The Experts below are selected from a list of 115599 Experts worldwide ranked by ideXlab platform
Badong Chen - One of the best experts on this subject based on the ideXlab platform.
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bias compensated normalized maximum correntropy criterion algorithm for system identification with noisy input
Signal Processing, 2018Co-Authors: Dongqiao Zheng, Zhiyu Zhang, Badong ChenAbstract:Abstract This paper proposes a bias-compensated normalized maximum correntropy criterion (BCNMCC) algorithm charactered by its low steady-state misalignment for system identification with noisy input in an impulsive Output Noise environment. The normalized maximum correntropy criterion (NMCC) is derived from a correntropy based cost function, which is rather robust with respect to impulsive Noises. To deal with the noisy input, we introduce a bias-compensated vector to the NMCC algorithm, and then an unbiasedness criterion and some reasonable assumptions are used to compute the bias-compensated vector. Taking advantage of the bias-compensated vector, the bias caused by the input Noise can be effectively suppressed. System identification simulation results demonstrate that the proposed BCNMCC algorithm can outperform other related algorithms with noisy input especially in an impulsive Output Noise environment.
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bias compensated normalized maximum correntropy criterion algorithm for system identification with noisy input
arXiv: Machine Learning, 2017Co-Authors: Dongqiao Zheng, Zhiyu Zhang, Badong ChenAbstract:This paper proposed a bias-compensated normalized maximum correntropy criterion (BCNMCC) algorithm charactered by its low steady-state misalignment for system identification with noisy input in an impulsive Output Noise environment. The normalized maximum correntropy criterion (NMCC) is derived from a correntropy based cost function, which is rather robust with respect to impulsive Noises. To deal with the noisy input, we introduce a bias-compensated vector (BCV) to the NMCC algorithm, and then an unbiasedness criterion and some reasonable assumptions are used to compute the BCV. Taking advantage of the BCV, the bias caused by the input Noise can be effectively suppressed. System identification simulation results demonstrate that the proposed BCNMCC algorithm can outperform other related algorithms with noisy input especially in an impulsive Output Noise environment.
Dongqiao Zheng - One of the best experts on this subject based on the ideXlab platform.
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bias compensated normalized maximum correntropy criterion algorithm for system identification with noisy input
Signal Processing, 2018Co-Authors: Dongqiao Zheng, Zhiyu Zhang, Badong ChenAbstract:Abstract This paper proposes a bias-compensated normalized maximum correntropy criterion (BCNMCC) algorithm charactered by its low steady-state misalignment for system identification with noisy input in an impulsive Output Noise environment. The normalized maximum correntropy criterion (NMCC) is derived from a correntropy based cost function, which is rather robust with respect to impulsive Noises. To deal with the noisy input, we introduce a bias-compensated vector to the NMCC algorithm, and then an unbiasedness criterion and some reasonable assumptions are used to compute the bias-compensated vector. Taking advantage of the bias-compensated vector, the bias caused by the input Noise can be effectively suppressed. System identification simulation results demonstrate that the proposed BCNMCC algorithm can outperform other related algorithms with noisy input especially in an impulsive Output Noise environment.
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bias compensated normalized maximum correntropy criterion algorithm for system identification with noisy input
arXiv: Machine Learning, 2017Co-Authors: Dongqiao Zheng, Zhiyu Zhang, Badong ChenAbstract:This paper proposed a bias-compensated normalized maximum correntropy criterion (BCNMCC) algorithm charactered by its low steady-state misalignment for system identification with noisy input in an impulsive Output Noise environment. The normalized maximum correntropy criterion (NMCC) is derived from a correntropy based cost function, which is rather robust with respect to impulsive Noises. To deal with the noisy input, we introduce a bias-compensated vector (BCV) to the NMCC algorithm, and then an unbiasedness criterion and some reasonable assumptions are used to compute the BCV. Taking advantage of the BCV, the bias caused by the input Noise can be effectively suppressed. System identification simulation results demonstrate that the proposed BCNMCC algorithm can outperform other related algorithms with noisy input especially in an impulsive Output Noise environment.
Zhiyu Zhang - One of the best experts on this subject based on the ideXlab platform.
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bias compensated normalized maximum correntropy criterion algorithm for system identification with noisy input
Signal Processing, 2018Co-Authors: Dongqiao Zheng, Zhiyu Zhang, Badong ChenAbstract:Abstract This paper proposes a bias-compensated normalized maximum correntropy criterion (BCNMCC) algorithm charactered by its low steady-state misalignment for system identification with noisy input in an impulsive Output Noise environment. The normalized maximum correntropy criterion (NMCC) is derived from a correntropy based cost function, which is rather robust with respect to impulsive Noises. To deal with the noisy input, we introduce a bias-compensated vector to the NMCC algorithm, and then an unbiasedness criterion and some reasonable assumptions are used to compute the bias-compensated vector. Taking advantage of the bias-compensated vector, the bias caused by the input Noise can be effectively suppressed. System identification simulation results demonstrate that the proposed BCNMCC algorithm can outperform other related algorithms with noisy input especially in an impulsive Output Noise environment.
