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Journal articles on the topic 'Noisy feedback'

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Author: Grafiati

Published: 10 December 2022

Last updated: 29 January 2023

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1

Lam, V. M. T., C. R. Poole, and P. C. L. Yip. "Exact noise figure of a noisy two-port with feedback." IEE Proceedings G Circuits, Devices and Systems 139, no. 4 (1992): 473. http://dx.doi.org/10.1049/ip-g-2.1992.0074.

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2

Sworder, D. D., and D. S. Chou. "Feedforward/Feedback Controls in a Noisy Environment." IEEE Transactions on Systems, Man, and Cybernetics 16, no. 4 (July 1986): 522–31. http://dx.doi.org/10.1109/tsmc.1986.289255.

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3

Lurie, O. J. "Feedback representation of a noisy two-port." Proceedings of the IEEE 74, no. 1 (1986): 221–22. http://dx.doi.org/10.1109/proc.1986.13438.

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4

Goldobin, D., M. Rosenblum, and A. Pikovsky. "Coherence of noisy oscillators with delayed feedback." Physica A: Statistical Mechanics and its Applications 327, no. 1-2 (September 2003): 124–28. http://dx.doi.org/10.1016/s0378-4371(03)00463-1.

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5

Grossman, Zachary, and David Owens. "An unlucky feeling: Overconfidence and noisy feedback." Journal of Economic Behavior & Organization 84, no. 2 (November 2012): 510–24. http://dx.doi.org/10.1016/j.jebo.2012.08.006.

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6

Zhang, Liangyin, Michael Z. Q. Chen, Chanying Li, and Zhan Shu. "Event-Triggered Control Over Noisy Feedback Channels." IFAC Proceedings Volumes 47, no. 3 (2014): 10493–98. http://dx.doi.org/10.3182/20140824-6-za-1003.00335.

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7

Draper, Stark C., and Anant Sahai. "Variable-length channel coding with noisy feedback." European Transactions on Telecommunications 19, no. 4 (2008): 355–70. http://dx.doi.org/10.1002/ett.1288.

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8

Burnashev, M. V., and H. Yamamoto. "On using noisy feedback in a Gaussian channel." Problems of Information Transmission 50, no. 3 (July 2014): 217–31. http://dx.doi.org/10.1134/s0032946014030028.

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9

Olmstead, Anne J., Navin Viswanathan, Jacqueline M. Albor, and Olivia A. Billetdeaux. "Perceptual training affects linguistic release from masking." Journal of the Acoustical Society of America 151, no. 4 (April 2022): A276. http://dx.doi.org/10.1121/10.0011323.

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Understanding speech in noisy conditions is a problem faced by all listeners. Previous studies have shown that listeners understand target speech better when background speech (masker) is in a different language. This is called Linguistic Release from Masking (LRM). In the current study, we examined whether training on identifying speech in noisy conditions can modulate LRM. In a pre-test/training/post-test design, 60 monolingual American English listeners transcribed English sentences presented in noisy backgrounds. In the pre-test and post-test, all listeners transcribed sentences presented with both English and Dutch maskers without feedback. During training, participants were randomly assigned to transcribe target sentences with Dutch, English, or white noise maskers and received feedback. Results showed an LRM effect in the pre-test; participants transcribed the target sentences better with a Dutch than with an English masker. After training, participants improved in all conditions, but greater improvements in the English masker condition eliminated LRM. Results provide insight into the role of perceptual learning as well as into the nature of informational masking effects underlying LRM. This study serves as a basis for future research examining improvement for speech in speech recognition and changes in LRM.

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10

Li, Gai, Liyang Wang, and Weihua Ou. "Robust Personalized Ranking from Implicit Feedback." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 01 (December 30, 2015): 1659001. http://dx.doi.org/10.1142/s0218001416590011.

