The Experts below are selected from a list of 12408 Experts worldwide ranked by ideXlab platform
Lin Xiao - One of the best experts on this subject based on the ideXlab platform.
-
zeroing neural network with comprehensive performance and its applications to time varying lyapunov equation and perturbed robotic tracking
Neurocomputing, 2020Co-Authors: Lin XiaoAbstract:Abstract The time-varying Lyapunov equation is an important problem that has been extensively employed in the engineering field and the Zeroing Neural Network (ZNN) is a powerful tool for solving such problem. However, unpredictable Noises can potentially harm ZNN’s accuracy in practical situations. Thus, the comprehensive performance of the ZNN model requires both fast convergence rate and strong robustness, which are not easy to accomplish. In this paper, based on a new neural dynamic, a novel Noise-Tolerance Finite-time convergent ZNN (NTFZNN) model for solving the time-varying Lyapunov equations has been proposed. The NTFZNN model simultaneously converges in finite time and have stable residual error even under unbounded time-varying Noises. Furthermore, the Simplified Finite-time convergent Activation Function (SFAF) with simpler structure is used in the NTFZNN model to reduce model complexity while retaining finite convergence time. Theoretical proofs and numerical simulations are provided in this paper to substantiate the NTFZNN model’s convergence and robustness performances, which are better than performances of the ordinary ZNN model and the Noise-Tolerance ZNN (NTZNN) model. Finally, simulation experiment of using the NTFZNN model to control a wheeled robot manipulator under perturbation validates the superior applicability of the NTFZNN model.
-
a recurrent neural network with predefined time convergence and improved Noise Tolerance for dynamic matrix square root finding
Neurocomputing, 2019Co-Authors: Bolin Liao, Lin XiaoAbstract:Abstract Zeroing neural network (ZNN, or termed Zhang neural network after its inventors) is an effective approach to dynamic matrix square root (DMSR) finding arising in numerous fields of science and engineering. The conventional ZNN models can obtain the theoretical DMSR in infinitely long time or in finite time. However, in some applications especially the ones that require to fulfill hard time constraints, these ZNN models may be not competent to guarantee a timely convergence. Hence, for solving DMSR, a ZNN model with explicitly and antecedently definable convergence time is more preferable. Being robust to external Noises is very significant for a neural network model. Unfortunately, the existing ZNN models exhibit limited Noise-Tolerance capability and the corresponding steady-state residual errors would be theoretically bounded when the ZNN models are perturbed by dynamic bounded non-vanishing Noises. To enhance the existing ZNN models, by using two novel activation functions, this paper for the first time enables the ZNN model to be predefined-time convergent with improved Noise-Tolerance capability. The convergence time of the accelerated ZNN model can be explicitly defined as a prior constant parameter. More importantly, such a predefined-time convergent ZNN (PTZNN) is capable of theoretically and completely enduring dynamic bounded vanishing and non-vanishing Noises. For handling constant Noises such as large constant model-implementation errors, the PTZNN can achieve improved Noise-Tolerance performance as compared with the existing ZNN models. Comparative simulation results demonstrate that the proposed PTZNN delivers superior convergence and robustness performance for solving DMSR in comparison with the existing ZNN models.
-
computing time varying quadratic optimization with finite time convergence and Noise Tolerance a unified framework for zeroing neural network
IEEE Transactions on Neural Networks, 2019Co-Authors: Lin Xiao, Mingxing DuanAbstract:Zeroing neural network (ZNN), as a powerful calculating tool, is extensively applied in various computation and optimization fields. Convergence and Noise-Tolerance performance are always pursued and investigated in the ZNN field. Up to now, there are no unified ZNN models that simultaneously achieve the finite-time convergence and inherent Noise Tolerance for computing time-varying quadratic optimization problems, although this superior property is highly demanded in practical applications. In this paper, for computing time-varying quadratic optimization within finite-time convergence in the presence of various additive Noises, a new framework for ZNN is designed to fill this gap in a unified manner. Specifically, different from the previous design formulas either possessing finite-time convergence or possessing Noise-Tolerance performance, a new design formula with finite-time convergence and Noise Tolerance is proposed in a unified framework (and thus called unified design formula). Then, on the basis of the unified design formula, a unified ZNN (UZNN) is, thus, proposed and investigated in the unified framework of ZNN for computing time-varying quadratic optimization problems in the presence of various additive Noises. In addition, theoretical analyses of the unified design formula and the UZNN model are given to guarantee the finite-time convergence and inherent Noise Tolerance. Computer simulation results verify the superior property of the UZNN model for computing time-varying quadratic optimization problems, as compared with the previously proposed ZNN models.
Andre Goedegebure - One of the best experts on this subject based on the ideXlab platform.
