Practical neural network recipes in C++
Practical neural network recipes in C++
Fuzzy identification from a grey box modeling point of view
Fuzzy model identification
Integrating membership functions and fuzzy rule sets from multiple knowledge sources
Fuzzy Sets and Systems
A hypertube as a possible interpolation region of a neural model
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
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Sometimes, when a fuzzy model is created, two knowledge sources are available --- a data set describing the behavior of an analyzed system and a set of expert rules. In such case three possible approaches to the modelling process are possible: utilize a data set, utilize a set of expert rules or utilize both knowledge sources simultaneously. According to the author of this paper, the third approach is the most reasonable because by joining expert rules with rules created on the basis of a data set, fuzzy model inherits the specific advantages of both rule sets. The aim of this paper is to verify this statement and to illustrate (on a real example) the superiority of a model composed of expert and "data" rules over a model composed only of expert rules and a model composed only of "data" rules. The paper presents a writer's method of combining two rule bases and its application for building a combined fuzzy model of a real economic problem.