Relevant bibliographies by topics / Feedforward

Academic literature on the topic 'Feedforward'

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Published: 4 June 2021
Last updated: 11 March 2023

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Journal articles on the topic "Feedforward"

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Back, A. D., and A. C. Tsoi. "FIR and IIR Synapses, a New Neural Network Architecture for Time Series Modeling." Neural Computation 3, no. 3 (September 1991): 375–85. http://dx.doi.org/10.1162/neco.1991.3.3.375.

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A new neural network architecture involving either local feedforward global feedforward, and/or local recurrent global feedforward structure is proposed. A learning rule minimizing a mean square error criterion is derived. The performance of this algorithm (local recurrent global feedforward architecture) is compared with a local-feedforward global-feedforward architecture. It is shown that the local-recurrent global-feedforward model performs better than the local-feedforward global-feedforward model.
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Douglas, Michael R. "Holomorphic feedforward networks." Pure and Applied Mathematics Quarterly 18, no. 1 (2022): 251–68. http://dx.doi.org/10.4310/pamq.2022.v18.n1.a7.

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Hovd, Morten, and Robert R. Bitmead. "Feedforward for stabilization." IFAC Proceedings Volumes 42, no. 11 (2009): 602–6. http://dx.doi.org/10.3182/20090712-4-tr-2008.00097.

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Conaghan, P., and A. Lockey. "Feedback to feedforward." Notfall + Rettungsmedizin 12, S2 (September 2009): 45–48. http://dx.doi.org/10.1007/s10049-009-1222-1.

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Kluger, Avraham N., and Dina Nir. "The feedforward interview." Human Resource Management Review 20, no. 3 (September 2010): 235–46. http://dx.doi.org/10.1016/j.hrmr.2009.08.002.

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Hirose, Noriaki, and Ryosuke Tajima. "Deadbeat Feedforward Compensation with Frequency Shaping of Position Feedforward Controller." IEEJ Journal of Industry Applications 6, no. 2 (2017): 100–106. http://dx.doi.org/10.1541/ieejjia.6.100.

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Xia, Lian, Jing Qiu, and Jiang Han. "Linear Motor Control Algorithm and Experimental Research Based on Feedforward Fuzzy PID." Key Engineering Materials 620 (August 2014): 363–68. http://dx.doi.org/10.4028/www.scientific.net/kem.620.363.

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In this paper, theory analysis, the MATLAB research and experimental verification about feedforward fuzzy PID control have been performed by combining the characteristics of the PID, feedforward control and fuzzy control. Simulation results show that the feedforward fuzzy PID control could improve the response speed of the system and reduce the tracking error of the system which shows the obvious superiority compared with the PID, feedforward PID, and fuzzy PID. Load experiment for such four kinds of control modes is done on the linear motor platform, and the experimental results show that the accuracy of the feedforward fuzzy PID control is obviously higher than the other three kinds of control modes and the feedforward fuzzy PID control is easier to be implemented. The position error of feedforward fuzzy PID control is changeless during the load change, and the change of the speed tracking error is small, which proves that the feedforward fuzzy PID control is suitable for the condition of load change or the great disturbance.
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MANGAL, MANISH, and MANU PRATAP SINGH. "ANALYSIS OF MULTIDIMENSIONAL XOR CLASSIFICATION PROBLEM WITH EVOLUTIONARY FEEDFORWARD NEURAL NETWORKS." International Journal on Artificial Intelligence Tools 16, no. 01 (February 2007): 111–20. http://dx.doi.org/10.1142/s0218213007003229.

