The objective of modelling from data is not that the model simply fits the training data well. Rather, the goodness of a model is characterized by its generalization capability, interpretability and ease for knowledge...The objective of modelling from data is not that the model simply fits the training data well. Rather, the goodness of a model is characterized by its generalization capability, interpretability and ease for knowledge extraction. All these desired properties depend crucially on the ability to construct appropriate parsimonious models by the modelling process, and a basic principle in practical nonlinear data modelling is the parsimonious principle of ensuring the smallest possible model that explains the training data. There exists a vast amount of works in the area of sparse modelling, and a widely adopted approach is based on the linear-in-the-parameters data modelling that include the radial basis function network, the neurofuzzy network and all the sparse kernel modelling techniques. A well tested strategy for parsimonious modelling from data is the orthogonal least squares (OLS) algorithm for forward selection modelling, which is capable of constructing sparse models that generalise well. This contribution continues this theme and provides a unified framework for sparse modelling from data that includes regression and classification, which belong to supervised learning, and probability density function estimation, which is an unsupervised learning problem. The OLS forward selection method based on the leave-one-out test criteria is presented within this unified data-modelling framework. Examples from regression, classification and density estimation applications are used to illustrate the effectiveness of this generic parsimonious modelling approach from data.展开更多
This paper presents a two-level learning method for designing an optimal Radial Basis Function Network (RBFN) using Adaptive Velocity Update Relaxation Particle Swarm Optimization algorithm (AVURPSO) and Orthogonal Le...This paper presents a two-level learning method for designing an optimal Radial Basis Function Network (RBFN) using Adaptive Velocity Update Relaxation Particle Swarm Optimization algorithm (AVURPSO) and Orthogonal Least Squares algorithm (OLS) called as OLS-AVURPSO method. The novelty is to develop an AVURPSO algorithm to form the hybrid OLS-AVURPSO method for designing an optimal RBFN. The proposed method at the upper level finds the global optimum of the spread factor parameter using AVURPSO while at the lower level automatically constructs the RBFN using OLS algorithm. Simulation results confirm that the RBFN is superior to Multilayered Perceptron Network (MLPN) in terms of network size and computing time. To demonstrate the effectiveness of proposed OLS-AVURPSO in the design of RBFN, the Mackey-Glass Chaotic Time-Series as an example is modeled by both MLPN and RBFN.展开更多
In an orthogonal frequency division multiplexing(OFDM) system,a time and frequency domain least mean square algorithm(TF-LMS) was proposed to cancel the frequency offset(FO).TF-LMS algorithm is composed of two stages....In an orthogonal frequency division multiplexing(OFDM) system,a time and frequency domain least mean square algorithm(TF-LMS) was proposed to cancel the frequency offset(FO).TF-LMS algorithm is composed of two stages.Firstly,time domain least mean square(TD-LMS) scheme was selected to pre-cancel the frequency offset in the time domain,and then the interference induced by residual frequency offset was eliminated by the frequency domain mean square(FD-LMS) scheme in frequency domain.The results of bit error rate(BER) and quadrature phase shift keying(QPSK) constellation figures show that the performance of the proposed suppression algorithm is excellent.展开更多
Equalizers are widely used in digital communication systems for corrupted or time varying channels. To overcome performance decline for noisy and nonlinear channels, many kinds of neural network models have been used ...Equalizers are widely used in digital communication systems for corrupted or time varying channels. To overcome performance decline for noisy and nonlinear channels, many kinds of neural network models have been used in nonlinear equalization. In this paper, we propose a new nonlinear channel equalization, which is structured by wavelet neural networks. The orthogonal least square algorithm is applied to update the weighting matrix of wavelet networks to form a more compact wavelet basis unit, thus obtaining good equalization performance. The experimental results show that performance of the proposed equalizer based on wavelet networks can significantly improve the neural modeling accuracy and outperform conventional neural network equalization in signal to noise ratio and channel non-linearity.展开更多
