In this paper, an adaptive semi-active SSDV (Synchronized Switch Damping on Voltage) method based on the lms algorithm is proposed and applied to the vibration control of a composite beam. In the SSDV method, the valu...
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ISBN:
(纸本)9781424428915
In this paper, an adaptive semi-active SSDV (Synchronized Switch Damping on Voltage) method based on the lms algorithm is proposed and applied to the vibration control of a composite beam. In the SSDV method, the value of voltage source in the switching circuit is critical to its control performance. In the adaptive approach proposed in this study, the voltage coefficient is adjusted adaptively using the lms algorithm. The new adaptive approach is compared with the derivative-based adaptive SSDV proposed in the former study in the control of the first mode of a composite beam. The control results show that adaptive adjustment of voltage coefficient is effective in the vibration control of the composite beam and that lms-based approach is slightly better than the derivative-based approach.
This paper describes a new control approach called Adaptive linear neuron (ADALINE) based Least mean square (lms) algorithm used in a three phase four wire (3P4W) distribution system under single phase fault condition...
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ISBN:
(纸本)9781509001286
This paper describes a new control approach called Adaptive linear neuron (ADALINE) based Least mean square (lms) algorithm used in a three phase four wire (3P4W) distribution system under single phase fault condition. Three phase reference supply currents are generated by using the proposed control technique thereafter they compared with the corresponding sensed supply currents to produce switching pulses for the IGBTs of a four leg voltage source converter (4-leg VSC) based distribution static compensator (DSTATCOM). In this paper, the DSTATCOM is used for voltage regulation, power factor correction and elimination of distorted/unbalanced source current & excessive neutral current in the system. MATLAB/SimPowerSystem environment is used to simulate the distribution system under both fault and without fault conditions.
Among strategies using in distributed adaptive networks diffusion based algorithms despite their scalability, robustness and steady state performance suffer from slow initial convergence. We propose a method to speed ...
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ISBN:
(纸本)9781467356343
Among strategies using in distributed adaptive networks diffusion based algorithms despite their scalability, robustness and steady state performance suffer from slow initial convergence. We propose a method to speed up this convergence rate by arranging the network nodes into subgroups, partitioning the tap weight vector and taking advantage of the larger step-size allowed for short filters. As our simulation results show, the proposed algorithm has a faster convergence rate as compared with conventional diffusion lms algorithm and other algorithms have intended to increase the initial convergence rate of diffusion algorithms.
This work presents a new block-sparse least mean square (BS-lms) algorithm with adaptive and nonuniform group sizes. Motivated by the fact that large and zero coefficients in block-sparse systems assemble in clusters ...
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ISBN:
(纸本)9781538668122;9781538668115
This work presents a new block-sparse least mean square (BS-lms) algorithm with adaptive and nonuniform group sizes. Motivated by the fact that large and zero coefficients in block-sparse systems assemble in clusters of variant sizes, we divide the adaptive filter tap-weights into groups of nonuniform size. A predetermined threshold value is utilized for real-time group partitioning Large coefficient groups consist of clusters of coefficients whose magnitudes are above the threshold value, while zero coefficient groups consist of clusters of coefficients whose magnitudes are below the threshold value. Then, coefficients in large coefficient groups are updated by following the standard lms algorithm, while those in zero coefficient groups suffer from an attractive force towards zero. A practical choice guideline is provided for the threshold value to facilitate applications. The proposed algorithm is tested in the identification of single- and multiple-block-sparse systems. Simulation results show that the proposed scheme effectively locates and tracks the non-zero blocks in the unknown systems, outperforming existing block-sparsity-inspired lms algorithms with uniform group sizes.
In this paper, we propose a block least mean square algorithm with delayed weight adaptation for hardware implementation of finite impulse response (FIR) adaptive filters. We have referred to the proposed algorithm as...