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This paper describes the application of two evolutionary algorithms to the feedforward neural networks used in classification problems. Besides of a simple backpropagation feedforward algorithm, the paper considers the genetic algorithm and random search algorithm. The objective is to analyze the performance of GAs over the simple backpropagation feedforward in terms of accuracy or speed in this problem. The experiments considered a feedforward neural network trained with genetic algorithm/random search algorithm and 39 types of network structures and artificial data sets. In most cases, the evolutionary feedforward neural networks seemed to have better of equal accuracy than the original backpropagation feedforward neural network. We found few differences in the accuracy of the networks solved by applying the EAs, but found ample differences in the execution time. The results suggest that the evolutionary feedforward neural network with random search algorithm might be the best algorithm on the data sets we tested.
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Alekseev, A. A., and V. V. Tyutikov. "Method for tuning feedforward in electric feed drive control systems." Vestnik IGEU, no. 6 (December 28, 2021): 45–53. http://dx.doi.org/10.17588/2072-2672.2021.6.045-053.

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The electric feed drive used in metal-cutting machines like any high-precision electric drive requires high accuracy of reference processing and robustness against perturbations. For this purpose, feedforwards are added to the position controller to improve set point processing time and to compensate for disturbances. Feedforwards are usually tuned manually when the machine is setup, either by applying a series of tests on the motor or by calculation. The calculation requires some information about the magnitudes of disturbances that can be compensated by appropriate feedforwards, but this information is not always available a priori. In this paper, we propose tuning the feedforward coefficients based on the results of the parametric identification of the values of the torques acting on the electric drive, as well as the apparent moment of inertia. For parametric identification the methods of electric drive theory, method of least squares, and digital signal processing method are used; mathematical modeling method is applied to assess the compensation quality. The authors propose the method of tuning the parameters of the control system of electric feed drive based on parametric identification of the values of torques acting on the motor and/or the operating device. The results of control system simulation show both high identification accuracy and significant reduction of dynamic control error when feedforwards are activated. The considered structure of the control system and the proposed algorithm of identification and adjustment of its parameters can be used in electric drives of metal-cutting machine tools. The simulation results have shown that the use of feedforwards, tuned in accordance with the algorithm, enable to reduce the dynamic position tracking error by more than 50 times, which can be critical in contour machining.
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Jensen, Konrad Johan, Morten Kjeld Ebbesen, and Michael Rygaard Hansen. "Adaptive Feedforward Control of a Pressure Compensated Differential Cylinder." Applied Sciences 10, no. 21 (November 5, 2020): 7847. http://dx.doi.org/10.3390/app10217847.

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This paper presents the design, simulation and experimental verification of adaptive feedforward motion control for a hydraulic differential cylinder. The proposed solution is implemented on a hydraulic loader crane. Based on common adaptation methods, a typical electro-hydraulic motion control system has been extended with a novel adaptive feedforward controller that has two separate feedforward states, i.e, one for each direction of motion. Simulations show convergence of the feedforward states, as well as 23% reduction in root mean square (RMS) cylinder position error compared to a fixed gain feedforward controller. The experiments show an even more pronounced advantage of the proposed controller, with an 80% reduction in RMS cylinder position error, and that the separate feedforward states are able to adapt to model uncertainties in both directions of motion.
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Dissertations / Theses on the topic "Feedforward"

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Johansson, Björn. "Feedforward Control in Dynamic Situations." Licentiate thesis, Linköping University, Linköping University, CSE - Cognitive Systems Engineering Laboratory, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-5690.

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This thesis proposal discusses control of dynamic systems and its relation to time. Although much research has been done concerning control of dynamic systems and decision making, little research exists about the relationship between time and control. Control is defined as the ability to keep a target system/process in a desired state. In this study, properties of time such as fast, slow, overlapping etc, should be viewed as a relation between the variety of a controlling system and a target system. It is further concluded that humans have great difficulties controlling target systems that have slow responding processes or "dead" time between action and response. This thesis proposal suggests two different studies to adress the problem of human control over slow responding systems and dead time in organisational control.


Report code: LiU-Tek-Lic-2003:17.
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Johansson, Björn. "Feedforward control in dynamic situations /." Linköping : Univ, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-5690.

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Smith, Alison M. "A wideband adaptive feedforward amplifier lineariser." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq24241.pdf.