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In this paper, we investigate the problem of personalized ranking from implicit feedback (PRIF). It is a more common scenario (e.g. purchase history, click log and page visitation) in recommender systems. The training data are only binary in these problems, reflecting the users’ actions or inactions. One shortcoming of previous PRIF algorithms is noise sensitivity: outliers in training data might bring significant fluctuations in the training process and lead to inaccuracy of the algorithm. In this paper, we propose two robust PRIF algorithms to solve the noise sensitivity problem of existing PRIF algorithms by using the pairwise sigmoid and pairwise fidelity loss functions. These two pairwise loss functions are flexible and can easily be adopted by popular collaborative filtering models such as the matrix factorization (MF) model and the K-nearest-neighbor (KNN) model. A learning process based on stochastic gradient descent with bootstrap sampling is utilized for the optimization. Experiments are conducted on practical datasets containing noisy data points or outliers. Results demonstrate that the proposed algorithms outperform several state-of-the-art one class collaborative filtering (OCCF) algorithms on both the MF and KNN models over different evaluation metrics.

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11

Wang, Junxiong, Debabrota Basu, and Immanuel Trummer. "Procrastinated Tree Search: Black-Box Optimization with Delayed, Noisy, and Multi-Fidelity Feedback." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 9 (June 28, 2022): 10381–90. http://dx.doi.org/10.1609/aaai.v36i9.21280.

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In black-box optimization problems, we aim to maximize an unknown objective function, where the function is only accessible through feedbacks of an evaluation or simulation oracle. In real-life, the feedbacks of such oracles are often noisy and available after some unknown delay that may depend on the computation time of the oracle. Additionally, if the exact evaluations are expensive but coarse approximations are available at a lower cost, the feedbacks can have multi-fidelity. In order to address this problem, we propose a generic extension of hierarchical optimistic tree search (HOO), called ProCrastinated Tree Search (PCTS), that flexibly accommodates a delay and noise-tolerant bandit algorithm. We provide a generic proof technique to quantify regret of PCTS under delayed, noisy, and multi-fidelity feedbacks. Specifically, we derive regret bounds of PCTS enabled with delayed-UCB1 (DUCB1) and delayed-UCB-V (DUCBV) algorithms. Given a horizon T, PCTS retains the regret bound of non-delayed HOO for expected delay of O(log T), and worsens by T^((1-α)/(d+2)) for expected delays of O(T^(1-α)) for α ∈ (0,1]. We experimentally validate on multiple synthetic functions and hyperparameter tuning problems that PCTS outperforms the state-of-the-art black-box optimization methods for feedbacks with different noise levels, delays, and fidelity.

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12

LAI, DEJIAN, and GUANRONG CHEN. "MAKING A DISCRETE DYNAMICAL SYSTEM CHAOTIC: THEORETICAL RESULTS AND NUMERICAL SIMULATIONS." International Journal of Bifurcation and Chaos 13, no. 11 (November 2003): 3437–42. http://dx.doi.org/10.1142/s0218127403008648.

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In this paper, we study state-feedback controller design for controlling the Lyapunov exponents of an n-dimensional dynamical system. We examine some theoretical results and perform numerical simulations for systems with and without noise influence. The controlled Lyapunov exponents are asymptotically normally distributed if the system has noisy inputs. Computer simulations on finite samples are all consistent with the theoretical results.

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13

Braverman, Elena, and Alexandra Rodkina. "Stabilization of difference equations with noisy proportional feedback control." Discrete & Continuous Dynamical Systems - B 22, no. 6 (2017): 2067–88. http://dx.doi.org/10.3934/dcdsb.2017085.

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14

Liu, Cheng-Cheng, Ting-Sheng Wei, Jia-Dong Shi, Zhi-Yong Ding, Juan He, Tao Wu, and Liu Ye. "Optimal measurement-based feedback control on noisy quantum systems." Laser Physics Letters 18, no. 11 (October 15, 2021): 115203. http://dx.doi.org/10.1088/1612-202x/ac2b95.

