本文關(guān)鍵詞：基于量子自適應(yīng)粒子群優(yōu)化徑向基函數(shù)神經(jīng)網(wǎng)絡(luò)的網(wǎng)絡(luò)流量預(yù)測(cè)，由筆耕文化傳播整理發(fā)布。

基于量子自適應(yīng)粒子群優(yōu)化徑向基函數(shù)神經(jīng)網(wǎng)絡(luò)的網(wǎng)絡(luò)流量預(yù)測(cè)

Network Traffic Prediction with Radial Basis Function Neural Network Based on Quantum Adaptive Particle Swarm Optimization

[1] [2] [3] [4]

Guo Tong Lan Ju-long Li Yu-feng Jiang Yi-ming （National Digital Switching System Engineering ＆ Technological R＆D Center, Zhengzhou 450002, China）

國(guó)家數(shù)字交換系統(tǒng)工程技術(shù)研究中心,鄭州450002

文章摘要：該文提出一種量子白適應(yīng)粒子群優(yōu)化算法，該算法中，粒子位置的編碼采用量子比特實(shí)現(xiàn)，利用粒子飛行軌跡信息動(dòng)態(tài)更新量子比特的狀態(tài)，并引入量子非門實(shí)現(xiàn)變異操作以避免陷入局部最優(yōu)。用該算法訓(xùn)練神經(jīng)網(wǎng)絡(luò)，實(shí)現(xiàn)了徑向基函數(shù)（RJBF）神經(jīng)網(wǎng)絡(luò)參數(shù)優(yōu)化，建立了基于量子自適應(yīng)粒子群優(yōu)化RBF神經(jīng)網(wǎng)絡(luò)算法的網(wǎng)絡(luò)流量預(yù)測(cè)模型。對(duì)真實(shí)網(wǎng)絡(luò)流量的預(yù)測(cè)結(jié)果表明，，該方法的收斂速度和預(yù)測(cè)精度均要優(yōu)于傳統(tǒng)RBF神經(jīng)網(wǎng)絡(luò)法、粒子群-RBF神經(jīng)網(wǎng)絡(luò)法、混合粒子群-RBF神經(jīng)網(wǎng)絡(luò)法和自適應(yīng)粒子群-RBF神經(jīng)網(wǎng)絡(luò)法，并且預(yù)測(cè)效果不易受時(shí)間尺度變化的影響。

Abstr：A novel Quantum Adaptive Particle Swarm Optimization （QAPSO） method is proposed. In this algorithm, the position encoding of the particle is achieved with quantum bits, and the state of quantum bit is updated dynamically with particle trajectory information. Then the mutation operation is performed by quantum non-gate to avoid falling into local optimum, which increases the diversity of particles. Afterwards, the Radial Basis Function （RBF） neural network is trained with QAPSO to implement the optimization of RBF neural network parameters. The network traffic prediction model is established based on the Quantum Adaptive Particle Swarm Optimization and RBF Neural Network （QAPSO~RBFNN）. Forecasting results on real network traffic demonstrate that the convergence speed of the proposed method is faster and prediction accuracy is more accurate than that of traditional RBF neural network, the Particle Swarm Optimization and RBFNN （PSO-RBFNN）, Hybrid Particle Swarm Optimization and RBFNN （HPSO-RBFNN）, Adaptive Particle Swarm Optimization and RBF Neural Network （APSO-RBFNN）. Furthermore, the forecasting effect of this method is stable on different scales

文章關(guān)鍵詞：

Keyword：:Radial Basis Yhnction Neural Network （RBFNN） Adaptive Particle Swarm Optimization （APSO） Quantum bit Traffic prediction

課題項(xiàng)目：國(guó)家973計(jì)劃項(xiàng)目（2012cB315900）和國(guó)家863計(jì)劃項(xiàng)目（2011AA01A103）資助課題

  本文關(guān)鍵詞：基于量子自適應(yīng)粒子群優(yōu)化徑向基函數(shù)神經(jīng)網(wǎng)絡(luò)的網(wǎng)絡(luò)流量預(yù)測(cè)，由筆耕文化傳播整理發(fā)布。