Research on Search Laws of Image Guided Missile

When image guided missile adopts guided search and common search law to search ground targets, under some conditions the movement of image on the monitor screen will be dissymmetrical, which is harmful for shooter to acquire and capture targets. To remove the dissymmetry of the image movement, such common search laws as parallel search law, ＂X＂ style search law, search law of one-dimension visual effect and cone search law are improved and designed again. Simulation results show that the dissymmetry can be removed by adopting all the above four improved search laws, but the search track of improved cone search law has serious transmutation compared with the original search track. The other three improved search laws have little transmutation of the search track, and they all can keep the main characters of the original search law. Study resuits are helpful for image guided missile in adopting guided search to choose appropriate search law.

Multi-objective optimization of feature selection using hybrid cat swarm optimization

With the pervasive generation of information from a wide range of sensors and devices,there always exist a large number of input features in databases,thus complicating machine learning problem formulation.However,certain features are relatively impertinent to specific problems,which may degrade the performances of classifiers in terms of prediction accuracy,sensitivity,specificity,and recall rate.The main goal of a multi-objective optimization problem is to identify the subsets of the given features.To this end,a hybrid cat swarm optimization(HCSO)algorithm is proposed in our paper for performance improvement of the basic cat swarm optimization(CSO)that incorporates guided and competitive&inherent characteristics into the original CSO.The performance of HCSO has been tested by finding the optimal feature subset for 15 benchmark datasets.The number of class labels for these datasets varies between 2 and 40.The time complexity analysis of both CSO and HCSO has also been evaluated.Moreover,the performance of the proposed algorithm has been compared with that of simple CSO and other state-ofthe-art techniques.The performances obtained by HCSO have an average 2.68%improvement with a standard deviation of 2.91.The maximum performance improvement is up to 10.09%in prediction accuracy.Tested on the same datasets,CSO has yielded improvements within the range of-7.27%to 8.51%with an average improvement 0.9%and standard deviation 3.96.The statistical tests carried out in the experiments prove that HCSO manifests a moderately better feature selection capacity than that of its counterparts.