Quantized kernel least mean square(QKLMS) algorithm is an effective nonlinear adaptive online learning algorithm with good performance in constraining the growth of network size through the use of quantization for inp...Quantized kernel least mean square(QKLMS) algorithm is an effective nonlinear adaptive online learning algorithm with good performance in constraining the growth of network size through the use of quantization for input space. It can serve as a powerful tool to perform complex computing for network service and application. With the purpose of compressing the input to further improve learning performance, this article proposes a novel QKLMS with entropy-guided learning, called EQ-KLMS. Under the consecutive square entropy learning framework, the basic idea of entropy-guided learning technique is to measure the uncertainty of the input vectors used for QKLMS, and delete those data with larger uncertainty, which are insignificant or easy to cause learning errors. Then, the dataset is compressed. Consequently, by using square entropy, the learning performance of proposed EQ-KLMS is improved with high precision and low computational cost. The proposed EQ-KLMS is validated using a weather-related dataset, and the results demonstrate the desirable performance of our scheme.展开更多
针对动态室内环境的变化及时变的接收信号强度(Received signal strength,RSS)对定位精度的影响,提出了一类基于核自适应滤波算法的农业无线传感器网络室内定位方法。核自适应滤波算法具体包括量化核最小均方(Quantized kernel least me...针对动态室内环境的变化及时变的接收信号强度(Received signal strength,RSS)对定位精度的影响,提出了一类基于核自适应滤波算法的农业无线传感器网络室内定位方法。核自适应滤波算法具体包括量化核最小均方(Quantized kernel least mean square,QKLMS)算法及固定预算(Fixed-budget,FB)核递推最小二乘(Kernel recursive least-squares,KRLS)算法。QKLMS算法基于一种简单在线矢量量化方法替代稀疏化,抑制核自适应滤波中径向基函数结构的增长。FB-KRLS算法是一种固定内存预算的在线学习方法,与以往的"滑窗"技术不同,每次时间更新时并不"修剪"最旧的数据,而是旨在"修剪"最无用的数据,从而抑制核矩阵的不断增长。通过构建RSS指纹信息与物理位置之间的非线性映射关系,核自适应滤波算法实现WSN的室内定位,将所提出的算法应用于仿真与物理环境下的不同实例中,在同等条件下,还与其他核学习算法、极限学习机(Extreme learning machine,ELM)等定位算法进行比较。仿真实验中2种算法在3种情形下的平均定位误差分别为0.746、0.443 m,物理实验中2种算法在2种情形下的平均定位误差分别为0.547、0.282 m。实验结果表明,所提出的核自适应滤波算法均能提高定位精度,其在线学习能力使得所提出的定位算法能自适应环境动态的变化。展开更多
连续搅拌反应釜(Continuous stirred tank reactor,CSTR)是典型的化工过程之一,本文针对其强非线性特性,提出了一种量化核最小均方(Quantized kernel least mean square,QKLMS)算法。该算法基于一种简单在线矢量量化技术替代稀疏化准则...连续搅拌反应釜(Continuous stirred tank reactor,CSTR)是典型的化工过程之一,本文针对其强非线性特性,提出了一种量化核最小均方(Quantized kernel least mean square,QKLMS)算法。该算法基于一种简单在线矢量量化技术替代稀疏化准则,可以对输入空间进行压缩,从而抑制核学习算法中径向基函数(Radial basis function,RBF)结构的增长。为验证该算法的有效性,将其应用于CSTR过程的模型辨识中,构建冷却剂流量与生成物浓度之间的非线性映射关系。此外,将所提算法与最小二乘支持向量机(Least square support vector machine,LS-SVM)、回声状态网络(Echo state network,ESN)以及核极限学习机(Extreme learning machine with kernels,KELM)等算法进行比较。实验结果表明,在同等条件下,本文所提算法具有更高的辨识精度和更好的在线学习能力。展开更多
