计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2014年
16期
216-220
,共5页
云理论%遗传算法%RBF神经网络%交通流量预测%Logistic混沌时间序列
雲理論%遺傳算法%RBF神經網絡%交通流量預測%Logistic混沌時間序列
운이론%유전산법%RBF신경망락%교통류량예측%Logistic혼돈시간서렬
cloud theory%genetic algorithm%RBF neural network%traffic flow prediction%Logistic chaotic time series
以神经网络和混沌时间序列理论为基础，提出了一种基于云遗传的RBF神经网络优化算法。该算法利用云模型云滴的随机性和稳定倾向性的特点，由正态云模型的Y条件云发生器实现交叉操作，由基本云发生器实现变异操作，提高了遗传搜索的效率，精简了网络结构。将该算法应用到Logistic混沌时间序列和实测交通流时间序列进行算法的有效性验证，并与传统的RBF算法和遗传算法优化的RBF算法（GARBF）进行比较。仿真结果表明该算法对混沌时间序列和交通流预测的精度有较大提高，从而证明该算法在交通流时间序列预测领域的可行性和有效性。
以神經網絡和混沌時間序列理論為基礎，提齣瞭一種基于雲遺傳的RBF神經網絡優化算法。該算法利用雲模型雲滴的隨機性和穩定傾嚮性的特點，由正態雲模型的Y條件雲髮生器實現交扠操作，由基本雲髮生器實現變異操作，提高瞭遺傳搜索的效率，精簡瞭網絡結構。將該算法應用到Logistic混沌時間序列和實測交通流時間序列進行算法的有效性驗證，併與傳統的RBF算法和遺傳算法優化的RBF算法（GARBF）進行比較。倣真結果錶明該算法對混沌時間序列和交通流預測的精度有較大提高，從而證明該算法在交通流時間序列預測領域的可行性和有效性。
이신경망락화혼돈시간서렬이론위기출，제출료일충기우운유전적RBF신경망락우화산법。해산법이용운모형운적적수궤성화은정경향성적특점，유정태운모형적Y조건운발생기실현교차조작，유기본운발생기실현변이조작，제고료유전수색적효솔，정간료망락결구。장해산법응용도Logistic혼돈시간서렬화실측교통류시간서렬진행산법적유효성험증，병여전통적RBF산법화유전산법우화적RBF산법（GARBF）진행비교。방진결과표명해산법대혼돈시간서렬화교통류예측적정도유교대제고，종이증명해산법재교통류시간서렬예측영역적가행성화유효성。
Based on neural networks theory and chaotic time series theory, an improved RBF neural networks based on cloud genetic algorithm is proposed. In this algorithm, Y-conditional cloud generator is used as the cross operator and basic cloud generator is used as the mutation operator by utilizing the properties of randomness and stable tendency of normal cloud mode, so improve the efficiency of genetic search and simplify the structure of the network. The efficiency of the proposed prediction method is tested by the simulation of time series of Logistic systems and real traffic flow. The simulation results show that the proposed method in the paper has higher precision compared with the traditional RBF neural network and GARBF neural network, so prove it is feasible and effective in the time series prediction of traffic flow.