Traditional PCA is a linear method, but most engineering problems are nonlinear. Using the linear PCA in nonlinear problems may bring distorted and misleading results. Therefore, an approach of nonlinear principal com...Traditional PCA is a linear method, but most engineering problems are nonlinear. Using the linear PCA in nonlinear problems may bring distorted and misleading results. Therefore, an approach of nonlinear principal component analysis (NLPCA) using radial basis function (RBF) neural network is developed in this paper. The orthogonal least squares (OLS) algorithm is used to train the RBF neural network. This method improves the training speed and prevents it from being trapped in local optimization. Results of two experiments show that this NLPCA method can effectively capture nonlinear correlation of nonlinear complex data, and improve the precision of the classification and the prediction.展开更多
A new channel estimation and data detection joint algorithm is proposed for multi-input multi-output （MIMO） - orthogonal frequency division multiplexing （OFDM） system using linear minimum mean square error （LMMSE...A new channel estimation and data detection joint algorithm is proposed for multi-input multi-output （MIMO） - orthogonal frequency division multiplexing （OFDM） system using linear minimum mean square error （LMMSE）- based space-alternating generalized expectation-maximization （SAGE） algorithm. In the proposed algorithm, every sub-frame of the MIMO-OFDM system is divided into some OFDM sub-blocks and the LMMSE-based SAGE algorithm in each sub-block is used. At the head of each sub-flame, we insert training symbols which are used in the initial estimation at the beginning. Channel estimation of the previous sub-block is applied to the initial estimation in the current sub-block by the maximum-likelihood （ML） detection to update channel estimatjon and data detection by iteration until converge. Then all the sub-blocks can be finished in turn. Simulation results show that the proposed algorithm can improve the bit error rate （BER） performance.展开更多
Modified constant modulus and recursive least squares （MCMA-RLS） algorithm is proposed to cancel interference caused by the variable frequency offset （FO） in the orthogonal frequency division multiplexing （OFDM）...Modified constant modulus and recursive least squares （MCMA-RLS） algorithm is proposed to cancel interference caused by the variable frequency offset （FO） in the orthogonal frequency division multiplexing （OFDM） system. The MCMA-RLS algorithm is composed of two stages including MCMA scheme and RLS scheme. MCMA is selected to pre-cancel the variable frequency offset firstly, and then the residual interference has been canceled by the RLS scheme. BR error rate is simulated to demonstrate that the proposed method is robust for canceling the variable frequency offset.展开更多
The matrix inversion operation is needed in the MMSE decoding algorithm of orthogonal space-time block coding （OSTBC） proposed by Papadias and Foschini. In this paper, an minimum mean square error （MMSE） decoding ...The matrix inversion operation is needed in the MMSE decoding algorithm of orthogonal space-time block coding （OSTBC） proposed by Papadias and Foschini. In this paper, an minimum mean square error （MMSE） decoding algorithm without matrix inversion is proposed, by which the computational complexity can be reduced directly but the decoding performance is not affected.展开更多
在带限数字通信系统中,平方根奈奎斯特(square-root Nyquist,SR-NYQ)滤波器通常同时应用于系统的发送端和接收端,可以有效减少符号间干扰(inter symbol interference,ISI)。本文提出了一种基于遗传算法(genetic algorithm,GA)的线性相位...在带限数字通信系统中,平方根奈奎斯特(square-root Nyquist,SR-NYQ)滤波器通常同时应用于系统的发送端和接收端,可以有效减少符号间干扰(inter symbol interference,ISI)。本文提出了一种基于遗传算法(genetic algorithm,GA)的线性相位SR-NYQ滤波器设计方法,其中滤波器ISI、通带和阻带波纹被融合构造为适应度函数。得益于GA强大的全局优化能力,该方法设计的原型滤波器在更加接近奈奎斯特条件的同时,提供了优于传统根升余弦滤波器的设计灵活性。此外,本文设计的SR-NYQ滤波器在正交频分复用系统中作为匹配和成型滤波器进行测试,并与传统的根升余弦滤波器进行对比。仿真对比结果表明,本文所设计的SR-NYQ滤波器具有更好的频率响应,可以显著降低符号错误率。展开更多
In countless applications,we need to reconstruct a K-sparse signal x∈R n from noisy measurements y=Φx+v,whereΦ∈R^(m×n)is a sensing matrix and v∈R m is a noise vector.Orthogonal least squares(OLS),which selec...In countless applications,we need to reconstruct a K-sparse signal x∈R n from noisy measurements y=Φx+v,whereΦ∈R^(m×n)is a sensing matrix and v∈R m is a noise vector.Orthogonal least squares(OLS),which selects at each step the column that results in the most significant decrease in the residual power,is one of the most popular sparse recovery algorithms.In this paper,we investigate the number of iterations required for recovering x with the OLS algorithm.We show that OLS provides a stable reconstruction of all K-sparse signals x in[2.8K]iterations provided thatΦsatisfies the restricted isometry property(RIP).Our result provides a better recovery bound and fewer number of required iterations than those proposed by Foucart in 2013.展开更多
