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Rule reduction is one of the focuses of numerous researches on belief-rule-based system, in some cases, too many redundant rules may be a concern to the rule-based system. Though rule reduction methods have been widely used in the belief-rule-based system, extended belief-rule-based system, which is an expansion of belief-rule-based system, still lacks methods to reduce and train rules in the extended belief rule base (EBRB). To this end, this paper proposes an EBRB reduction and training method. Based on the density-based spatial clustering applications with noise (DBSCAN) algorithm, a new EBRB reduction method is proposed, where all the rules in the EBRB will be visited and rules within the distance of the fusion threshold will be fused. Moreover, the EBRB training method using parameter learning, which uses a set of training data to train the parameters of EBRB, is also proposed to improve the accuracy of the EBRB system. Two case studies of regression and classification are used to illustrate the feasibility and efficiency of the proposed EBRB reduction and training method. Comparison results show that the proposed method can effectively downsize the EBRB and increase the accuracy of EBRB system.

The fading factor exerts a significant role in the strong tracking idea. However, traditional fading factor introduction method hinders the accuracy and robustness advantages of current strong-tracking-based nonlinear filtering algorithms such as Cubature Kalman Filter (CKF) since traditional fading factor introduction method only considers the first-order Taylor expansion. To this end, a new fading factor idea is suggested and introduced into the strong tracking CKF method. The new fading factor introduction method expanded the number of fading factors from one to two with reselected introduction positions. The relationship between the two fading factors as well as the general calculation method can be derived based on Taylor expansion. Obvious superiority of the newly suggested fading factor introduction method is demonstrated according to different nonlinearity of the measurement function. Equivalent calculation method can also be established while applied to CKF. Theoretical analysis shows that the strong tracking CKF can extract the third-order term information from the residual and thus realize second-order accuracy. After optimizing the strong tracking algorithm process, a Fast Strong Tracking CKF (FSTCKF) is finally established. Two simulation examples show that the novel FSTCKF improves the robustness of traditional CKF while minimizing the algorithm time complexity under various conditions. Keywords: Algorithm time complexity, Cubature Kalman filter, Nonlinear filtering, Robustness, Strong tracking filter

The time cooperation technology has important influence on multiple unmanned combat aerial vehicles (multi-UCAVs) cooperative attacking, which improves the operational effectiveness of UCAVs conspicuously. In this paper, a novel time cooperation method based on hybrid non-uniform adjustment is proposed to solve the time collaboration problem of multiple UCAVs. The hybrid non-uniform cooperation method construct the time coordination mode combining maneuvering coordination and speed coordination, adopting the non-uniform cruising rate along the whole route. The simulation results verify the method proposed in this paper has a larger adjustment range and higher adjustment accuracy compared with the existing time collaboration methods.

With the continuous development and application of modeling technology, civil aircraft designers urgently need to apply model-based systems engineering (MBSE) theory and methods into the field of civil aircraft development. However, the existing modeling tools, which combined with MBSE methodologies, only focus on one particular area, such as requirement modeling, functional modeling and design synthesis. This kind of low coupling design system makes it difficult to link the upstream and downstream designers in the early stage of system development. This paper introduces the MBSE development process of civil aircraft and then establishes an integrated platform composed of six work stations for civil aircraft development based on this process. Commercially available modeling tools, such as IBM Rational DOORS and enterprise architect (EA), are conducted in the platform. The development of flight control system is taken as an example to test the feasibility and efficiency of our integrated platform in logic layer. This platform can complete the activities of aircraft life cycle function analysis, requirement and model management. The loose coupling between the modules and the high cohesive nature allows different system engineers to maintain their design independence and consistency at different stages of development.

Clustering is one of the most important methods to discover the intrinsic grouping in a set of unlabeled data. As ways of getting data are more various and easier, the amount of data processed is increasing exponentially and the data is more likely to be located at different clients. Traditional clustering methods cannot process the large dataset one time due to the limit of memories. In this paper, an Image Scaling Density-based Clustering (ISDC) algorithm is proposed. ISDC can process data by a client alone as well as process in parallel by several clients to deal with data located at different clients. The ISDC algorithm does not need any parameters to be designated manually. The parameters are determined by the algorithm based on the statistical features of dataset. In Parallel ISDC or PISDC, each data block located at different client is clustered alone to form intermediate clusters. By border detection algorithm, representative clusters are formed by the points that are at the edge of intermediate clusters. Then, in global clustering, representative clusters from all clients are merged by the server. The border detection algorithm reduces the communication cost between clients and the server, as well as increases the efficiency of global clustering. At last, the server feeds back the clustering information to clients to complete clustering. Our experimental results verified the effectiveness and efficiency of PISDC and ISDC.

