Search

Aiming at the deficiency that K2 algorithm depends on the maximum number of parent nodes and node of order, this paper proposed an improved Bayesian network structure learning algorithm (MWST-CS-K2). The algorithm firstly obtained the maximum number of parent nodes by constructing the maximum support tree (MWST). Then it used the correlation degree and updated coefficient between variables to set rules for adding, subtracting and reversing edges. The paper used improved cuckoo algorithm to optimize the nest location, and applied the breadth first search strategy to search and traverse to get the node order. Finally, the paper used maximum number of parent nodes and the order of nodes as the input of K2 algorithm to get the final network. Experiments show that in the standard ASIA,SACHS and CHILD network data tests, the average correct edge ratio of MWST-CS-K2 algorithm reaches 97.3%, 87.7 % and 95.6% respectively. The learning effect is better than other corresponding comparison algorithms, and the network structure obtains the most similar to the standard network structure. [ABSTRACT FROM AUTHOR]

Model counting is the number of models for a given proposition formula, which is a basic problem in the field of artificial intelligence. There are many exactly-one constraints in many practical problems such as Bayesian networks, bounded model detection, and accurate set coverage. A common processing method is to encode exactly-one constraints as CNF formulas, and then call the model counter to solve them. This method expands the scale of the proposition formula and easily leads to too long solution time. This paper respectively proposes the ECR algorithm that restores exactly-one constraints from the CNF formula and the ECP algorithm that handles exactlyone constraints. The ECR algorithm can significantly improve the solution efficiency of the C2D compiler. Based on the latest model counter ExactMC, this paper improves the model counter ECMC that can recognize and handle exactly-one constraints separately. The experimental results show that the time efficiency of ECMC is significantly improved compared to ExactMC. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Relation recommendation based on common neighbors' property is a hot research branch of link prediction in social network analysis. This paper proposesd a new measure of relation recommendation by introducing a hidden naive Bayesians (HNB) classification model, which model the task by analyzing the dependency among properties and incorporates this idea to measure the influence and contribution among common neighbors. Then built a ranking model to learn the highest similarity associated with each candidate pair by maximizing the likelihood of relationship building and extended the model to CN, AA and RA similarity-based recommendation algorithms. Experimental evaluation by AUC on real social networks proved that the proposed model can achieve a better result than some baseline and LNB. Finally, it also discovered that attributes with different network topologies recommended precision linear effects. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive inference algorithm for unmanned aerial vehicle mission decision-making under uncertain environment based on structure-variable discrete dynamic Bayesian networks. The proposed algorithm can dynamically adjust the parameters of mission decision-making model by using soft/hard evidence and a priori information, and make the mission decision-making model suitable for the dynamic environment by reasoning and interactive parameter learning. The simulation results validate that the proposed algorithm can provide assurance for unmanned aerial vehicle mission decision-making under the conditions of incomplete information of abrupt threats, inaccurate or unawareness of a prior parameters. [ABSTRACT FROM AUTHOR]

To establish practical failure prediction model and facilitate its applications, this paper proposed a failure prediction Bayesian network (FPBN) modeling method based on the failure mode, effects and criticality analysis (FMECA). According to the useful failure relationships embedded in the FMECA unit, the FPBN network construction and the probability parameter computation algorithms were discussed to build the corresponding FPBN unit model. Then, the FPBN unit model of each part in the complex equipment was connected with each other to establish the integrated FPBN model of the whole system. At last, based on the FMECA knowledge of a head up display (HUD), the practical FPBN case of the electronic part was built for the actual failure prediction tasks. The case analysis results show that the FMECA knowledge embedded FPBN model, which has the advantages of uncertainty representation and quantitative analysis, can perform effectively in the failure prediction of complex equipments. [ABSTRACT FROM AUTHOR]

To solve the ill-posed problem in electrical resistance tomography (ERT) technology, this paper proposed a one-step dynamic image reconstruction algorithm based on Bayesian theorem, it which was different to the existing ERT image reconstruction algorithms. Based on the basic theory of resistance tomography, it solved the sensitive field mathematical model by finite element method to obtain the sensitivity matrix and project data. Next, based on analysis of Bayesian theorem, it derived one-step dynamic reconstruction algorithm model. And it was taken into account statistical information including the prior probability of permittivity distribution and the noise information which was applied for image reconstruction. Finally, it researched the function of impact factors including the prior probability parameters and different signal-to-noise ratio(SNR) to reconstructed result under different distribution. Meanwhile, the presented method was compared with Gauss-Newton iteration and Bayesian iteration methods. The results show that this algorithm gets better effect in the precision and speed than others iteration methods. [ABSTRACT FROM AUTHOR]