基金supported by the National Key Technologies R&D Program of China under Grant No. 2015BAK38B01the National Natural Science Foundation of China under Grant Nos. 61174103 and 61603032+4 种基金the National Key Research and Development Program of China under Grant Nos. 2016YFB0700502, 2016YFB1001404, and 2017YFB0702300the China Postdoctoral Science Foundation under Grant No. 2016M590048the Fundamental Research Funds for the Central Universities under Grant No. 06500025the University of Science and Technology Beijing - Taipei University of Technology Joint Research Program under Grant No. TW201610the Foundation from the Taipei University of Technology of Taiwan under Grant No. NTUT-USTB-105-4
文摘Quantized kernel least mean square(QKLMS) algorithm is an effective nonlinear adaptive online learning algorithm with good performance in constraining the growth of network size through the use of quantization for input space. It can serve as a powerful tool to perform complex computing for network service and application. With the purpose of compressing the input to further improve learning performance, this article proposes a novel QKLMS with entropy-guided learning, called EQ-KLMS. Under the consecutive square entropy learning framework, the basic idea of entropy-guided learning technique is to measure the uncertainty of the input vectors used for QKLMS, and delete those data with larger uncertainty, which are insignificant or easy to cause learning errors. Then, the dataset is compressed. Consequently, by using square entropy, the learning performance of proposed EQ-KLMS is improved with high precision and low computational cost. The proposed EQ-KLMS is validated using a weather-related dataset, and the results demonstrate the desirable performance of our scheme.
文摘针对动态室内环境的变化及时变的接收信号强度(Received signal strength,RSS)对定位精度的影响,提出了一类基于核自适应滤波算法的农业无线传感器网络室内定位方法。核自适应滤波算法具体包括量化核最小均方(Quantized kernel least mean square,QKLMS)算法及固定预算(Fixed-budget,FB)核递推最小二乘(Kernel recursive least-squares,KRLS)算法。QKLMS算法基于一种简单在线矢量量化方法替代稀疏化,抑制核自适应滤波中径向基函数结构的增长。FB-KRLS算法是一种固定内存预算的在线学习方法,与以往的"滑窗"技术不同,每次时间更新时并不"修剪"最旧的数据,而是旨在"修剪"最无用的数据,从而抑制核矩阵的不断增长。通过构建RSS指纹信息与物理位置之间的非线性映射关系,核自适应滤波算法实现WSN的室内定位,将所提出的算法应用于仿真与物理环境下的不同实例中,在同等条件下,还与其他核学习算法、极限学习机(Extreme learning machine,ELM)等定位算法进行比较。仿真实验中2种算法在3种情形下的平均定位误差分别为0.746、0.443 m,物理实验中2种算法在2种情形下的平均定位误差分别为0.547、0.282 m。实验结果表明,所提出的核自适应滤波算法均能提高定位精度,其在线学习能力使得所提出的定位算法能自适应环境动态的变化。
基金National Natural Science Foundation of China(No.51467008)Scientific Research Projects of Colleges and Universities in Gansu Province(Nos.2018C-10,2017D-09)。
文摘连续搅拌反应釜(Continuous stirred tank reactor,CSTR)是典型的化工过程之一,本文针对其强非线性特性,提出了一种量化核最小均方(Quantized kernel least mean square,QKLMS)算法。该算法基于一种简单在线矢量量化技术替代稀疏化准则,可以对输入空间进行压缩,从而抑制核学习算法中径向基函数(Radial basis function,RBF)结构的增长。为验证该算法的有效性,将其应用于CSTR过程的模型辨识中,构建冷却剂流量与生成物浓度之间的非线性映射关系。此外,将所提算法与最小二乘支持向量机(Least square support vector machine,LS-SVM)、回声状态网络(Echo state network,ESN)以及核极限学习机(Extreme learning machine with kernels,KELM)等算法进行比较。实验结果表明,在同等条件下,本文所提算法具有更高的辨识精度和更好的在线学习能力。