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Khan, Altaf Hamid. "Feedforward neural networks with constrained weights." Thesis, University of Warwick, 1996. http://wrap.warwick.ac.uk/4332/.

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The conventional multilayer feedforward network having continuous-weights is expensive to implement in digital hardware. Two new types of networks are proposed which lend themselves to cost-effective implementations in hardware and have a fast forward-pass capability. These two differ from the conventional model in having extra constraints on their weights: the first allows its weights to take integer values in the range [-3,3] only, whereas the second restricts its synapses to the set {-1,0,1} while allowing unrestricted offsets. The benefits of the first configuration are in having weights which are only 3-bits deep and a multiplication operation requiring a maximum of one shift, one add, and one sign-change instruction. The advantages of the second are in having 1-bit synapses and a multiplication operation which consists of a single sign-change instruction. The procedure proposed for training these networks starts like the conventional error backpropagation procedure, but becomes more and more discretised in its behaviour as the network gets closer to an error minimum. Mainly based on steepest descent, it also has a perturbation mechanism to avoid getting trapped in local minima, and a novel mechanism for rounding off 'near integers'. It incorporates weight elimination implicitly, which simplifies the choice of the start-up network configuration for training. It is shown that the integer-weight network, although lacking the universal approximation capability, can implement learning tasks, especially classification tasks, to acceptable accuracies. A new theoretical result is presented which shows that the multiplier-free network is a universal approximator over the space of continuous functions of one variable. In light of experimental results it is conjectured that the same is true for functions of many variables. Decision and error surfaces are used to explore the discrete-weight approximation of continuous-weight networks using discretisation schemes other than integer weights. The results suggest that provided a suitable discretisation interval is chosen, a discrete-weight network can be found which performs as well as a continuous-weight networks, but that it may require more hidden neurons than its conventional counterpart. Experiments are performed to compare the generalisation performances of the new networks with that of the conventional one using three very different benchmarks: the MONK's benchmark, a set of artificial tasks designed to compare the capabilities of learning algorithms, the 'onset of diabetes mellitus' prediction data set, a realistic set with very noisy attributes, and finally the handwritten numeral recognition database, a realistic but very structured data set. The results indicate that the new networks, despite having strong constraints on their weights, have generalisation performances similar to that of their conventional counterparts.
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Shah, Jagesh V. (Jagesh Vijaykumar). "Learning dynamics in feedforward neural networks." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/36541.

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Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.
Includes bibliographical references (leaves 108-115).
by Jagesh V. Shah.
M.S.
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Lotter, Paul. "Development of feedforward RF power amplifier." Thesis, Cape Peninsula University of Technology, 2006. http://hdl.handle.net/20.500.11838/2206.

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Thesis (MTech(Electrical Engineering))--Cape Peninsula University of Technology, 2006.
Electronic communication systems have become an integral part of our everyday lives. RF (Radio Frequency) power amplifiers form part of the fundamental building blocks of an electronic communication system. RF power amplifiers can also be one of the major causes of distortion in an electronic communication system. This thesis describes the linearity requirement for a RF power amplifier that is used in a transmitter section of an electronic communication system. Furthermore, five different linearisation techniques are presented and their characteristics compared. Since a power amplifier employing the Feedforward linearisation technique was designed, built and tested, this thesis focuses on the Feedforward technique. The design methods for the various Feedforward components are presented. The measured parameters of the Feedforward linearised amplifier are compared with the measured parameters of a non-linearised amplifier.
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Leonard, Julia Anne. "The feedforward control of posture and movement." Thesis, McGill University, 2013. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=114142.