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15

Liu, Qing Quan. "Observer-Based Quantized Feedback Control via Noisy Communication Channels." Advanced Materials Research 433-440 (January 2012): 6242–49. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.6242.

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This paper investigates the input and output quantized control problem for stochastic linear systems with unbounded and possibly non-Gaussian process disturbance, where sensors, controllers and plants are connected by a noisy digital communication channel. Due to the unbounded process disturbance, a dynamic, logarithmic quantization scheme is proposed. An observer-based control policy is presented to stabilize the unstable plant in the mean square sense. Simulation results show the validity of the proposed quantization and control policy.

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16

Pimenova, Anastasiya V., and Denis S. Goldobin. "Coherence of Noisy Oscillators with Delayed Feedback Inducing Multistability." Journal of Physics: Conference Series 681 (February 3, 2016): 012045. http://dx.doi.org/10.1088/1742-6596/681/1/012045.

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17

Yu Xiang and Young-Han Kim. "Gaussian Channel With Noisy Feedback and Peak Energy Constraint." IEEE Transactions on Information Theory 59, no. 8 (August 2013): 4746–56. http://dx.doi.org/10.1109/tit.2013.2254233.

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18

Abdoli, Javad, Akbar Ghasemi, and Amir Keyvan Khandani. "Interference and X Networks With Noisy Cooperation and Feedback." IEEE Transactions on Information Theory 61, no. 8 (August 2015): 4367–89. http://dx.doi.org/10.1109/tit.2015.2445763.

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19

Ben-Yishai, Assaf, and Ofer Shayevitz. "Interactive Schemes for the AWGN Channel with Noisy Feedback." IEEE Transactions on Information Theory 63, no. 4 (April 2017): 2409–27. http://dx.doi.org/10.1109/tit.2017.2648821.

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20

Chance, Zachary, and David J. Love. "Concatenated Coding for the AWGN Channel With Noisy Feedback." IEEE Transactions on Information Theory 57, no. 10 (October 2011): 6633–49. http://dx.doi.org/10.1109/tit.2011.2165796.

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21

Bratus, Alexander, Ivan Yegorov, and Daniil Yurchenko. "Optimal bounded noisy feedback control for damping random vibrations." Journal of Vibration and Control 24, no. 10 (September 26, 2016): 1874–88. http://dx.doi.org/10.1177/1077546316670072.

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22

Park, Byeong Eon, Kyeong Hoon Kim, Ho Suk Kang, Su Whan Sung, and In-Beum Lee. "Improved Relay Feedback Method Under Noisy and Disturbance Environments." JOURNAL OF CHEMICAL ENGINEERING OF JAPAN 52, no. 5 (May 20, 2019): 430–38. http://dx.doi.org/10.1252/jcej.18we230.

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23

Gershon, Eli, and Uri Shaked. "State-multiplicative Noisy Systems: Robust H ∞ Output-Feedback Control." IFAC-PapersOnLine 50, no. 1 (July 2017): 3823–28. http://dx.doi.org/10.1016/j.ifacol.2017.08.488.

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24

Guo, Zhenyu, Yujuan Sun, Muwei Jian, and Xiaofeng Zhang. "Deep Residual Network with Sparse Feedback for Image Restoration." Applied Sciences 8, no. 12 (November 28, 2018): 2417. http://dx.doi.org/10.3390/app8122417.

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A deep neural network is difficult to train due to a large number of unknown parameters. To increase trainable performance, we present a moderate depth residual network for the restoration of motion blurring and noisy images. The proposed network has only 10 layers, and the sparse feedbacks are added in the middle and the last layers, which are called FbResNet. FbResNet has fast convergence speed and effective denoising performance. In addition, it can also reduce the artificial Mosaic trace at the seam of patches, and visually pleasant output results can be produced from the blurred images or noisy images. Experimental results show the effectiveness of our designed model and method.

