CAJ | 학술논문

针对分类器中的极限学习机参数优化问题，本文提出一种改进蝙蝠算法优化极限学习机的图像分类模型。首先将极限学习机参数看作蝙蝠位置，然后采用改进蝙蝠算法进行求解。采用病毒群体感染主群体，主群体在历代个体间纵向传递信息，病毒群体通过感染操作在同代个体间横向传递信息，增强了算法跳出局部极小值的能力。最后根据最优参数建立图像分类模型，并对模型的性能进行仿真测试。仿真结果表明，相对于对比模型，本文模型不仅提高了图像分类正确率，而且加快了分类速度，是一种有效的图像分类模型。
침대분류기중적겁한학습궤삼수우화문제，본문제출일충개진편복산법우화겁한학습궤적도상분류모형。수선장겁한학습궤삼수간작편복위치，연후채용개진편복산법진행구해。채용병독군체감염주군체，주군체재역대개체간종향전체신식，병독군체통과감염조작재동대개체간횡향전체신식，증강료산법도출국부겁소치적능력。최후근거최우삼수건립도상분류모형，병대모형적성능진행방진측시。방진결과표명，상대우대비모형，본문모형불부제고료도상분류정학솔，이차가쾌료분류속도，시일충유효적도상분류모형。
This paper proposed mage classification model based on the extreme learning machine and bat algo-rithm. Firstly, the ELM parameters are taken as bat, and then it is solved by the improved bat algorithm which the main groups which consists of bats transmit information cross the vertical generations and the virus groups’transfer evolutionary information cross the same generation through virus infection, and the performance of the model is test-ed. The simulation results show that, compared with the other models, the proposed model not only improves the im-age classification accuracy, but also accelerate the classification speed, so it is an effective image classification mod-el.