Informaton fusion by fuzzy set operation and genetic algorithms

This paper describes novel multisensor information fusion methods based on fuzzy logic and genetic algorithms. Unlike most fuzzy logic-based systems that perform reasoning by fuzzy IF-THEN rules, the reasoning in this work takes place by means of fuzzy aggregation connectives. These connectives are capable of combining information not only by union and intersection used in traditional set theories but also by compensatory connectives that better minic the human reasoning process. The particular connective used in this work for the purpose of data fusion is the generalized mean aggregation connective. The distinctive feature of this information fusion method is that the optimal parameters of the aggregation connective are automatically found by a genetic algorithm. Both elitist and nonelitist strategies for genetic algorithms are investigated. Two different methods are developed. The first technique performs aggregation of evidence from two sensors in one step; if there are more sensors, information from the next sensor is fused with the data already aggregated. The second technique developed performs one step fusion from all the sensors available. The techniques devised are tested on a vibration monitoring problem and the results are described.