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25

Milias-Argeitis, Andreas, Stefan Engblom, Pavol Bauer, and Mustafa Khammash. "Stochastic focusing coupled with negative feedback enables robust regulation in biochemical reaction networks." Journal of The Royal Society Interface 12, no. 113 (December 2015): 20150831. http://dx.doi.org/10.1098/rsif.2015.0831.

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Nature presents multiple intriguing examples of processes that proceed with high precision and regularity. This remarkable stability is frequently counter to modellers' experience with the inherent stochasticity of chemical reactions in the regime of low-copy numbers. Moreover, the effects of noise and nonlinearities can lead to ‘counterintuitive’ behaviour, as demonstrated for a basic enzymatic reaction scheme that can display stochastic focusing (SF). Under the assumption of rapid signal fluctuations, SF has been shown to convert a graded response into a threshold mechanism, thus attenuating the detrimental effects of signal noise. However, when the rapid fluctuation assumption is violated, this gain in sensitivity is generally obtained at the cost of very large product variance, and this unpredictable behaviour may be one possible explanation of why, more than a decade after its introduction, SF has still not been observed in real biochemical systems. In this work, we explore the noise properties of a simple enzymatic reaction mechanism with a small and fluctuating number of active enzymes that behaves as a high-gain, noisy amplifier due to SF caused by slow enzyme fluctuations. We then show that the inclusion of a plausible negative feedback mechanism turns the system from a noisy signal detector to a strong homeostatic mechanism by exchanging high gain with strong attenuation in output noise and robustness to parameter variations. Moreover, we observe that the discrepancy between deterministic and stochastic descriptions of stochastically focused systems in the evolution of the means almost completely disappears, despite very low molecule counts and the additional nonlinearity due to feedback. The reaction mechanism considered here can provide a possible resolution to the apparent conflict between intrinsic noise and high precision in critical intracellular processes.

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26

Yeh, Yi-Liang, Hsuan-Wei Pan, and Yuan-Hong Shen. "Model-Free Output-Feedback Sliding-Mode Control Design for Piezo-Actuated Stage." Machines 11, no. 2 (January 22, 2023): 152. http://dx.doi.org/10.3390/machines11020152.

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Hysteresis in a piezoelectric actuator must be compensated for, and this compensation constitutes the main challenge in the high-precision motion control of piezo-actuated stages. This paper presents an output-feedback sliding-mode control (SMC) scheme to suppress unknown nonlinearity; in this scheme, hysteresis behavior is considered an external disturbance, and complex hysteresis models are thus not required. The scheme functions in the absence of transfer function of system state information, and a robust loop-transfer recovery observer is employed as a noise-free differentiator to estimate the required signal derivatives when the relevant system is in a noisy environment.

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27

Glonti, O. "Optimal Transmission of Gaussian Signals Through A Feedback Channel." gmj 1, no. 4 (August 1994): 353–65. http://dx.doi.org/10.1515/gmj.1994.353.

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Abstract Using the methodology and results of the theory of filtering of conditionally Gaussian processes, the optimal schemes of transmission of Gaussian signals through the noisy feedback channel are constructed under the new power conditions.

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28

CHEN, KUAN-YU, PI-CHENG TUNG, SHIH-LIN LIN, and MONG-TAO TSAI. "CHAOS SYNCHRONIZATION IN THE PRESENCE OF NOISE." International Journal of Modern Physics C 22, no. 12 (December 2011): 1409–18. http://dx.doi.org/10.1142/s0129183111017007.

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In this paper, we first develop a systematic procedure of state feedback control, based on a Lur'e-type system, to analyze the synchronization of two chaotic systems in the presence of random white noise. With the aid of the modified independent component analysis (ICA), the real chaotic signal can be extracted from a noisy source where the chaotic signal has been contaminated by random white noise. Hence, a new scheme has been proposed in this paper to combine the modified ICA design and the state feedback control method for achieving chaos synchronization. The synchronization time can be arbitrarily designed to guarantee stability, even if the system's output is corrupted by measuring noise. A Duffing system example is provided to show the effectiveness of the proposed scheme. The new scheme is first used for control systems with measurement noise which can replace the conventional Kalman filter.

