The least means squares （LMS） adaptive filter algorithm was used in active suspension system. By adjusting the weight of adaptive filter, the minimum quadratic performance index was obtained. For two-degree-of-freed...The least means squares （LMS） adaptive filter algorithm was used in active suspension system. By adjusting the weight of adaptive filter, the minimum quadratic performance index was obtained. For two-degree-of-freedom vehicle suspension model, LMS adaptive controller was designed. The acceleration of the sprung mass,the dynamic tyre load between wheels and road,and the dynamic deflection between sprung mass and unsprung mass were determined as the evaluation targets of suspension performance. For LMS adaptive control suspension, compared with passive suspension, acceleration power spectral density of sprung mass acceleration under the road input model decreased 8-10 times in high frequency resonance band or low frequency resonance band. The simulation results show that LMS adaptive control is simple and remarkably effective. It further proves that the active control suspension system can improve both the riding comfort and handling safety in various operation conditions, and the method is fit for the active control of the suspension system.展开更多
According to the characteristic of maneuvering targets tracking system, adaptive track predicting control is proposed from the point of predicting the motion track of the maneuvering target. For this method, least mea...According to the characteristic of maneuvering targets tracking system, adaptive track predicting control is proposed from the point of predicting the motion track of the maneuvering target. For this method, least mean square（LMS） adaptive filter is applied to estimate the future track of the target. The structure of this filter is simple and the calculation amount is small. It is therefore suitable to being used in real-time control system. Testing results have proved that the control method can improve the tracking precision for maneuvering targets obviously.展开更多
In this paper, the authors explore the potential of several popular equalization techniques while overcoming their disadvantages. First, extensive literature survey on equalization is conducted. The focus is on popula...In this paper, the authors explore the potential of several popular equalization techniques while overcoming their disadvantages. First, extensive literature survey on equalization is conducted. The focus is on popular linear equalization algorithms such as the conventional least mean square (LMS ) algorithm, the recursive least squares ( RLS ) algorithm, the filtered X LMS algorithm and their development. To overcome the slow convergence problem while keeping the simplicity of the LMS based algorithms, an H 2 optimal initialization is proposed.展开更多
With independence assumption, this paper proposes and proves the superior step-size theorem on least mean square (LMS) algorithm, from the view of minimizing mean squared error (MSE). Following the theorem we construc...With independence assumption, this paper proposes and proves the superior step-size theorem on least mean square (LMS) algorithm, from the view of minimizing mean squared error (MSE). Following the theorem we construct a parallel variable step-size LMS filters algorithm. The theoretical model of the proposed algorithm is analyzed in detail. Simulations show the proposed theoretical model is quite close to the optimal variable step-size LMS (OVS-LMS) model. The experimental learning curves of the proposed algorithm also show the fastest convergence and fine tracking performance. The proposed algorithm is therefore a good realization of the OVS-LMS model.展开更多
Image denoising is a classical problem in image processing. Its essential goal is to preserve the image features and to reduce noise effiectively. The nonlocal means(NL-means) filter is a successful approach proposed ...Image denoising is a classical problem in image processing. Its essential goal is to preserve the image features and to reduce noise effiectively. The nonlocal means(NL-means) filter is a successful approach proposed in recent years due to its patch similarity comparison. However, the accuracy of similarities in this algorithm degrades when it suffiers from heavy noise. In this paper, we introduce feature similarities based on a multichannel filter into NL-means filter. The multi-bank based feature vectors of each pixel in the image are computed by convolving from various orientations and scales to Leung-Malik set(edge, bar and spot filters), and then the similarities based on this information are computed instead of pixel intensity. Experiments are carried out with Rician noise. The results demonstrate the superior performance of the proposed method. The wavelet-based method and traditional NL-means in term of both mean square error(MSE) and perceptual quality are compared with the proposed method, and structural similarity(SSIM) and quality index based on local variance(QILV) are given.展开更多
文摘The least means squares （LMS） adaptive filter algorithm was used in active suspension system. By adjusting the weight of adaptive filter, the minimum quadratic performance index was obtained. For two-degree-of-freedom vehicle suspension model, LMS adaptive controller was designed. The acceleration of the sprung mass,the dynamic tyre load between wheels and road,and the dynamic deflection between sprung mass and unsprung mass were determined as the evaluation targets of suspension performance. For LMS adaptive control suspension, compared with passive suspension, acceleration power spectral density of sprung mass acceleration under the road input model decreased 8-10 times in high frequency resonance band or low frequency resonance band. The simulation results show that LMS adaptive control is simple and remarkably effective. It further proves that the active control suspension system can improve both the riding comfort and handling safety in various operation conditions, and the method is fit for the active control of the suspension system.
文摘According to the characteristic of maneuvering targets tracking system, adaptive track predicting control is proposed from the point of predicting the motion track of the maneuvering target. For this method, least mean square（LMS） adaptive filter is applied to estimate the future track of the target. The structure of this filter is simple and the calculation amount is small. It is therefore suitable to being used in real-time control system. Testing results have proved that the control method can improve the tracking precision for maneuvering targets obviously.
文摘In this paper, the authors explore the potential of several popular equalization techniques while overcoming their disadvantages. First, extensive literature survey on equalization is conducted. The focus is on popular linear equalization algorithms such as the conventional least mean square (LMS ) algorithm, the recursive least squares ( RLS ) algorithm, the filtered X LMS algorithm and their development. To overcome the slow convergence problem while keeping the simplicity of the LMS based algorithms, an H 2 optimal initialization is proposed.
文摘With independence assumption, this paper proposes and proves the superior step-size theorem on least mean square (LMS) algorithm, from the view of minimizing mean squared error (MSE). Following the theorem we construct a parallel variable step-size LMS filters algorithm. The theoretical model of the proposed algorithm is analyzed in detail. Simulations show the proposed theoretical model is quite close to the optimal variable step-size LMS (OVS-LMS) model. The experimental learning curves of the proposed algorithm also show the fastest convergence and fine tracking performance. The proposed algorithm is therefore a good realization of the OVS-LMS model.
基金the Postgraduate Innovation Ability Cultivating Foundation of China(No.Z-SY-009)
文摘Image denoising is a classical problem in image processing. Its essential goal is to preserve the image features and to reduce noise effiectively. The nonlocal means(NL-means) filter is a successful approach proposed in recent years due to its patch similarity comparison. However, the accuracy of similarities in this algorithm degrades when it suffiers from heavy noise. In this paper, we introduce feature similarities based on a multichannel filter into NL-means filter. The multi-bank based feature vectors of each pixel in the image are computed by convolving from various orientations and scales to Leung-Malik set(edge, bar and spot filters), and then the similarities based on this information are computed instead of pixel intensity. Experiments are carried out with Rician noise. The results demonstrate the superior performance of the proposed method. The wavelet-based method and traditional NL-means in term of both mean square error(MSE) and perceptual quality are compared with the proposed method, and structural similarity(SSIM) and quality index based on local variance(QILV) are given.
