CAJ | 학술논문

提出了一种混合加权距离测量（weighted distance measure ，weighted DM ）参数的构建和训练RBF（radial basis function）神经网络的两步批处理算法。该算法在引进了 DM 系数参数的基础上，采用Newton 法分别对径向基函数的覆盖参数、均值向量参数、加权距离测度系数以及输出权值进行了优化，并在优化过程中利用 OLS（orthogonal least squares）法来求解 New ton 法的方程组。通过实验数据，不仅分析了 New ton 法优化的各个参数向量对 RBF 网络训练的影响，而且比较了混合优化加权 DM 与RLS‐RBF（recursive least square RBF neural network）网络训练算法的收敛性和计算成本。所得到的结论表明整合了优化参数的加权 DM‐RBF 网络训练算法收敛速度比 RLS‐RBF 网络训练算法更快，而且具有比 LM‐RBF （Levenberg‐Marquardt RBF ）训练算法更小的计算成本，从而说明 OLS 求解的Newton 法对优化 RBF 网络参数具有重要应用价值。
제출료일충혼합가권거리측량（weighted distance measure ，weighted DM ）삼수적구건화훈련RBF（radial basis function）신경망락적량보비처리산법。해산법재인진료 DM 계수삼수적기출상，채용Newton 법분별대경향기함수적복개삼수、균치향량삼수、가권거리측도계수이급수출권치진행료우화，병재우화과정중이용 OLS（orthogonal least squares）법래구해 New ton 법적방정조。통과실험수거，불부분석료 New ton 법우화적각개삼수향량대 RBF 망락훈련적영향，이차비교료혼합우화가권 DM 여RLS‐RBF（recursive least square RBF neural network）망락훈련산법적수렴성화계산성본。소득도적결론표명정합료우화삼수적가권 DM‐RBF 망락훈련산법수렴속도비 RLS‐RBF 망락훈련산법경쾌，이차구유비 LM‐RBF （Levenberg‐Marquardt RBF ）훈련산법경소적계산성본，종이설명 OLS 구해적Newton 법대우화 RBF 망락삼수구유중요응용개치。
A hybrid two‐step second‐order batch approach is presented for constructing and training radial basis function (RBF) neural networks .Unlike other RBF neural network learning algorithms , the proposed paradigm uses New ton’s method to train each set of network parameters ,i .e .spread parameters ,mean vector parameters and weighted distance measure (DM ) coefficients and output weights parameters .For efficiently calculating the second‐order equations of New ton’s method ,all the optimal parameters are found out using orthogonal least squares (OLS ) with the multiply optimal learning factors(MOLFs) for training mean vector parameters .The simulation results of the proposed hybrid training algorithm on a real dataset are compared with those of the recursive least square based RBF(RLS‐RBF) and Levenberg‐Marquardt method based RBF(LM‐RBF) training algorithms .Also , the analysis of the training performance for optimization of each set of parameters has been presented . The experimental results show that the proposed hybrid optimal weighted DM training algorithm , which is based on the optimization of the mean vectors , weighted DM coefficients and spread parameters ,has significant improvement on training convergence speed compared with that of RLS‐RBF and has very less computation cost compared with that of LM‐RBF .It confirms that Newton’s method solved by OLS is a significantly valuable method for training the RBF neural network .