CAJ | 학술논문

针对传统小波神经网络（ wavelet neural network， WNN）受隐含层节点数影响大、网络误差易陷入局部极小、预测结果不稳定的问题，提出使用GentleAdaBoost和小波神经网络相结合的方法，提高网络预测精度和泛化能力。该方法首先对样本数据进行预处理并初始化测试数据分布权值；然后通过选取不同的隐含层节点数、小波基函数构造出不同类型的小波神经网络弱预测器序列并对样本数据进行反复训练；最后使用GentleAdaBoost算法将得到的多个小波神经网络弱预测器组成新的强预测器并进行回归预测。对UCI数据库中数据集进行仿真实验，结果表明，本方法比传统小波神经网络预测平均误差减少40％以上，有效地提高了神经网络预测精度，为小波神经网络应用提供借鉴。
침대전통소파신경망락（ wavelet neural network， WNN）수은함층절점수영향대、망락오차역함입국부겁소、예측결과불은정적문제，제출사용GentleAdaBoost화소파신경망락상결합적방법，제고망락예측정도화범화능력。해방법수선대양본수거진행예처리병초시화측시수거분포권치；연후통과선취불동적은함층절점수、소파기함수구조출불동류형적소파신경망락약예측기서렬병대양본수거진행반복훈련；최후사용GentleAdaBoost산법장득도적다개소파신경망락약예측기조성신적강예측기병진행회귀예측。대UCI수거고중수거집진행방진실험，결과표명，본방법비전통소파신경망락예측평균오차감소40％이상，유효지제고료신경망락예측정도，위소파신경망락응용제공차감。
In view that the traditional wavelet neural network ( WNN) was affected largely by the number of hidden lay-er nodes, easy to fall into local minimum and had unstable forecast results, a method of combining the GentleAdaBoost algorithm with WNN was put forward to improve the forecasting accuracy and generalization ability.First, this method performed the pretreatment for the historical data and initialized the distribution weights of test data.Second, different hidden layer nodes and wavelet basis functions were selected randomly to construct weak predictors of WNN and trained the sample data repeatedly.Finally, the multiple weak predictors of WNN were used to form a new strong predictor by GentleAdaBoost algorithm for regression forecasting.A simulation experiment using datasets from the UCI database was carried out.The results showed that this method had reduced the average error value by more than 40%compared to the traditional WNN, improved the forecasting accuracy of neural network, and could provide references for the WNN fore-casting.