The DOI matches its Crossref record.
A New Approach to Manipulator Control: The Cerebellar Model Articulation Controller (CMAC)
Abstract
CM AC is an adaptive system by which control functions for many degrees of freedom operating simultaneously can be computed by referring to a table rather than by mathematical solution of simultaneous equations. CM AC combines input commands and feedback variables into an input vector which is used to address a memory where the appropriate output variables are stored. Each address consists of a set of physical memory locations, the arithmetic sum of whose contents is the value of the stored variable. The CM AC memory addressing algorithm takes advantage of the continuous nature of the control function in a way which promises to make it possible to store the necessary data in a physical memory of practical size.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“In 1975, Albus proposed CMAC, a new type of NN as an alternative to MLP [11].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [11]
In the citing paper
“These include the Backpropagation (BP) algorithm, the Cerebellar Model Articulation Controller (CMAC) NN model [11], the Expectation Maximization (EM) algorithm [115], the to-be-referred-to as Temporal Difference (TD) learning [478], and the Iterative Dichotomiser 3 (ID3) algorithm [373].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [11]
Citations
Cited by
Sorted by citations
Items 901–999 of 999
Fuzzy hypercubes: Linguistic learning/reasoning systems for intelligent control and identification
Kang, H., & Vachtsevanos, G. (1993) · Journal of Intelligent & Robotic Systems · doi:10.1007/bf01257820 · Cited by 7
Golkhou, V. Z., Parnianpour, M., & Lucas, C. L. (2005) · Technology and Health Care · doi:10.3233/thc-2004-12602 · Cited by 7
A System Identification Model for Adaptive Nonlinear Control
Linse, D. J., & Stengel, R. F. (1991) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.23919/acc.1991.4791685 · Cited by 7
A trivial and efficient learning method for motion and force control
Burdet, E., Rey, L., & Codourey, A. (2001) · Engineering Applications of Artificial Intelligence · doi:10.1016/s0952-1976(01)00022-7 · Cited by 7
Murakoshi, K., & Mizuno, J. (2004) · Biosystems · doi:10.1016/j.biosystems.2004.05.001 · Cited by 7
“We apply Q-learning with CMAC (cerebellar model articulation controller) (Albus, 1975; Sutton, 1996) to this problem for continuous states and actions.”
MethodAN ADAPTIVE FUZZY CONTROLLER WITH CMAC-BASED SCALING FACTORS
Inoue, A., Hirashima, Y., & Chaoying, L. (2002) · IFAC Proceedings Volumes · doi:10.3182/20020721-6-es-1901.00677 · Cited by 7
“Many researchers have performed an extensive series of the experiments that show how the process dynamic and static characteristics change when scaling factors of fuzzy controller are altered (Procyk and Mamdani, 1979; Brown and Harris, 1994; Albus 1975).”
BackgroundMethodAircraft Landing Control Based on CMAC and GA Techniques
Lin, W.-P., & Juang, J. (2008) · IFAC Proceedings Volumes · doi:10.3182/20080706-5-kr-1001.01450 · Cited by 7
An iterative system inversion technique
Carotenuto, R. (2001) · International Journal of Adaptive Control and Signal Processing · doi:10.1002/1099-1115(200102)15:1 · Cited by 7
Reinforcement learning in zero-sum Markov games for robot soccer systems
Hwnag, K.-S., Chiou, J.-Y., & Chen, T.-Y. (2004) · IEEE International Conference on Networking, Sensing and Control (ICNSC) · doi:10.1109/icnsc.2004.1297102 · Cited by 7
ESOFCMAC: Evolving Self-Organizing Fuzzy Cerebellar Model Articulation Controller
Nguyen, M. N., Guo, J., & Shi, D. (2006) · 2022 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2006.247384 · Cited by 7
“INTRODUCTION HE Cerebellar Model Articulation Controller (CMAC) [1] is a type of associative memory neural network that models how a human cerebellum would take inputs, organize its memory and compute outputs.”
BackgroundModelling of nonlinear systems from input-output data for state space realization
Foley, D. C., & Sadegh, N. (2003) · Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes · doi:10.1109/cdc.2001.980730 · Cited by 7
“The architecture has a resemblance to cerebellar model articulation controller (CMAC) networks [1, 9], where nonlinear functions are approximated by local multilinear splines.”