-
effects of a transient Noise reduction algorithm on speech intelligibility in Noise Noise Tolerance and perceived annoyance in cochlear implant users
International Journal of Audiology, 2018Co-Authors: Gertjan J Dingemanse, Jantien L Vroegop, Andre GoedegebureAbstract:AbstractObjective: To evaluate the validity and efficacy of a transient Noise reduction algorithm (TNR) in cochlear implant processing and the interaction of TNR with a continuous Noise reduction algorithm (CNR). Design: We studied the effects of TNR and CNR on the perception of realistic sound samples with transients, using subjective ratings of annoyance, a speech-in-Noise test and a Noise Tolerance test. Study sample: Participants were 16 experienced cochlear implant recipients wearing an Advanced Bionics Naida Q70 processor. Results: CI users rated sounds with transients as moderately annoying. Annoyance was slightly, but significantly reduced by TNR. Transients caused a large decrease in speech intelligibility in Noise and a moderate decrease in Noise Tolerance, measured on the Acceptable Noise Level test. The TNR had no significant effect on Noise Tolerance or on speech intelligibility in Noise. The combined application of TNR and CNR did not result in interactions. Conclusions: The TNR algorithm wa...
-
application of Noise reduction algorithm clearvoice in cochlear implant processing effects on Noise Tolerance and speech intelligibility in Noise in relation to spectral resolution
Ear and Hearing, 2015Co-Authors: Gertjan J Dingemanse, Andre GoedegebureAbstract:Objectives Noise reduction algorithms have recently been introduced in the design of clinically available cochlear implants. This study was intended to (1) evaluate the effect of Noise reduction algorithm "ClearVoice" on Noise Tolerance and on speech intelligibility in noisy conditions at different speech-in-Noise ratios in cochlear implant users, and (2) test the hypothesis that CI recipients with low spectral resolution might benefit more from Noise reduction algorithms than CI users with high spectral resolution. Design A double-blind crossover design was used to measure the effect of the Noise reduction algorithm ClearVoice on Noise Tolerance with the acceptable Noise level (ANL) test and on speech in Noise for three performance levels: speech reception thresholds (SRT) at 50%, 70%, and at a speech to Noise ratio of SRT50% + 11 dB. Furthermore, they tested speech intelligibility in quiet. The effective spectral resolution was measured with a spectral-ripple discrimination test. Twenty users of the Advanced Bionics Harmony processor with HiRes120-processing participated in this study. Results The Noise reduction algorithm led to a significant improvement-a decrease of 3.6 dB-in the ANL test but had no significant effect on any of the three speech-in-Noise performance levels. The improvement in ANL was not significantly correlated with any of the speech-in-Noise measures, nor with the speech-in-Noise ratio in the ANL test. However, higher maximum speech intelligibility in quiet conditions correlated significantly with higher Noise Tolerance. Spectral-ripple discrimination thresholds were not significantly correlated with the effect of Noise reduction on ANL or on speech intelligibility in Noise nor with the speech-in-Noise ratios. The spectral-ripple discrimination thresholds did correlate significantly with maximum speech intelligibility in quiet but not with speech reception thresholds in Noise. Conclusions The Noise reduction algorithm ClearVoice improves Noise Tolerance. However, this study shows no change in speech intelligibility in Noise due to the algorithm. The improvement in Noise Tolerance is not significantly related to spectral-ripple discrimination thresholds, speech intelligibility measures, or signal to Noise ratio. Our hypothesis that CI recipients with low spectral resolution have a greater benefit from Noise reduction than CI users with high spectral resolution does not hold for Noise Tolerance or for speech intelligibility in Noise.
Yoshiaki Kisaka - One of the best experts on this subject based on the ideXlab platform.
-
laser phase Noise Tolerance of uniform and probabilistically shaped qam signals for high spectral efficiency systems
Journal of Lightwave Technology, 2020Co-Authors: Takeo Sasai, Fukutaro Hamaoka, Asuka Matsushita, Masanori Nakamura, Seiji Okamoto, Yoshiaki KisakaAbstract:We numerically and experimentally investigate the laser phase Noise Tolerance of probabilistically shaped (PS) and uniformly shaped (US) quadrature amplitude modulation (QAM) signals. In the simulations, we compare PS-64QAM to US-16QAM, PS-256QAM to US-64QAM, and PS-1024QAM to US-256QAM under the same information rate (IR). We confirm that a sufficient shaping gain is observed with narrow linewidth lasers, whereas degradation of the shaping gain is clearly observed when large phase Noise and high order modulation formats are assumed. In our experiments, we compare polarization-division-multiplexed (PDM) 16-GBd PS-1024QAM and US-256QAM under the same IR using lasers with 0.1-kHz and 40-kHz linewidths. For carrier phase recovery (CPR), we employ a pilot-assisted digital phase locked loop. Results reveal that PS-1024QAM achieves high performance with the 0.1 kHz-laser or >5% pilot ratio, whereas US-256QAM outperforms PS-1024QAM when lasers with 40-kHz linewidth and <5% pilot ratio are used. We also evaluate the pilot ratio dependency of the required optical signal-to-Noise ratio at the forward error correction limit and the achievable information rate. Additionally, we compare the performance of two types of CPR updating schemes: updating phase estimation at only the pilot symbol or at all symbols.