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ISBN:
(纸本)9781424424085
In this paper, we propose a block least mean square algorithm with delayed weight adaptation for hardware implementation of finite impulse response (FIR) adaptive filters. We have referred to the proposed algorithm as delayed block least mean square (DBlms) algorithm. Unlike the delayed least mean square (Dlms) algorithm, the DBlms algorithm takes a block of L input samples and yields a block of L output in every training cycle. The simulation result shows that the DBlms algorithm has convergence performance equivalent to that of the Dlms algorithm. We have exploited the parallelism inherent in the DBlms algorithm to derive a highly concurrent systolic architecture for FIR adaptive filters. The proposed architecture can support L time higher sampling rate compared with the best of the existing pipelined designs and, therefore, it would involve less samples of adaptation delays and would provide a more efficient implementation of lms-based adaptive filters.
Sparse representations of model parameters have been widely studied. In the adaptive filtering literature, most studies address the cases where the sparsity is directly observed, therefore, there is a growing interest...
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ISBN:
(纸本)9789082797039
Sparse representations of model parameters have been widely studied. In the adaptive filtering literature, most studies address the cases where the sparsity is directly observed, therefore, there is a growing interest in developing strategies to exploit hidden sparsity. Recently, the feature lms (F-lms) algorithm was proposed to expose the sparsity of models with low- and high-frequency contents. In this paper, the F-lms algorithm is extended to expose hidden sparsity related to models with bandpass spectrum, including the cases of narrowband and broader passband sources. Some simulation results show that the proposed approaches lead to F-lms algorithms with fast convergence, low misadjustment after convergence, and low computational cost.
The paper introduces the principle and structure of adaptive filter based on lms algorithm, studies a design scheme of a single frequency adaptive notch filter, and simulates its working procedure by using the Simulin...
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ISBN:
(纸本)9783037851951
The paper introduces the principle and structure of adaptive filter based on lms algorithm, studies a design scheme of a single frequency adaptive notch filter, and simulates its working procedure by using the Simulink simulation tool. The simulation results show that the adaptive notch filter based on lms algorithm has the better convergence and the smaller steady-state error than traditional notch filter in the appropriate parameter.
This paper presents a variable step-size Least Mean Square (lms) algorithm based on cloud model which is a new cognitive model for uncertain transformation between linguistic concepts and quantitative values. In this ...
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ISBN:
(数字)9783642162480
ISBN:
(纸本)9783642162473
This paper presents a variable step-size Least Mean Square (lms) algorithm based on cloud model which is a new cognitive model for uncertain transformation between linguistic concepts and quantitative values. In this algorithm, we use the error differences between two adjacent iteration periods to qualitatively estimate the state of the algorithm, and translate it into a propriety step-size in number according to the linguistic description of basic principle of variable step-size lms. Simulation results show that the proposed algorithm is able to improve the steady-state performance while keeping a better convergence rate. We also apply this new algorithm to the multiuser interference cancellation, and results are also satisfied.
In this paper, a new competent control approach is implemented to investigate the performance of a Distribution STATic COMpensator (DSTATCOM) in a three phase balanced nonlinear load supplied by a three-phase AC mains...
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ISBN:
(纸本)9781509001286
In this paper, a new competent control approach is implemented to investigate the performance of a Distribution STATic COMpensator (DSTATCOM) in a three phase balanced nonlinear load supplied by a three-phase AC mains. The modern control approach, ADALINE based lms algorithm is used to produce the switching pulses for the voltage source inverter (VSI) of DSTATCOM followed by the generation of reference source current. The performance of the DSTATCOM is analysed under single phase fault condition. System stabilization, harmonic mitigation of source current and alleviation of unbalanced supply current are achieved successfully using the control approach under single phase fault condition. The simulations of the proposed system are performed in a MATLAB/Simulink software.
In this paper, a digital background error-correction technique for pipelined successive approximation analogue-to-digital converter (SAR ADC) based on Least Mean Square (lms) algorithm is presented. This technique use...
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ISBN:
(纸本)9781467325233
In this paper, a digital background error-correction technique for pipelined successive approximation analogue-to-digital converter (SAR ADC) based on Least Mean Square (lms) algorithm is presented. This technique uses a slow but accurate ADC as a reference ADC and combines with lms algorithm to calibrate the capacitor mismatch, gain error, reference voltage offset error of the inaccurate pipelined SAR ADC. The simulation validates the effectiveness of this technique for a pipelined SAR ADC with 16 bit resolution. The effective number of bits(ENOB) is improved from 10.31 bits to 15.66 bits.
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