BackgroundART FCMAC: a memory efficient neural network for robotic pose estimation
Langley, C. S., & D’Eleuterio, G. M. T. (2004) · doi:10.1109/cira.2003.1222126 · Cited by 7
Carbon monoxide prediction using novel intelligent network
Abbaspour, M., Rahmani, A. M., & Teshnehlab, M. (2005) · International Journal of Environmental Science and Technology · doi:10.1007/bf03325840 · Cited by 7
“At first, CMAC model was used in order to control robot arms (Albus, 1975).”
MethodNonlinear system process prediction using neural networks
Carotenuto, R., Franchina, L., & Coli, M. (2002) · doi:10.1109/icnn.1996.548888 · Cited by 7
The Emergence of Multiple Movement Units in the Presence of Noise and Feedback Delay
Kositsky, M., & Barto, A. G. (2002) · The MIT Press eBooks · doi:10.7551/mitpress/1120.003.0010 · Cited by 7
CMAC Models Learn to Play Soccer
Wiering, M., Sałustowicz, R., & Schmidhuber, J. (1998) · Perspectives in neural computing · doi:10.1007/978-1-4471-1599-1_66 · Cited by 7
“CMACs [1] use lters mapping inputs to a set of activated 1”
MethodPath planning for mobile robots using an improved reinforcement learning scheme
Fujisawa, S., Kurozumi, R., Yamamoto, T., et al. (2003) · IEEE International Symposium on Intelligent Control · doi:10.1109/isic.2002.1157740 · Cited by 7
The use of neural networks to enhance sensorless position detection in switched reluctance motors
Reay, D. S., Dessouky, Y. G., & Williams, B. W. (2002) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.1998.728151 · Cited by 7
Visual guided grasping of aggregates using self-valuing learning
Rössler, B., Zhang, J., & Knoll, A. (2003) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2002.1014336 · Cited by 7
Intelligent ballistic control of a jointed leg
Doerschuk, P. I., Nguyen, V., Simon, W., et al. (2002) · IEEE International Joint Symposia on Intelligence and Systems (IJSIS) · doi:10.1109/ijsis.1996.565059 · Cited by 7
Neural fuzzy approach to F/A-18 aircraft failure isolation and reconfiguration design
Chiang, C.-Y., & Youssef, H. M. (1995) · Guidance, Navigation and Control Conference · doi:10.2514/6.1995-3179 · Cited by 7
Cornforth, D. J., Jelinek, H. F., Teich, M., et al. (2004) · Charles Sturt University Research Output (CRO) · Cited by 7
Reinforcement Learning of Dimensional Attention for Categorization
Philips, J. L., & Noelle, D. C. (2004) · eScholarship (California Digital Library) · Cited by 8
The Cerebellar Model Articulation Controller (CMAC)
Aved'yan, E. D. (1995) · Springer eBooks · doi:10.1007/978-1-4471-3089-5_9 · Cited by 7
Lin, C.-S., & Kim, H. (1991) · Elsevier eBooks · doi:10.1016/b978-0-444-89178-5.50072-5 · Cited by 7
Self-Tuning Adaptive Control using Fourier Series Neural Networks
Zhu, C., & Paul, F. W. (1993) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.23919/acc.1993.4793347 · Cited by 6
A Bio-Inspired Robotic Mechanism for Autonomous Locomotion in Unconventional Environments
Maravall, D., & de Lope, J. (2003) · Studies in fuzziness and soft computing · doi:10.1007/978-3-7908-1767-6_10 · Cited by 7
Neural Networks for Motor Program Generation
Eckmiller, R. (1989) · Springer eBooks · doi:10.1007/978-3-642-83740-1_37 · Cited by 7
Zhang, W.-R. (2002) · IEEE International Symposium on Intelligent Control · doi:10.1109/isic.1996.556256 · Cited by 7
Using neural networks for robot positioning control
Wu, C.-H., Jiang, B. C., & Wu, C. H. (1993) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/0736-5845(93)90052-l · Cited by 7
Expose hidden assumptions in network theory
Haberlandt, K. F. (1990) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00079826 · Cited by 7
“The logistic or hyperbolic tangent functions have been used in the fan-out of units in models like ART (adaptive resonance theory, Grossberg 1987), brain-state-in-box, Boltzmann machines (Ackley et al. 1985), CMAC (Albus 1975), and back-propagation (Rumelhart, Hinton & Williams 1986).”
