♦ Large Data ♦ Neural Network ♦ Fuzzy Point ♦ Rectangular Area ♦ Interesting Application ♦ System Approximates ♦ Simulation Model ♦ Dynamic Behaviour ♦ Adjustable Precision
Description Function approximation using example data has gained considerable interest in the past. One interesting application is the approximation of the behaviour of simulation models, called metamodelling. The goal is to approximate the behaviour as well as to extract some understandable knowledge about the simulation model. In this paper a combination of a special type of Neural Network (Rectangular Basis Function Network) with a (de--)fuzzification module is used. The resulting system approximates real valued functions with an adjustable precision. A constructive algorithm builds the network from scratch, resulting in a structure where each hidden unit represents a rectangular area with a corresponding membership function (or a fuzzy point). The underlying knowledge can be extracted from the network in form of a Fuzzy Graph. I. Introduction The dynamic behaviour of systems can be analyzed using simulation models. Unfortunately, often the results are only available in the form of large datas...
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 1998-01-01
Publisher Institution Proc. of Fuzzy-Neuro Systems