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Goal-directed arm movements performed in the standing position potentially disturb the body's equilibrium as a result of the multi-linked structure of the musculoskeletal system. To compensate for these disturbances and ensure that stability is maintained, the central nervous system (CNS) organizes postural adjustments preceding and accompanying the voluntary movement in a feedforward manner (Massion 1992) using knowledge of the dynamics of the body (Bouisset and Zattara 1981). To date, most studies investigating the control of posture during voluntary movements in humans have focused on either the role of the postural activity preceding the movement or on the temporal structure of these anticipatory postural adjustments (APAs) with respect to the focal movement. As such, detailed knowledge about the spatial organization of postural activity is lacking. Furthermore, it is not clear how posture is coordinated when the goal of a voluntary movement changes online. Therefore, the studies in this thesis were aimed at addressing these questions to develop a greater understanding of the organization of feedforward postural control during voluntary movements. Muscle activity, kinetics and kinematics were recorded as subjects performed unperturbed and perturbed reaching movements to targets located in multiple directions while standing. Feedforward postural control strategies preceding and accompanying the reaching movements were quantified. Characterization of the spatial and temporal patterns of muscle activity and ground reaction forces of postural adjustments preceding reach movements revealed that muscle activity was directionally-tuned to reach direction and forces that were constrained to two principal directions. Also, muscle synergies were able to explain the spatial and temporal variability in postural muscle activity in the period preceding the reaching movements, suggesting that a modular organization of muscle recruitment is adopted for this task. Overall, these strategies are similar to those observed for feedback postural responses, suggesting that the CNS relies on shared neural structures for controlling posture in both modes of control. Lastly, the nature of postural control was examined when reaching movements were perturbed with a shift of the visual target after the reaching movement was initiated. Here, muscle activity in the legs was consistently modulated prior to changes in the muscle activity related to the online correction of the arm trajectory.Taken together, the findings of this thesis provide important insights into how the brain coordinates the control of posture and movement. This work provides a measure of feedforward postural control strategies in healthy, young adults as a first step to understanding how and why deficits in balance control may occur during the execution of voluntary movements in fall-prone individuals.
Les mouvements volontaires effectués dans la position debout peuvent engendrer des perturbations de l'équilibre en raison de la structure complexe du système musculo-squelettique. Pour amorcer ces perturbations et s'assurer que l'équilibre est maintenu, le système nerveux central (SNC) amorce le déplacement du centre de masse (CM) par la mise en jeu d'ajustements posturaux avant et accompagnant les mouvements programmés en mode proactif (Massion 1992) en utilisant des représentations internes du corps et de l'environnement. À ce jour, la majorité des études portant sur le contrôle de la posture lors des mouvements volontaires chez l'homme ont comme but soit l'identification du rôle ou la caractérisation de la structure temporelle de ces ajustements posturaux anticipateurs. Cependant, une connaissance approfondie concernant l'organisation spatiale de l'activité posturale est manquante. De plus, ce n'est pas évident comment la posture est coordonnée lorsque le but du mouvement change après le commencement du mouvement. Ainsi, les études présentées ici ont comme but de répondre à ces questions pour développer une meilleure compréhension de l'organisation centrale de la posture et le mouvement. Les signaux électromyographiques, les forces de réaction au sol et la cinématique tridimensionnelle ont été enregistrés pendant que les sujets effectuaient des mouvements de pointage vers des cibles distinctes dans la position debout. Les stratégies posturales organisées en mode proactif ont été quantifiées sans pertubations et avect des pertubations visuomotrices des movements d'atteinte. La caractérisation de l'organisation spatiale et temporelle de l'éléctromyographie et des forces appliquées au sol ont révélé que l'activité des muscles était biaisée vers la direction de pointage ('directionally-tuned') mais que les forces au sol étaient appliquées dans un nombre de directions limitées ('force constraint strategy'). De plus, la variabilité spatiale et temporelle de l'activité des muscles posturaux était expliquée par les synergies musculaires. Ceci suggère qu'une organisation modulaire est utilisée par le SNC pour faciliter la tâche de contrôle de la posture. Ces stratégies sont similaires à celles observées pour les ajustements posturaux compensatoires (à base de 'feedback' ou rétroaction), ce qui suggère que le SNC dépend des mêmes structures neuronales pour contrôler la posture dans la mode proactif et rétroactif. Par la suite, la nature du signal pour le contrôle de la posture a été examinée lors des mouvements de pointage qui ont été perturbés avec un déplacement de la cible visuelle après que le mouvement ait été commencé. Ici, l'activité musculaire dans les jambes était modulée avant la modulation de l'activité musculaire liée à la correction de la trajectoire du bras. Ensemble, les conclusions de cette thèse fournissent un aperçu important sur la façon dont le cerveau coordonne le contrôle de la posture et du mouvement. Les résultats présentés supportent la conclusion que les commandes centrales pour la posture et le mouvement interagissent dans le SNC, et que les structures neuronales sont partagées pour la posture organisée de façon anticipatoire, ou proactif, et compensatoire. Les stratégies posturales typiques dans les jeunes adultes en santé sont quantifiées et forment une base de données pour la comparaison avec des gens sujets au déséquilibre lors de la performance des mouvements volontaires.
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Unar, Mukhtiar Ali. "Ship steering control using feedforward neural networks." Thesis, University of Glasgow, 1999. http://theses.gla.ac.uk/4493/.