Closed-loop method to improve image PSNR in pyramidal CMAC networks
Lu, H., & Tao, T. (2006) · International Journal of Computer Applications in Technology · doi:10.1504/ijcat.2006.008641 · Cited by 7
“In general, let v x; y be the input vector and wv j be the stored weight in the j-th layer memory cells (or so called hypercubes) which are mapped by input vector in the K-layer CMAC network (Albus, 1975b; Lin and Chiang, 1997; Tao et al., 2002).”
BackgroundSystolic implementation of higher-order CMAC and its application in colour calibration
Ker, J., Kuo, Y.-H., & Liu, B. (1997) · IEE Proceedings - Circuits Devices and Systems · doi:10.1049/ip-cds:19971005 · Cited by 7
Adaptive nonlinear compensation for CDMA communication systems
González-Serrano, F. J., & Murillo-Fuentes, J. J. (2001) · IEEE Transactions on Vehicular Technology · doi:10.1109/25.917867 · Cited by 7
Neural network control of a three-link leg
Doerschuk, P. I., Nguyen, V., & Li, A. (2002) · doi:10.1109/tai.1995.479614 · Cited by 7
Robotic sensorimotor learning in continuous domains
Salganicoff, M., & Bajcsy, R. K. (2003) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.1992.219980 · Cited by 7
Designing neural networks for adaptive control
Kaiser, M., Retey, A., & Dillmann, R. (2002) · Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes · doi:10.1109/cdc.1995.480608 · Cited by 7
Further Explorations in Visually-Guided Reaching: Making MURPHY Smarter
Mel, B. W. (1988) · neural information processing systems · Cited by 7
DRIVER-ADAPTIVE WARNING SYSTEM
Goldman, R., Miller, C. H., Harp, S. A., et al. (1995) · Cited by 7
Learning Representations in Reinforcement Learning
Heravi, J. R. (2019) · Cited by 7
Adaptive Single-Input Recurrent WCMAC-Based Supervisory Control for De-icing Robot Manipulator
Ngo, T. Q., Le, T. T. H., Lam, M. B., et al. (2023) · Journal of Robotics and Control (JRC) · doi:10.18196/jrc.v4i4.18464 · Cited by 6
Automatic construction and evaluation of macro-actions in reinforcement learning
Farahani, M. D., & Mozayani, N. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.105574 · Cited by 6
Backlash compensation by neural-network online learning
He, C., Zhang, Y., & Meng, M. (2002) · IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA) · doi:10.1109/cira.2001.1013190 · Cited by 6
Application of CMAC Neural Network to Solar Energy Heliostat Field Fault Diagnosis
Pai, N., Yau, H., Hung, T.-H., et al. (2013) · International Journal of Photoenergy · doi:10.1155/2013/938162 · Cited by 6
Fuzzy Q-learning in continuous state and action space
Ming-liang, X. U., & Wen-bo, X. (2010) · 中国邮电高校学报:英文版 · doi:10.1016/s1005-8885(09)60495-7 · Cited by 6
Golkhou, V. Z., Lucas, C., & Parnianpour, M. (2004) · Biomedical Engineering Applications Basis and Communications · doi:10.4015/s1016237204000426 · Cited by 6
Disambiguation and spelling correction for a neural network based character recognition system
Trenkle, J. M., & Vogt, R. C., III (1994) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.171120 · Cited by 6
A SELF-IMPROVING FUZZY CEREBELLAR MODEL ARTICULATION CONTROLLER WITH STOCHASTIC ACTION GENERATION
Hwang, K., & Hsu, Y.-P. (2002) · Cybernetics & Systems · doi:10.1080/019697202753435890 · Cited by 6
“The CMAC, originally proposed by James Albus [1, 2], is a linear combination of overlapped basis function.”
BackgroundFPGA Parallel Implementation of CMAC Type Neural Network with on Chip Learning
Brassai, S. T., Bakó, L., & Dan, S. (2007) · IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI) · doi:10.1109/saci.2007.375494 · Cited by 6
“These controllers are expected to cope with t a vcr complex [1], uncertain and nonlinear dynamic processes.”
BackgroundA Converged Recurrent Structure for CMAC_GBF and S_CMAC_GBF
Chiang, C., & Chiang, T. (2007) · IEEE International Symposium on Industrial Electronics (ISIE) · doi:10.1109/isie.2007.4374893 · Cited by 6
Sliding-mode-based fuzzy CMAC controller design for a class of uncertain nonlinear system
Chen, C. (2009) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2009.5345930 · Cited by 6
“The cerebellar model articulation controller (CMAC), developed by Albus in the 1970s [4,5], is a nonfully connected perceptron-like associative memory network that computes a nonlinear function by referring a look-up table over a domain of interest.”