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bias compensated normalized maximum correntropy criterion algorithm for system identification with noisy input
arXiv: Machine Learning, 2017Co-Authors: Dongqiao Zheng, Zhiyu Zhang, Badong ChenAbstract:This paper proposed a bias-compensated normalized maximum correntropy criterion (BCNMCC) algorithm charactered by its low steady-state misalignment for system identification with noisy input in an impulsive Output Noise environment. The normalized maximum correntropy criterion (NMCC) is derived from a correntropy based cost function, which is rather robust with respect to impulsive Noises. To deal with the noisy input, we introduce a bias-compensated vector (BCV) to the NMCC algorithm, and then an unbiasedness criterion and some reasonable assumptions are used to compute the BCV. Taking advantage of the BCV, the bias caused by the input Noise can be effectively suppressed. System identification simulation results demonstrate that the proposed BCNMCC algorithm can outperform other related algorithms with noisy input especially in an impulsive Output Noise environment.
Bosco Leung - One of the best experts on this subject based on the ideXlab platform.
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quantization Noise spectrum of double loop sigma delta converter with sinusoidal input
IEEE Transactions on Circuits and Systems Ii: Analog and Digital Signal Processing, 1994Co-Authors: Sundeep Rangan, Bosco LeungAbstract:An exact formula for the Output Noise spectrum of a double-loop sigma-delta modulator, under the no overloading assumption and with a sinusoidal input, is derived without the use of a white-Noise model. In the case of a sinusoidal input with irrational input amplitude and digital frequency, the result agrees with the exact formula derived by ergodic theory for two-stage modulators. In addition, the present method also provides an exact formula for sinusoidal inputs with rational frequency and amplitude. Furthermore, the period of the Output with rational initial conditions and DC input is also calculated. The results are of primary interest to multibit sigma-delta modulators, which do not overload over the entire input amplitude range. The ergodic theory method for calculating the exact Noise spectrum involves explicitly determining the autocorrelation of the internal quantization error with ergodic theory techniques, and then determining the Noise spectrum from the correlation function. The present method, however, directly determines the quantization Noise spectrum by using an open-loop model for the coder and applying a Fourier series representation of the quantization error function. The result of both of these methods is that the Output Noise spectrum for a sinusoidal input is composed of discrete spectral lines shaped by a sin /sup 4/(w/2) envelope. >
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quantization Noise spectrum of double loop sigma delta converter with sinusoidal input
Midwest Symposium on Circuits and Systems, 1993Co-Authors: Sundeep Rangan, Bosco LeungAbstract:An exact formula for the Output Noise spectrum of a double-loop sigma-delta modulator with a sinusoidal input is derived without the use of a white-Noise model. In the case of a sinusoidal input with irrational input amplitude and digital frequency, the result agrees with the exact formula derived by ergodic theory for two-stage modulators. In addition, the present method also provides an exact formula for a sinusoidal input with rational frequency and amplitude. Furthermore, the period of the Output with rational initial conditions and DC input is also calculated. All results assume that the quantizer does not overload, and hence apply only to multi-bit coders. The ergodic theory method for calculating the exact Noise spectrum involves explicitly determining the autocorrelation of the internal quantization error with ergodic theory techniques, and then determining the Noise spectrum from the correlation function. The present method, however, directly determines the quantization Noise spectrum by using an open-loop model for the coder and applying a Fourier series representation of the quantization error function. The result of both of these methods is that the Output Noise spectrum for a sinusoidal input is composed of discrete spectral lines shaped by a sin/sup 4/(/spl omega2) envelope. >
F. Ayazi - One of the best experts on this subject based on the ideXlab platform.
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a new input switching scheme for a capacitive micro g accelerometer
Symposium on VLSI Circuits, 2004Co-Authors: Babak Vakili Amini, S. Pourkamali, Mohammad Faisal Zaman, F. AyaziAbstract:The design and implementation of a new input switching capacitive microaccelerometer interface circuit with /spl mu/g resolution is presented. The accelerometers were fabricated on 50 /spl mu/m thick silicon-on-insulator (SOI) substrates using a two-mask, dry-release and low temperature process. Fabricated devices were interfaced with a high resolution, low Noise and low power switched-capacitor integrated circuit (IC) implemented in a 2.5V 0.25 /spl mu/m N-well CMOS process with the chip size of 0.5/spl times/1.3mm/sup 2/. The measured sensitivity is 0.45V/g and the Output Noise floor is 4.4/spl mu/g//spl radic/Hz at 150Hz. The total power consumption is 5mW.
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A high resolution, stictionless, CMOS compatible SOI accelerometer with a low Noise, low power, 0.25 /spl mu/m CMOS interface
17th IEEE International Conference on Micro Electro Mechanical Systems. Maastricht MEMS 2004 Technical Digest, 2004Co-Authors: .v. Amini, S. Pourkamali, F. AyaziAbstract:The implementation and characterization of a high sensitivity silicon-on-insulator (SOI) capacitive microaccelerometer with sub-25 /spl mu/g resolution is presented. The in-plane accelerometers were fabricated on 40 /spl mu/m thick SOI substrates using a two-mask, dry-release low temperature process comprising of three plasma etching steps. The fabricated devices were interfaced with a high resolution, low Noise and low power switched-capacitor integrated circuit (IC) fabricated in a 2.5 V 0.25 /spl mu/m N-well CMOS process. The measured sensitivity is 0.2 pF/g and the Output Noise floor is 20 /spl mu/g//spl radic/Hz. The total power consumption is 3 mW.