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One significant problem in the design of ship steering control systems is that the dynamics of the vessel change with operating conditions such as the forward speed of the vessel, the depth of the water and loading conditions etc. Approaches considered in the past to overcome these difficulties include the use of self adaptive control systems which adjust the control characteristics on a continuous basis to suit the current operating conditions. Artificial neural networks have been receiving considerable attention in recent years and have been considered for a variety of applications where the characteristics of the controlled system change significantly with operating conditions or with time. Such networks have a configuration which remains fixed once the training phase is complete. The resulting controlled systems thus have more predictable characteristics than those which are found in many forms of traditional self-adaptive control systems. In particular, stability bounds can be investigated through simulation studies as with any other form of controller having fixed characteristics. Feedforward neural networks have enjoyed many successful applications in the field of systems and control. These networks include two major categories: multilayer perceptrons and radial basis function networks. In this thesis, we explore the applicability of both of these artificial neural network architectures for automatic steering of ships in a course changing mode of operation. The approach that has been adopted involves the training of a single artificial neural network to represent a series of conventional controllers for different operating conditions. The resulting network thus captures, in a nonlinear fashion, the essential characteristics of all of the conventional controllers. Most of the artificial neural network controllers developed in this thesis are trained with the data generated through simulation studies. However, experience is also gained of developing a neuro controller on the basis of real data gathered from an actual scale model of a supply ship. Another important aspect of this work is the applicability of local model networks for modelling the dynamics of a ship. Local model networks can be regarded as a generalized form of radial basis function networks and have already proved their worth in a number of applications involving the modelling of systems in which the dynamic characteristics can vary significantly with the system operating conditions. The work presented in this thesis indicates that these networks are highly suitable for modelling the dynamics of a ship.
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Tebbs, Robert. "Functionality constraints in feedforward neuromorphic learning systems." Thesis, University of Surrey, 1995. http://epubs.surrey.ac.uk/804354/.

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Chen, Francis Xinghang. "Modeling human vision using feedforward neural networks." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/112824.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 81-86).
In this thesis, we discuss the implementation, characterization, and evaluation of a new computational model for human vision. Our goal is to understand the mechanisms enabling invariant perception under scaling, translation, and clutter. The model is based on I-Theory [50], and uses convolutional neural networks. We investigate the explanatory power of this approach using the task of object recognition. We find that the model has important similarities with neural architectures and that it can reproduce human perceptual phenomena. This work may be an early step towards a more general and unified human vision model.
by Francis Xinghang Chen.
M. Eng.
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Books on the topic "Feedforward"

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Johansson, Björn. Feedforward control in dynamic situations. Linköping: Department of Computer and Information Science, Linköpings universitet, 2003.

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Zevlaris, Charalambos. Feedforward squarewave FM data link. Manchester: UMIST, 1994.

