Causal relevance to improve the prediction accuracy of dynamical systems using inductive reasoning
In this paper, the concept of causal relevance (CR) is introduced in the context of the fuzzy inductive reasoning (FIR) modelling and simulation methodology. The idea behind CR is to quantify how much influence each system variable has, from the spatial and temporal points of view, on the prediction of the output. This paper introduces the FIR inference engine, and describes how it can be improved by means of the CR concept, helping to reduce uncertainty during the forecasting stage. The FIR inference engine is based on the k-nearest neighbour classification rule, commonly used in the field of pattern recognition, and uses a Euclidean distance measure to compute the distance between neighbours. In this paper, a weight-Euclidean distance measure is proposed that is able to find better quality neighbours by using the CR concept. Applications from different fields are studied in the light of the prediction process, and a comparison between the accuracy of the predictions obtained when using the classical inference engine and the CR option is performed. The results obtained from this research show that FIR predictions are more accurate and precise when the CR option is used, especially for systems where classical FIR forecasting performs rather poorly.
causal relevance fuzzy inductive reasoning forecasting fuzzy systems uncertainty reduction k-nearest neighbour rule qualitative simulation