微型机与应用
微型機與應用
미형궤여응용
Microcomputer & its Applications
2015年
21期
51-54
,共4页
旅游客流量预测%BP 神经网络%粒子群算法%非线性递减
旅遊客流量預測%BP 神經網絡%粒子群算法%非線性遞減
여유객류량예측%BP 신경망락%입자군산법%비선성체감
tourist flow forecast%BP neural network%particle swarm algorithm%nonlinear decreasing
旅游客流量受多种因素影响，传统的时间序列预测模型无法描述预测对象的规律，人工智能方法如 BP 神经网络，其结构的选择过多依赖经验，基于此提出了利用改进的粒子群算法优化 BP神经网络，通过惯性因子的非线性递减来改善粒子群的寻优性能。将该预测模型应用于自贡灯会的客流量进行实际预测分析，通过对150组训练样本和50组测试样本的实验仿真，可知改进后的方法提高了预测结果的准确度，并且涉及参数少、简单有效。
旅遊客流量受多種因素影響，傳統的時間序列預測模型無法描述預測對象的規律，人工智能方法如 BP 神經網絡，其結構的選擇過多依賴經驗，基于此提齣瞭利用改進的粒子群算法優化 BP神經網絡，通過慣性因子的非線性遞減來改善粒子群的尋優性能。將該預測模型應用于自貢燈會的客流量進行實際預測分析，通過對150組訓練樣本和50組測試樣本的實驗倣真，可知改進後的方法提高瞭預測結果的準確度，併且涉及參數少、簡單有效。
여유객류량수다충인소영향，전통적시간서렬예측모형무법묘술예측대상적규률，인공지능방법여 BP 신경망락，기결구적선택과다의뢰경험，기우차제출료이용개진적입자군산법우화 BP신경망락，통과관성인자적비선성체감래개선입자군적심우성능。장해예측모형응용우자공등회적객류량진행실제예측분석，통과대150조훈련양본화50조측시양본적실험방진，가지개진후적방법제고료예측결과적준학도，병차섭급삼수소、간단유효。
Tourist flow is influenced by many factors. The traditional time series prediction model cannot describe the laws of the forecasted object. Artificial intelligence methods such as BP neural network, the choice of its structure relies too much on experience. Based on these above, the improved particle swarm optimization (PSO) algorithm was used to optimize the BP neural network. It uses nonlinear decreasing inertia factor to improve the performance of particle swarm optimization. The prediction model is applied to the flow of Zigong Lantern Festival forecast analysis. Through simulation of 150 sets of training samples and 50 groups of test samples, the result shows that the improved method improves the accuracy of the prediction, and involves less parameters, simple and effective.
