计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2013年
17期
226-229
,共4页
短时交通流量%最小二乘支持向量机%人工鱼群算法%时间序列
短時交通流量%最小二乘支持嚮量機%人工魚群算法%時間序列
단시교통류량%최소이승지지향량궤%인공어군산법%시간서렬
short-time traffic flow%least squares support vector machine%artificial fish swarm algorithm%time series
为了提高短时交通流量的预测精度，针对最小二乘支持向量机（LSSVM）参数优化难题，提出一种人工鱼群算法（AFSA）和LSSVM相结合的短时交流量预测模型（AFSA-LSSVM），通过采用AFSA优化LSSVM参数，并采用具体短时交通流量数据进行仿真实验。仿真结果表明，相对于参比模型，AFSA-LSSVM可以获得更优的LSSVM参数，能够更加准确地描述短时交通流量变化趋势，提高了短时交通量的预测精度，为非线性短时交通流量预测提供了一种新的研究思路。
為瞭提高短時交通流量的預測精度，針對最小二乘支持嚮量機（LSSVM）參數優化難題，提齣一種人工魚群算法（AFSA）和LSSVM相結閤的短時交流量預測模型（AFSA-LSSVM），通過採用AFSA優化LSSVM參數，併採用具體短時交通流量數據進行倣真實驗。倣真結果錶明，相對于參比模型，AFSA-LSSVM可以穫得更優的LSSVM參數，能夠更加準確地描述短時交通流量變化趨勢，提高瞭短時交通量的預測精度，為非線性短時交通流量預測提供瞭一種新的研究思路。
위료제고단시교통류량적예측정도，침대최소이승지지향량궤（LSSVM）삼수우화난제，제출일충인공어군산법（AFSA）화LSSVM상결합적단시교류량예측모형（AFSA-LSSVM），통과채용AFSA우화LSSVM삼수，병채용구체단시교통류량수거진행방진실험。방진결과표명，상대우삼비모형，AFSA-LSSVM가이획득경우적LSSVM삼수，능구경가준학지묘술단시교통류량변화추세，제고료단시교통량적예측정도，위비선성단시교통류량예측제공료일충신적연구사로。
In order to improve the prediction accuracy of short-term traffic flow, in view of parameters optimization problem for Least Squares Support Vector Machine(LSSVM), this paper proposes a short-term traffic prediction model based on Artificial Fish Swarm Algorithm(AFSA)and LSSVM(AFSA-LSSVM)which LSSVM parameters are optimized by improved AFSA, and the simulation experiment is carried out based on short-term traffic flow data. The simulation results show that the proposed model can obtain better LSSVM parameters and can more accurately reflect the changes of short-term traffic flow, and improves the short-term traffic prediction accuracy compared with the reference model, and it provides a new research method for the non-linear short-term traffic flow prediction.