This paper is devoted to the unmanned aerial vehicle (UAV) mission allocation problem. To solve this problem in a more realistic battlefield environment, an improved mathematical model for UAV mission allocation is proposed. Being different from previous formulations, this model not only considers the difference in the importance of the target but also the constraints of the time window. In addition, an indicator of reconnaissance reward is added to this model. Each target area has a different importance, just as the strategic value of each region is different in combat. In this paper, we randomly generate the value factor for each reconnaissance area. To solve the mathematical model with different operational intentions, a dimensionality reduction process for which the reconnaissance reward is the optimization objective is presented. Finally, based on the improved model, the simulation result with Lingo is compared with that of non-dominated sorting genetic algorithm with elite strategy (NSGA-II) and genetic algorithm (GA) to verify the reliability and the effectiveness of the improved method.

Among many rule-based systems employed in classification problems, the belief rule-based (BRB) system has been significant for its ability to deal with both quantitative and qualitative information under uncertainty. However, it may face excessive information and low accuracy in some applications due to the limitations of the conventional BRB generation method. To this end, a greedy-based BRB learning method is proposed in this paper. Firstly, the BRB is generated by selecting a reduced number of belief rules from a set of candidate rules. Then, the BRB learning process is conducted by exploiting a selection and reduction strategy, which searches and selects the optimal rules from candidate rules as well as removes noise and redundant rules. Moreover, the original procedures of the inference process and class estimation are retained from conventional BRB systems. Thirty standard classification benchmarks are tested to validate the effectiveness and efficiency of the proposed method, and the classification results are compared with existing rule-based systems, novel belief rule-based systems, and conventional machine learning methods. The comparison results show that the proposed method could achieve relatively satisfactory accuracy while having a significantly smaller BRB. Furthermore, the results derived from benchmarks with two or three classes show the superior performance of the proposed method compared with some state-of-the-art classification methods.

A novel low-cost adaptive square-root cubature Kalman filter (LCASCKF) is proposed to enhance the robustness of process models while only increasing the computational load slightly. It is well-known that the Kalman filter cannot handle uncertainties in a process model, such as initial state estimation errors, parameter mismatch and abrupt state changes. These uncertainties severely affect filter performance and may even provoke divergence. A strong tracking filter (STF), which utilizes a suboptimal fading factor, is an adaptive approach that is commonly adopted to solve this problem. However, if the strong tracking SCKF (STSCKF) uses the same method as the extended Kalman filter (EKF) to introduce the suboptimal fading factor, it greatly increases the computational load. To avoid this problem, a low-cost introductory method is proposed and a hypothesis testing theory is applied to detect uncertainties. The computational load analysis is performed by counting the total number of floating-point operations and it is found that the computational load of LCASCKF is close to that of SCKF. Experimental results prove that the LCASCKF performs as well as STSCKF, while the increase in computational load is much lower than STSCKF.

Search speed, quality of resulting paths and the cost of pre-processing are the principle evaluation metrics of a pathfinding algorithm. In this paper, a new algorithm for grid-based maps, rectangle expansion A∗ (REA∗), is presented that improves the performance of A∗ significantly. REA∗ explores maps in units of unblocked rectangles. All unnecessary points inside the rectangles are pruned and boundaries of the rectangles (instead of individual points within those boundaries) are used as search nodes. This makes the algorithm plot fewer points and have a much shorter open list than A∗. REA∗ returns jump and grid-optimal path points, but since the line of sight between jump points is protected by the unblocked rectangles, the resulting path of REA∗ is usually better than grid-optimal. The algorithm is entirely online and requires no offline pre-processing. Experimental results for typical benchmark problem sets show that REA∗ can speed up a highly optimized A∗ by an order of magnitude and more while preserving completeness and optimality. This new algorithm is competitive with other highly successful variants of A∗.

Dynamic function allocation for aircraft cockpit benefits the complementary between operators and automation and the whole system performance. We selected two important physiological parameters HRV2 and TLI2, which have great influences on the operator functional state (OFS) and took their measurements as a trigger mechanism for dynamic function allocation of the cockpit system. After that, we utilized fuzzy logic to put forward a dynamic adjustment methodology for the levels of automation and used MATLAB to verify the utility of the method.

Aiming at the problem that the traditional DempsterShafer (D-S) evidence theory cannot deal with conflicted evidences effectively and correctly, this paper points out that the key issue of this problem is to measure the degree of conflict between evidences correctly after analyzing various improved methods. The existing evidence conflict measure methods are analyzed, and a new evidence conflict measure method called evidence similarity measure based on the Tanimoto measure is proposed, while a new evidence combination method is proposed on the basis of evidence similarity measure. Firstly, the conflict degrees between evidences are obtained through the evidence similarity measure. Then the evidence sources are modified based on the credibility of different evidences and the weights of conflicted parts of evidences on different focal elements are determined. Finally, the fusion result is obtained by this method. Numerical examples show that the proposed method can effectively fuse evidences when evidences are consistent or highly conflicted, and it has a fast convergence speed, a high degree of accuracy and good adaptability.