Compatible multi-model output fusion for complex systems modeling via set operation-based focus data identification

Abstract

Is an inferior model completely useless in complex systems modeling? This study proposes a novel multi-model output fusion approach that makes the best of the outputs from multi-models, i.e., using the limited superior results from inferior models to compensate for certain inferior results from even superior models. For fusing the outputs from multi-models, the weight assigned to each output is calculated based on two factors, namely the accuracy of each model and the similarity between the testing input and the focus data used for constructing the respective model. Specifically for similarity calculation, the focus data list is identified based on set operations. There are three theoretical contributions of this study, namely accuracy-and-similarity-based weight calculation, the set-operation-based similarity calculation which is an addition to traditional distance-based calculation, and the high compatibility of the proposed approach which is independent from any baseline approach. A practical case of overall reconnaissance capability evaluation of the Unmanned Aerial Vehicle (UAV) swarm is studied for validation. Primary results indicate that the proposed approach can outperform two single models: the backpropagation neural network (BPNN) and the Radial Basis Function (RBF) neural network. Further validations demonstrate that the proposed approach also outperforms multi-model output fusion with equal weights, without model accuracy, with varied focus data percentages ranging from 0.1 to 0.9. More importantly, the proposed approach remains effective in four different conditions of multi-model outputs fusion with improvements from 9.58 % to 38.03 %.