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29

KAPITANIAK, T., LJ KOCAREV, and L. O. CHUA. "CONTROLLING CHAOS WITHOUT FEEDBACK AND CONTROL SIGNALS." International Journal of Bifurcation and Chaos 03, no. 02 (April 1993): 459–68. http://dx.doi.org/10.1142/s0218127493000362.

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We describe an effective method for controlling chaos by coupling a main chaotic system to a new but simple system with easily changeable parameters. The method is applied to Duffing’s oscillator (numerical and analytical study) and to Chua’s circuit (experimental study). The effectiveness of our method in controlling noisy systems, as well as an illustrative application in a mechanical and an electrical system, are discussed.

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30

Damavandi, Ojan Khatib, and David K. Lubensky. "Statistics of noisy growth with mechanical feedback in elastic tissues." Proceedings of the National Academy of Sciences 116, no. 12 (February 28, 2019): 5350–55. http://dx.doi.org/10.1073/pnas.1816100116.

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Tissue growth is a fundamental aspect of development and is intrinsically noisy. Stochasticity has important implications for morphogenesis, precise control of organ size, and regulation of tissue composition and heterogeneity. However, the basic statistical properties of growing tissues, particularly when growth induces mechanical stresses that can in turn affect growth rates, have received little attention. Here, we study the noisy growth of elastic sheets subject to mechanical feedback. Considering both isotropic and anisotropic growth, we find that the density–density correlation function shows power law scaling. We also consider the dynamics of marked, neutral clones of cells. We find that the areas (but not the shapes) of two clones are always statistically independent, even when they are adjacent. For anisotropic growth, we show that clone size variance scales like the average area squared and that the mode amplitudes characterizing clone shape show a slow1/ndecay, where n is the mode index. This is in stark contrast to the isotropic case, where relative variations in clone size and shape vanish at long times. The high variability in clone statistics observed in anisotropic growth is due to the presence of two soft modes—growth modes that generate no stress. Our results lay the groundwork for more in-depth explorations of the properties of noisy tissue growth in specific biological contexts.

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31

Wei, Thomas, Taylor A. Kessler Faulkner, and Andrea L. Thomaz. "Extending Policy Shaping to Continuous State Spaces (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (May 18, 2021): 15919–20. http://dx.doi.org/10.1609/aaai.v35i18.17956.

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Policy Shaping is a Human-in-the-loop Reinforcement Learning (HRL) algorithm. We extend this work to continuous states with our algorithm, Deep Policy Shaping (DPS). DPS uses a feedback neural network that learns the optimality of actions from noisy feedback combined with an RL algorithm. In simulation, we find that DPS outperforms or matches baselines averaged over multiple hyperparameter settings and varying feedback correctness.

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32

Bao, Lei, Mikael Skoglund, and Karl Henrik Johansson. "Iterative Encoder-Controller Design for Feedback Control Over Noisy Channels." IEEE Transactions on Automatic Control 56, no. 2 (February 2011): 265–78. http://dx.doi.org/10.1109/tac.2010.2051833.

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33

Li, Chong. "Capacity-Achieving Codes for Noisy Feedback Channels: A Necessary Condition." IEEE Communications Letters 18, no. 6 (June 2014): 1007–10. http://dx.doi.org/10.1109/lcomm.2014.2319820.

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34

Burnashev, M. V., and H. Yamamoto. "On the reliability function for a BSC with noisy feedback." Problems of Information Transmission 46, no. 2 (June 2010): 103–21. http://dx.doi.org/10.1134/s0032946010020018.

