انجمن کامپیوتر ایران

A new structure learning approach for Bayesian networks (BNs) based on asexual reproduction optimization (ARO) is proposed in this paper. ARO can be essentially considered as an evolutionary based algorithm that mathematically models the budding mechanism of asexual reproduction. In ARO, a parent produces a bud through a reproduction operator; thereafter the parent and its bud compete to survive according to a performance index obtained from the underlying objective function of the optimization problem; this leads to the fitter individual. The proposed method is applied to real-world and benchmark applications, while its effectiveness is demonstrated through computer simulation. Results of simulation show that ARO outperforms GA because ARO results good structure in comparison with GA and the speed of convergence in ARO is more than GA. Finally, the ARO performance is statistically shown.

The presence of undetected direct path conditions in the TOA (Time of Arrival)-based indoor geolocation introduces major errors into distance measurements. In the proposed location estimation, an improved procedure is introduced in Ultra-wideband (UWB) systems in Line of Sight (LOS) and Non-Line of Sight (NLOS) multipath environments by incorporating the skewness as new statistic information of multipath channel. Weighted least square error (WLS) algorithm is used to estimate source position in this paper. Simulation results show effectiveness of the proposed algorithm, by reducing source localization error. Uncertainty region between NLOS and LOS identification is reduced in the proposed method compared to previous methods..