The wavelet support machine is obtained with wavelet kernel function, which is then used in time series forecasting for a certain kind of gyro.
将子波核函数应用于支撑矢量机,得到小波支撑矢量机(WSVM)方法,用于某陀螺仪漂移时间序列预测中,预测精度优于基于传统核函数的支撑矢量机。
Aiming at the normal Gaussian distributional noise,greater breadth noise and oddity point noise of product sales series and combing a designed robust loss function with wavelet kernel function,we propose a new waveletν-support vector machine,named as robust waveletν-support vector machine (RWν-SVM).
针对产品销售时序具有正态高斯分布、幅值较大、奇异点等混合噪音,设计一种鲁棒损失函数,并采用小波核函数,由此得到一种新的小波ν-支持向量机,即鲁棒小波ν-支持向量机(Robust wavelet ν-support vector machine,RWν-SVM)。
A variety of wavelet kernel functions are constructed.
构造了一类小波核函数,并利用小波核函数支持向量机对预应力混凝土碳化深度进行了仿真和预测。
To improve the ability of least square support vector regression algorithm, a least square wavelet support vector regression model by introducing the Morlet wavelet kernel is presented.
为了提高最小二乘支持向量回归机的性能,将Morlet小波核函数引入其中,形成了最小二乘小波支持向量回归机模型。
A new wavelet kernel function network (WKFN) was proposed as an alternative of support vector machine (SVM).
提出一种子波核函数网络作为支撑矢量机的一种替代学习机 ,仿真实验验证了子波核函数网络的逼近性能和识别性能都可以与相应的支撑矢量机相媲美 ,并优于子波神经网络 。