文摘The objective of modelling from data is not that the model simply fits the training data well. Rather, the goodness of a model is characterized by its generalization capability, interpretability and ease for knowledge extraction. All these desired properties depend crucially on the ability to construct appropriate parsimonious models by the modelling process, and a basic principle in practical nonlinear data modelling is the parsimonious principle of ensuring the smallest possible model that explains the training data. There exists a vast amount of works in the area of sparse modelling, and a widely adopted approach is based on the linear-in-the-parameters data modelling that include the radial basis function network, the neurofuzzy network and all the sparse kernel modelling techniques. A well tested strategy for parsimonious modelling from data is the orthogonal least squares (OLS) algorithm for forward selection modelling, which is capable of constructing sparse models that generalise well. This contribution continues this theme and provides a unified framework for sparse modelling from data that includes regression and classification, which belong to supervised learning, and probability density function estimation, which is an unsupervised learning problem. The OLS forward selection method based on the leave-one-out test criteria is presented within this unified data-modelling framework. Examples from regression, classification and density estimation applications are used to illustrate the effectiveness of this generic parsimonious modelling approach from data.
文摘This paper presents a two-level learning method for designing an optimal Radial Basis Function Network (RBFN) using Adaptive Velocity Update Relaxation Particle Swarm Optimization algorithm (AVURPSO) and Orthogonal Least Squares algorithm (OLS) called as OLS-AVURPSO method. The novelty is to develop an AVURPSO algorithm to form the hybrid OLS-AVURPSO method for designing an optimal RBFN. The proposed method at the upper level finds the global optimum of the spread factor parameter using AVURPSO while at the lower level automatically constructs the RBFN using OLS algorithm. Simulation results confirm that the RBFN is superior to Multilayered Perceptron Network (MLPN) in terms of network size and computing time. To demonstrate the effectiveness of proposed OLS-AVURPSO in the design of RBFN, the Mackey-Glass Chaotic Time-Series as an example is modeled by both MLPN and RBFN.
基金Project(60532030) supported by the National Natural Science Foundation of China
文摘In an orthogonal frequency division multiplexing(OFDM) system,a time and frequency domain least mean square algorithm(TF-LMS) was proposed to cancel the frequency offset(FO).TF-LMS algorithm is composed of two stages.Firstly,time domain least mean square(TD-LMS) scheme was selected to pre-cancel the frequency offset in the time domain,and then the interference induced by residual frequency offset was eliminated by the frequency domain mean square(FD-LMS) scheme in frequency domain.The results of bit error rate(BER) and quadrature phase shift keying(QPSK) constellation figures show that the performance of the proposed suppression algorithm is excellent.
基金the Tsinghua University Research Foundation the Excellent Young Teacher Program of the Ministry of Education and the Returnee Science Research Startup Fund of the Ministry of Education of China
文摘Equalizers are widely used in digital communication systems for corrupted or time varying channels. To overcome performance decline for noisy and nonlinear channels, many kinds of neural network models have been used in nonlinear equalization. In this paper, we propose a new nonlinear channel equalization, which is structured by wavelet neural networks. The orthogonal least square algorithm is applied to update the weighting matrix of wavelet networks to form a more compact wavelet basis unit, thus obtaining good equalization performance. The experimental results show that performance of the proposed equalizer based on wavelet networks can significantly improve the neural modeling accuracy and outperform conventional neural network equalization in signal to noise ratio and channel non-linearity.