-
experimental analysis of laser phase Noise Tolerance of uniform 256qam and probabilistically shaped 1024qam
Optical Fiber Communication Conference, 2019Co-Authors: Takeo Sasai, Fukutaro Hamaoka, Asuka Matsushita, Masanori Nakamura, Seiji Okamoto, Yoshiaki KisakaAbstract:We experimentally compare laser linewidth Tolerance of US-256QAM and PS-1024QAM. US-256QAM has higher achievable information rate (AIR) with <5% pilot symbol and 40 kHz linewidth, while PS-1024QAM realizes higher AIR than US-256QAM with narrower linewidth.
-
laser phase Noise Tolerance of probabilistically shaped constellations
Optical Fiber Communication Conference, 2018Co-Authors: Seiji Okamoto, Fukutaro Hamaoka, Masanori Nakamura, Yoshiaki KisakaAbstract:We numerically confirmed that required-OSNR gain was obtained by probabilistically-shaped 64/256 QAM compared with uniformly-shaped 16/64 QAM in not only arbitrary white Gaussian Noise condition but also in phase Noise condition.
Muhammad Usman Rafique - One of the best experts on this subject based on the ideXlab platform.
-
a novel recurrent neural network for manipulator control with improved Noise Tolerance
IEEE Transactions on Neural Networks, 2018Co-Authors: Huanqing Wang, Muhammad Usman RafiqueAbstract:In this paper, we propose a novel recurrent neural network to resolve the redundancy of manipulators for efficient kinematic control in the presence of Noises in a polynomial type. Leveraging the high-order derivative properties of polynomial Noises, a deliberately devised neural network is proposed to eliminate the impact of Noises and recover the accurate tracking of desired trajectories in workspace. Rigorous analysis shows that the proposed neural law stabilizes the system dynamics and the position tracking error converges to zero in the presence of Noises. Extensive simulations verify the theoretical results. Numerical comparisons show that existing dual neural solutions lose stability when exposed to large constant Noises or time-varying Noises. In contrast, the proposed approach works well and has a low tracking error comparable to Noise-free situations.
Pinaki Mazumder - One of the best experts on this subject based on the ideXlab platform.
-
a novel technique to improve Noise immunity of cmos dynamic logic circuits
Design Automation Conference, 2004Co-Authors: Li Ding, Pinaki MazumderAbstract:Dynamic CMOS logic circuits are widely employed in high performance VLSI chips in pursuing very high system performance. However, dynamic circuits are inherently less resistant to Noises than static CMOS gates. With the increasing stringent Noise requirement due to aggressive technology scaling, the Noise Tolerance of dynamic circuits has to be first improved for the overall reliable operation of VLSI systems. In this paper, we present a novel Noise-tolerant design technique using circuitry exhibiting a negative differential resistance effect. We have demonstrated that using the proposed method the Noise Tolerance of dynamic logic gates can be improved beyond the level of static CMOS logic gates while the performance advantage of dynamic circuits is still retained.
-
On circuit techniques to improve Noise immunity of CMOS dynamic logic
IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2004Co-Authors: Li Ding, Pinaki MazumderAbstract:Dynamic CMOS logic circuits are widely employed in high-performance VLSI chips in pursuing very high system performance. However, dynamic CMOS gates are inherently less resistant to Noises than static CMOS gates. With the increasing stringent Noise requirement due to aggressive technology scaling, the Noise Tolerance of dynamic circuits has to be first improved for the overall reliable operation of VLSI chips designed using deep submicron process technology. In the literature, a number of design techniques have been proposed to enhance the Noise Tolerance of dynamic logic gates. An overview and classification of these techniques are first presented in this paper. Then, we introduce a novel Noise-tolerant design technique using circuitry exhibiting a negative differential resistance effect. We have demonstrated through analysis and simulation that using the proposed method the Noise Tolerance of dynamic logic gates can be improved beyond the level of static CMOS logic gates while the performance advantage of dynamic circuits is still retained. Simulation results on large fan-in dynamic CMOS logic gates have shown that, at a supply voltage of 1.6 V, the input Noise immunity level can be increased to 0.8 V for about 10% delay overhead and to 1.0 V for only about 20% delay overhead.
