Adaptive Combination Rule Research Articles

Maximum Total Correntropy Diffusion Adaptation Over Networks With Noisy Links

Distributed estimation over networks draws much attraction in recent years. In many situations, due to imperfect information communication among nodes, the performance of traditional diffusion adaptive algorithms such as the diffusion LMS (DLMS) may degrade. To deal with this problem, several modified DLMS algorithms have been proposed. However, these DLMS based algorithms still suffer from biased estimation and are not robust to impulsive link noise. In this paper, we focus on improving the performance of diffusion adaptation with noisy links from two aspects: accuracy and robustness. A new algorithm called diffusion maximum total correntropy (DMTC) is proposed. The new algorithm is theoretically unbiased in Gaussian noise, and can efficiently handle the link noises in the presence of large outliers. The adaptive combination rule is applied to further improve the performance. The stability analysis of the proposed algorithm is given. Simulation results show that the DMTC algorithm can achieve good performance in both Gaussian and non-Gaussian noise environments.

Data fusion: a study of adaptative combinations with incomplete certainty qualification

PurposeThe first part of this issue investigated the properties of the adaptive rule initially proposed by Dubois and Prade given in the framework of possibility theory, when the certainty qualification is rather expressed in more general t‐norms and t‐conorms connectives. This led to two new family of adaptive rules expressed using residual implication and t‐conorm connective, respectively. The problem of addressing uncertain inputs has also been examined and a waved decomposition has been proposed in PII we study adaptative combinations with incomplete certainty qualification. However, another problem that arises when combining uncertain inputs consists of the relationship between the certainty attached to the inputs and the certainty attached to the output, conceptualized by the resulting distribution when using adaptive combination rule. In other words, how does the combination rule improves or deteriorates the certainty of the overall system? This paper seeks to address this issue.Design/methodology/approachThis paper fully addresses this issue and attempts to evaluate the combination rule from the certainty viewpoint attached to the result in comparison to initial certainty values attached to the inputs.FindingsEspecially, it has been proven that under certain hypotheses, the rule allows the user to hide the local certainties attached to the initial inputs, while highlighting only the certainty due to the lack of consistency among the sources.Originality/valueNew functional adaptative rules are put forward based on residual implicators and t‐conorm operators.

Data fusion: a study of adaptative combinations with incomplete certainty qualification

PurposeIn previous work, the algebraical properties of this rule and its relationship with other generalized operator was studied. In this paper, the aim is to focus on one of the previous steps, which consists in certainty qualification, and it is investigated how this factor influences the behavior of the induced combination rule.Design/methodology/approachDubois and Prade have proposed an adaptive combination rule that moves gradually from a conjunctive mode to a disjunctive mode as soon as the conflict between the sources increases. The proposal can be viewed as a result of some rational steps. This includes: conjunctive combination; re‐normalization of a subnormal result that may results from conjunctive operation where the lack of normalization is interpreted as a conflict; certainty qualification; restriction of the conflict influence; generalization to more than two sources.FindingsAlgebraical properties of the proposals have been investigated and illustrations of some special cases are highlighted and evaluated. Further studies continue in Part II.Originality/valueNew functional adaptive rules are put forward based on Residual implicators and t‐conorm operators.

Study of some algebraical properties of adaptive combination rules

Dubois and Prade, Data Fusion in Robotics and Machine Intelligence, Academic Press, New York, 1992, have proposed an adaptive combination rule that moves gradually from conjunctive mode to disjunctive mode as soon as the conflict between sources increases. The authors also proposed in Dubois and Prade, Control Eng. Practice 2 (1994) pp. 812–823; a generalization of the previous rule to more than two sources based upon restricting the number of reliable sources between an optimistic case and a pessimistic case. In this paper, we are particularly interested in studying these two rules in the light of some appealing algebraical properties. Particularly, we will study the link of these rules with commutativity, associativity, compromise, convexity, autoduality, ignorance and impossible cases. We also investigate their link with the MICA operators introduced by Yager [Fuzzy Sets and Systems 67 (1994) pp. 129–146]. Finally, we propose some extension of the rules.