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35

Wu, Tianyu, and Vincent K. N. Lau. "Robust Precoder Adaptation for MIMO Links With Noisy Limited Feedback." IEEE Transactions on Information Theory 55, no. 4 (April 2009): 1640–49. http://dx.doi.org/10.1109/tit.2009.2013049.

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36

Kim, Young-Han, Amos Lapidoth, and Tsachy Weissman. "Error Exponents for the Gaussian Channel With Active Noisy Feedback." IEEE Transactions on Information Theory 57, no. 3 (March 2011): 1223–36. http://dx.doi.org/10.1109/tit.2011.2104991.

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37

Murthy, C. R., Jun Zheng, and B. D. Rao. "Performance of Quantized Equal Gain Transmission With Noisy Feedback Channels." IEEE Transactions on Signal Processing 56, no. 6 (June 2008): 2451–60. http://dx.doi.org/10.1109/tsp.2007.914967.

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38

Ekbatani, Siavash, and Hamid Jafarkhani. "Combining Beamforming and Space-Time Coding Using Noisy Quantized Feedback." IEEE Transactions on Communications 57, no. 5 (May 2009): 1280–86. http://dx.doi.org/10.1109/tcomm.2009.05.070272.

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39

Sollund, Tomas, and Harry Leib. "Feedback Communication with Reduced Delay over Noisy Time-Dispersive Channels." IEEE Transactions on Communications 60, no. 3 (March 2012): 688–705. http://dx.doi.org/10.1109/tcomm.2012.12.100001.

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40

Johansson, Anders, Kai Ramsch, Martin Middendorf, and David J. T. Sumpter. "Tuning positive feedback for signal detection in noisy dynamic environments." Journal of Theoretical Biology 309 (September 2012): 88–95. http://dx.doi.org/10.1016/j.jtbi.2012.05.023.

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41

Horbelt, W., J. Timmer, and H. U. Voss. "Parameter estimation in nonlinear delayed feedback systems from noisy data." Physics Letters A 299, no. 5-6 (July 2002): 513–21. http://dx.doi.org/10.1016/s0375-9601(02)00748-x.

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42

Liaw, Yu-Min, and Pi-Cheng Tung. "Controlling chaos via state feedback cancellation under a noisy environment." Physics Letters A 211, no. 6 (March 1996): 350–56. http://dx.doi.org/10.1016/0375-9601(96)00009-6.

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43

Bokes, Pavol, Yen Ting Lin, and Abhyudai Singh. "High Cooperativity in Negative Feedback can Amplify Noisy Gene Expression." Bulletin of Mathematical Biology 80, no. 7 (April 25, 2018): 1871–99. http://dx.doi.org/10.1007/s11538-018-0438-y.

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44

Lee, Jietae, Jin-Su Kim, Jeonguk Byeon, Su Whan Sung, and Thomas F. Edgar. "Relay feedback identification for processes under drift and noisy environments." AIChE Journal 57, no. 7 (October 1, 2010): 1809–16. http://dx.doi.org/10.1002/aic.12394.

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45

Zhao, Yang, Shiyan Sun, and Ping Wei. "Non-feedback control of Lorenz chaotic system." Journal of Physics: Conference Series 2258, no. 1 (April 1, 2022): 012056. http://dx.doi.org/10.1088/1742-6596/2258/1/012056.

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Abstract Non-feedback chaos control of Lorenz chaotic system is realized with non-resonant parametric drive. The control input is a periodic signal whose frequency is far higher than the system characteristic frequency. The feasibility of this scheme is proved by averaging method and Lyapunov method, and the condition of control parameter is obtained. Numerical researches show that the controlled system can reach stable state rapidly even when the control signal is noisy.

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46

Cao, Zhixing, and Ramon Grima. "Accuracy of parameter estimation for auto-regulatory transcriptional feedback loops from noisy data." Journal of The Royal Society Interface 16, no. 153 (April 3, 2019): 20180967. http://dx.doi.org/10.1098/rsif.2018.0967.

