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Modern information fusion systems essentially associate decision-making processes with multi-sensor systems. Precise decision-making processes depend upon aggregating useful information extracted from large numbers of messages or large datasets; meanwhile, the distributed multi-sensor systems which employ several geographically separated local sensors are required to provide sufficient messages or data with similar and/or dissimilar characteristics. These kinds of information fusion techniques have been widely investigated and used for implementing several information retrieval systems. However, the results obtained from the information fusion systems vary in different situations and performing intelligent aggregation and fusion of information from a distributed multi-source, multi-sensor network is essentially an optimization problem. A flexible and versatile framework which is able to solve complex global optimization problems is a valuable alternative to traditional information fusion. Furthermore, because of the highly dynamic and volatile nature of the information flow, a swift soft computing technique is imperative to satisfy the demands and challenges. In this paper, a nonlinear aggregation based on the Choquet integral (NACI) model is considered for information fusion systems that include outliers under inherent interaction among feature attributes. The estimation of interaction coefficients for the proposed model is also performed via a modified algorithm based on particle swarm optimization with quantum-behavior (QPSO) and the high breakdown value estimator, least trimmed squares (LTS). From simulation results, the proposed MQPSO algorithm with LTS (named LTS-MQPSO) readily corrects the deviations caused by outliers and swiftly achieves convergence in estimating the parameters of the proposed NACI model for the information fusion systems with outliers.

A nonlinear multi-regression based on fuzzy integral (NAFI) model that include outliers under inherent interaction among feature attributes is considered in this paper. The modeling of the proposed model is also performed via a modified algorithm base on particle swarm optimization with quantumbehavior (MQPSO) and the high breakdown value estimator, least trimmed squares (LTS). That is, we successfully integrate mechanisms of the genetic algorithm and the simulated annealing into the QPSO algorithm to estimate parameters of the NAFI model; meanwhile, the LTS estimator is also introduced to filter out outliers. From simulation results, the proposed MQPSO algorithm with LTS estimator (named QPSO-GS) readily corrects the deviation caused by outliers and swiftly achieves convergences on estimating the parameters of the proposed NAFI model with outliers.Keywords-Fuzzy integral; nonlinear multi-regression based on fuzzy integral; particle swarm optimization with quantum-behavior; least trimmed squares.