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Konstantinou, K. Feedforward linearization of microwave transmitter amplifiers. Manchester: UMIST, 1995.

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Khan, Altaf Hamid. Feedforward neural networks with constrained weights. [s.l.]: typescript, 1996.

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Duggan, D. M. Investigation of random feedforward Boolean neural networks. Manchester: UMIST, 1993.

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Wally, Merrill, and United States. National Aeronautics and Space Administration., eds. A comparative robustness evaluation of feedforward neurofilters. [Washington, DC]: National Aeronautics and Space Administration, 1993.

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W, Sandberg I., ed. Nonlinear dynamical systems: Feedforward neural network perspectives. New York: John Wiley, 2001.

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Liu, Biao. Adaptive feedforward controllers for active noise control. Aachen: Shaker, 2001.

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Mortimer, I. Bounding the cognitive domain in feedforward neural networks. Manchester: UMIST, 1997.

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Kuan, Chung-Ming. Forecasting exchange rates using feedforward and recurrent neural networks. Champaign: University of Illinois at Urbana-Champaign, 1993.

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Book chapters on the topic "Feedforward"

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Masters, Timothy. "Feedforward Networks." In Deep Belief Nets in C++ and CUDA C: Volume 3, 1–22. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3721-2_1.

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Isermann, Rolf. "Feedforward Control." In Digital Control Systems, 56–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-86420-9_6.

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Fleming, Andrew J., and Kam K. Leang. "Feedforward Control." In Design, Modeling and Control of Nanopositioning Systems, 251–73. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06617-2_9.

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Visioli, Antonio, and Qing-Chang Zhong. "Feedforward Control." In Control of Integral Processes with Dead Time, 95–120. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-070-0_6.

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Thathachar, M. A. L., and P. S. Sastry. "Feedforward Networks." In Networks of Learning Automata, 105–38. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/978-1-4419-9052-5_3.

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Gooch, Jan W. "Feedforward Control." In Encyclopedic Dictionary of Polymers, 298. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_4818.

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Brause, Rüdiger W. "Einschichtige feedforward Netze." In Neuronale Netze, 89–169. Wiesbaden: Vieweg+Teubner Verlag, 1995. http://dx.doi.org/10.1007/978-3-322-93994-4_3.

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Gegov, Alexander. "Feedforward Fuzzy Networks." In Fuzzy Networks for Complex Systems, 161–200. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15600-7_7.

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Zhang, Xiang-Sun. "Feedforward Neural Networks." In Nonconvex Optimization and Its Applications, 95–136. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4757-3167-5_6.

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Masters, Timothy. "Supervised Feedforward Networks." In Deep Belief Nets in C++ and CUDA C: Volume 1, 9–89. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3591-1_2.

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Conference papers on the topic "Feedforward"

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Xian Li, Qing-Guo Wang, and Wen-Jian Cai. "Approximate feedforward control." In 2015 10th Asian Control Conference (ASCC). IEEE, 2015. http://dx.doi.org/10.1109/ascc.2015.7244555.

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Li, Xian, Shuai Liu, Kok Kiong Tan, Qing-Guo Wang, and Wen-Jian Cai. "Predictive feedforward control." In 2016 12th IEEE International Conference on Control and Automation (ICCA). IEEE, 2016. http://dx.doi.org/10.1109/icca.2016.7505377.

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Pearson, Ronald K. "Analysis of feedforward networks." In San Diego '92, edited by Su-Shing Chen. SPIE, 1992. http://dx.doi.org/10.1117/12.130827.

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DeBruin, James C., James M. B. Royalty, Marty Wand, and Edwin Allen. "Feedforward stabilization test bed." In Aerospace/Defense Sensing and Controls, edited by Michael K. Masten and Larry A. Stockum. SPIE, 1996. http://dx.doi.org/10.1117/12.241916.

