Title :
Amalgamation of knowledge and data through fuzzy modelling
Author :
Marsh, C. ; McGowan, C.
Author_Institution :
Dept. of Production Technol., Massey Univ., Palmerston North, New Zealand
Abstract :
Typically, two sources of information about a system are available: some artisan knowledge and a sample of input-output data. This paper proposes a method for the amalgamation of these to synthesise a fuzzy model of the system. The artisan knowledge will likely be qualitative, of low resolution and accuracy whilst the data sample noisy and incomplete (not comprehensively covering the whole input space). A model derived from the union of these is potentially superior to one developed from either alone
Keywords :
fuzzy logic; inference mechanisms; knowledge acquisition; uncertainty handling; artisan knowledge; data sample; fuzzy inference; fuzzy modelling; incomplete data; input-output data; qualitative knowledge; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Gravity; Lead; Least squares approximation; Parameter estimation; Statistics; Vectors; Yield estimation;
Conference_Titel :
Artificial Neural Networks and Expert Systems, 1995. Proceedings., Second New Zealand International Two-Stream Conference on
Conference_Location :
Dunedin
Print_ISBN :
0-8186-7174-2
DOI :
10.1109/ANNES.1995.499487
