数字技术与应用
數字技術與應用
수자기술여응용
DIGITAL TECHNOLOGY AND APPLICATION
2013年
12期
123-124
,共2页
公交车%交通拥塞%神经网络%粒子群%流量预测
公交車%交通擁塞%神經網絡%粒子群%流量預測
공교차%교통옹새%신경망락%입자군%류량예측
buses%traffic congestion%neural network and particle swarm%traffic prediction
本文结合城市公交流量预测的实际需求,基于粒子群优化算法对传统神经网络算法进行了优化。仿真结果说明:用优化之后的神经网络算法对公交车拥塞进行预测,能够取得较为满意的效果。
本文結閤城市公交流量預測的實際需求,基于粒子群優化算法對傳統神經網絡算法進行瞭優化。倣真結果說明:用優化之後的神經網絡算法對公交車擁塞進行預測,能夠取得較為滿意的效果。
본문결합성시공교류량예측적실제수구,기우입자군우화산법대전통신경망락산법진행료우화。방진결과설명:용우화지후적신경망락산법대공교차옹새진행예측,능구취득교위만의적효과。
First considering various influential facts ful y and building hereditary neural net forecast model, then comparing hereditary neural net forecast result with the forecast result of neural net of bp algorithm. For hereditary neural net owning strong learning ability and self-adaptability, so it is better than neural net of bp algorithm and has good value for application.