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Bayesian and non-Bayesian moment-based inference methods are commonly used to estimate the parameters defining stochastic models of gene regulatory networks from noisy single cell or population snapshot data. However, a systematic investigation of the accuracy of the predictions of these methods remains missing. Here, we present the results of such a study using synthetic noisy data of a negative auto-regulatory transcriptional feedback loop, one of the most common building blocks of complex gene regulatory networks. We study the error in parameter estimation as a function of (i) number of cells in each sample; (ii) the number of time points; (iii) the highest-order moment of protein fluctuations used for inference; (iv) the moment-closure method used for likelihood approximation. We find that for sample sizes typical of flow cytometry experiments, parameter estimation by maximizing the likelihood is as accurate as using Bayesian methods but with a much reduced computational time. We also show that the choice of moment-closure method is the crucial factor determining the maximum achievable accuracy of moment-based inference methods. Common likelihood approximation methods based on the linear noise approximation or the zero cumulants closure perform poorly for feedback loops with large protein–DNA binding rates or large protein bursts; this is exacerbated for highly heterogeneous cell populations. By contrast, approximating the likelihood using the linear-mapping approximation or conditional derivative matching leads to highly accurate parameter estimates for a wide range of conditions.

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47

LI, X. Y., J. H. YANG, and X. B. LIU. "THE COHERENCE RESONANCE IN VAN DER POL SYSTEM INDUCED BY NOISE RECYCLING." Fluctuation and Noise Letters 11, no. 02 (June 2012): 1250002. http://dx.doi.org/10.1142/s0219477512500022.

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The phenomenon of coherence resonance (CR) in a delayed noisy Van der Pol system with supercritical Hopf bifurcation, which is influenced by a recycled noise, is numerically studied. Different from the traditional CR theory, in this paper, the characteristics of CR is affected by the time delay in the input noise. Namely, the CR is weakened or enhanced by the time delay feedback. Moreover, we find that several characteristics of this particular system vary periodically and its period has some certain relation with the natural frequency. By using the results given by the paper, we can control the noise-induced motion by modulating the time delay in noise.

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48

Grossberg, Stephen. "Brain feedback and adaptive resonance in speech perception." Behavioral and Brain Sciences 23, no. 3 (June 2000): 332–33. http://dx.doi.org/10.1017/s0140525x00303247.

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The brain contains ubiquitous reciprocal bottom-up and top-down intercortical and thalamocortical pathways. These resonating feedback pathways may be essential for stable learning of speech and language codes and for context-sensitive selection and completion of noisy speech sounds and word groupings. Context-sensitive speech data, notably interword backward effects in time, have been quantitatively modeled using these concepts but not with purely feedforward models.

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49

Mahajan, Aditya, and Demosthenis Teneketzis. "On the design of globally optimal communication strategies for real-time noisy communication systems with noisy feedback." IEEE Journal on Selected Areas in Communications 26, no. 4 (May 2008): 580–95. http://dx.doi.org/10.1109/jsac.2008.080502.

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50

Yeh, Yi-Liang. "A Robust Noise-Free Linear Control Design for Robot Manipulator with Uncertain System Parameters." Actuators 10, no. 6 (June 5, 2021): 121. http://dx.doi.org/10.3390/act10060121.

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In robot control, the sliding mode control is known for its robustness against external disturbances and system uncertainties. However, it has the disadvantage of control chattering, which can damage the actuator and degrade system performance. With a new stability proof, this paper presents an alternative simple linear feedback control that can cope with large system uncertainties and suppress large external disturbances, doing so as effectively as sliding mode control does. The advantage of using linear control is that the control law is simple and control chattering can be avoided. Moreover, a noise-free control scheme is proposed as an improvement of the feedback control; the modified design preserves the advantages of linear control and generates a chattering-free control signal even in a noisy environment.

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