文摘Traditional PCA is a linear method, but most engineering problems are nonlinear. Using the linear PCA in nonlinear problems may bring distorted and misleading results. Therefore, an approach of nonlinear principal component analysis (NLPCA) using radial basis function (RBF) neural network is developed in this paper. The orthogonal least squares (OLS) algorithm is used to train the RBF neural network. This method improves the training speed and prevents it from being trapped in local optimization. Results of two experiments show that this NLPCA method can effectively capture nonlinear correlation of nonlinear complex data, and improve the precision of the classification and the prediction.
基金Supported by the National Natural Science Foundation of China （No. 61001105）, the National Science and Technology Major Projects （No. 2011ZX03001- 007- 03） and Beijing Natural Science Foundation （No. 4102043）.
文摘A new channel estimation and data detection joint algorithm is proposed for multi-input multi-output （MIMO） - orthogonal frequency division multiplexing （OFDM） system using linear minimum mean square error （LMMSE）- based space-alternating generalized expectation-maximization （SAGE） algorithm. In the proposed algorithm, every sub-frame of the MIMO-OFDM system is divided into some OFDM sub-blocks and the LMMSE-based SAGE algorithm in each sub-block is used. At the head of each sub-flame, we insert training symbols which are used in the initial estimation at the beginning. Channel estimation of the previous sub-block is applied to the initial estimation in the current sub-block by the maximum-likelihood （ML） detection to update channel estimatjon and data detection by iteration until converge. Then all the sub-blocks can be finished in turn. Simulation results show that the proposed algorithm can improve the bit error rate （BER） performance.
基金Supported by the National Natural Science Foundation of China （No. 60532030）.
文摘Modified constant modulus and recursive least squares （MCMA-RLS） algorithm is proposed to cancel interference caused by the variable frequency offset （FO） in the orthogonal frequency division multiplexing （OFDM） system. The MCMA-RLS algorithm is composed of two stages including MCMA scheme and RLS scheme. MCMA is selected to pre-cancel the variable frequency offset firstly, and then the residual interference has been canceled by the RLS scheme. BR error rate is simulated to demonstrate that the proposed method is robust for canceling the variable frequency offset.
文摘The matrix inversion operation is needed in the MMSE decoding algorithm of orthogonal space-time block coding （OSTBC） proposed by Papadias and Foschini. In this paper, an minimum mean square error （MMSE） decoding algorithm without matrix inversion is proposed, by which the computational complexity can be reduced directly but the decoding performance is not affected.
文摘在带限数字通信系统中,平方根奈奎斯特(square-root Nyquist,SR-NYQ)滤波器通常同时应用于系统的发送端和接收端,可以有效减少符号间干扰(inter symbol interference,ISI)。本文提出了一种基于遗传算法(genetic algorithm,GA)的线性相位SR-NYQ滤波器设计方法,其中滤波器ISI、通带和阻带波纹被融合构造为适应度函数。得益于GA强大的全局优化能力,该方法设计的原型滤波器在更加接近奈奎斯特条件的同时,提供了优于传统根升余弦滤波器的设计灵活性。此外,本文设计的SR-NYQ滤波器在正交频分复用系统中作为匹配和成型滤波器进行测试,并与传统的根升余弦滤波器进行对比。仿真对比结果表明,本文所设计的SR-NYQ滤波器具有更好的频率响应,可以显著降低符号错误率。
基金supported by the National Natural Science Foundation of China(grant nos.61907014,11871248,11701410,61901160)the Natural Science Foundation of Guangdong province(No.2021A1515010857)+2 种基金Youth Science Foundation of Henan Normal University(grant no.2019QK03)China Postdoctoral Science Foundation(grant no.2019M660557)Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme(2019).
文摘In countless applications,we need to reconstruct a K-sparse signal x∈R n from noisy measurements y=Φx+v,whereΦ∈R^(m×n)is a sensing matrix and v∈R m is a noise vector.Orthogonal least squares(OLS),which selects at each step the column that results in the most significant decrease in the residual power,is one of the most popular sparse recovery algorithms.In this paper,we investigate the number of iterations required for recovering x with the OLS algorithm.We show that OLS provides a stable reconstruction of all K-sparse signals x in[2.8K]iterations provided thatΦsatisfies the restricted isometry property(RIP).Our result provides a better recovery bound and fewer number of required iterations than those proposed by Foucart in 2013.