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Hickson, M. T., D. K. Paul, P. Gardner, and K. Konstantinou. "High Efficiency Feedforward Linearizers." In 24th European Microwave Conference, 1994. IEEE, 1994. http://dx.doi.org/10.1109/euma.1994.337313.

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Ishihara, Abraham, Yoo Yeh, Parth Kumar, Nick Alley, and Jim Neidhoeffer. "Adaptive Feedforward Aircraft Control." In AIAA Infotech@Aerospace 2010. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2010. http://dx.doi.org/10.2514/6.2010-3418.

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Garcia, R. Ferreiro, F. J. Perez Castelo, and J. Vidal Paz. "Multivariable fuzzy feedforward compensation." In 2001 European Control Conference (ECC). IEEE, 2001. http://dx.doi.org/10.23919/ecc.2001.7076449.

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Ying Luo, YangQuan Chen, and YouGuo Pi. "Fractional order adaptive feedforward cancellation." In 2011 American Control Conference. IEEE, 2011. http://dx.doi.org/10.1109/acc.2011.5991265.

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Balachandran, Balakumar, Arun Sampath, and John Park. "Feedforward active interior noise control." In Smart Structures & Materials '95, edited by Inderjit Chopra. SPIE, 1995. http://dx.doi.org/10.1117/12.208304.

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Ock, Sungmin, Jaegan Ko, and Ranjit Gharpurey. "A Cartesian Feedback Feedforward Transmitter." In 2011 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2011. http://dx.doi.org/10.1109/iscas.2011.5937538.

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Reports on the topic "Feedforward"

1

Noga, Andrew J. An Introduction to a Feedforward Demodulator. Fort Belvoir, VA: Defense Technical Information Center, July 2000. http://dx.doi.org/10.21236/ada380206.

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Brabel, Michael J. Basin Sculpting a Hybrid Recurrent Feedforward Neural Network. Fort Belvoir, VA: Defense Technical Information Center, January 1998. http://dx.doi.org/10.21236/ada336386.

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Schmidt, Vincent A., and Jane M. Binner. Analyzing Divisia Rules Extracted from a Feedforward Neural Network. Fort Belvoir, VA: Defense Technical Information Center, March 2006. http://dx.doi.org/10.21236/ada457596.

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Rao, N. S. V., V. Protopopescu, R. C. Mann, E. M. Oblow, and S. S. Iyengar. Learning algorithms for feedforward networks based on finite samples. Office of Scientific and Technical Information (OSTI), September 1994. http://dx.doi.org/10.2172/10190716.

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Mu, Ruihui, and Xiaoqin Zeng. Improved Webpage Classification Technology Based on Feedforward Backpropagation Neural Network. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, September 2018. http://dx.doi.org/10.7546/crabs.2018.09.11.

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Vassilev, Apostol. BowTie – A deep learning feedforward neural network for sentiment analysis. Gaithersburg, MD: National Institute of Standards and Technology, April 2019. http://dx.doi.org/10.6028/nist.cswp.04222019.

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Vassilev, Apostol. BowTie – A deep learning feedforward neural network for sentiment analysis. Gaithersburg, MD: National Institute of Standards and Technology, 2019. http://dx.doi.org/10.6028/nist.cswp.8.

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Roberts, Matthew L. A Feedforward Compensation Technique for Use in Mitigating Platform Induced Jitter. Fort Belvoir, VA: Defense Technical Information Center, May 2010. http://dx.doi.org/10.21236/ada548933.

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Pati, Y. C., and P. S. Krishnaprasad. Analysis and Synthesis of Feedforward Neural Networks Using Discrete Affine Wavelet Transformations. Fort Belvoir, VA: Defense Technical Information Center, January 1990. http://dx.doi.org/10.21236/ada444558.

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Raka, E. Some Remarks on Feedback and Feedforward Employed to Reduce Beam Induced Voltages. Office of Scientific and Technical Information (OSTI), July 1988. http://dx.doi.org/10.2172/1119133.

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