BackgroundWen, C.-M., & Cheng, M.-Y. (2008) · doi:10.1109/rose.2008.4669183 · Cited by 6
“CMAC was first proposed by Albus in the 1970's [10,11].”
BackgroundCooperative reinforcement learning based on zero-sum games
Hwang, K., Chiou, J.-Y., & Chen, T.-Y. (2008) · SICE Annual Conference (SICE) · doi:10.1109/sice.2008.4655172 · Cited by 6
“We use linear tile-coding function approximation (also known as CMACs [11]).”
MethodDesign of a human-skill based PID controller using CMACs
Koiwai, K., Kawada, K., & Yamamoto, T. (2008) · International Conference on Control, Automation and Systems (ICCAS) · doi:10.1109/iccas.2008.4694528 · Cited by 6
Voice coil motor motion control using fuzzy cerebellar model articulation controller
Lin, C., & Li, H.-Y. (2011) · International Conference on System Science and Engineering · doi:10.1109/icsse.2011.5961911 · Cited by 6
“The cerebellar model articulation controller (CMAC) is a type of associative memory neural network inspired by the neurophysiologic theory of the cerebellum, first introduced by Albus[6].”
MethodKernel Recursive Least Squares for the CMAC Neural Network
Laufer, C., & Coghill, G. G. (2013) · International Journal of Computer Theory and Engineering · doi:10.7763/ijcte.2013.v5.729 · Cited by 6
“The Cerebellar Model Articulation Controller (CMAC) is a neural network that was invented by Albus [1] in 1975.”
BackgroundFuzzy-Neuro Systems for Local and Personalized Modelling
Kasabov, N. K., Song, Q., & Ma, T. M. (2007) · Springer eBooks · doi:10.1007/978-3-540-73185-6_8 · Cited by 6
Zhou, X., Li, L., Yuan, J., et al. (2016) · International Journal of Advanced Robotic Systems · doi:10.1177/1729881416664520 · Cited by 6
Load-aware multicast routing in multi-radio wireless mesh networks using FCA-CMAC neural network
Ramezani, M., & Jahanshahi, M. (2017) · Computing · doi:10.1007/s00607-017-0579-0 · Cited by 6
“For each state in the input vector, one weight in each layer is activated [31].”
BackgroundMethodInfluentialSelf-adaptive Decision Making for the Management of Component-Based Applications
Belhaj, N., Belaïd, D., & Mukhtar, H. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-69462-7_36 · Cited by 6
Zhang, X., Eguchi, M., & Ohmori, H. (2018) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2018.10.112 · Cited by 6
A Metric for Evaluating Neural Input Representation in Supervised Learning Networks
Carrillo, R. R., Naveros, F., Ros, E., & Luque, N. R. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00913 · Cited by 6
“This is a common approach considered in functional models (Albus, 1975; Kawato and Gomi, 1992; Tyrrell and Willshaw, 1992; Rucci et al., 1997; Raymond and Lisberger, 1998; Doya, 1999; Kardar and Zee, 2002; Friedel and van Hemmen, 2008; Garrido et al., 2013; Clopath et al., 2014).”
MethodFault Diagnosis and Tolerant Control for Three-Level T-Type Inverters
Chao, K., Chang, L.-Y., & Hung, C.-C. (2022) · Electronics · doi:10.3390/electronics11162496 · Cited by 6
Efficient Advertisement Discovery for Audio Podcast Content Using Candidate Segmentation
Nguyen, M. N., Tian, Q., & Xue, P. (2010) · EURASIP Journal on Audio Speech and Music Processing · doi:10.1155/2010/572571 · Cited by 6
“First proposed by Albus [14], the original CMAC has been widely applied in many areas such as robotic control, signal processing, and pattern recognition.”
BackgroundWen, C.-M., & Cheng, M.-Y. (2012) · Control Engineering Practice · doi:10.1016/j.conengprac.2012.06.009 · Cited by 6
Le, T.-L., Quynh, N. V., Long, N. K., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3036125 · Cited by 6
“This controller, which was provided by Albus in 1975, is a special type of neural network [5].”
BackgroundSensorimotor Learning Using Active Perception in Continuous Domains
Salganicoff, M., & Bajcsy, R. K. (1991) · ScholarlyCommons (University of Pennsylvania) · Cited by 6
Movement Control of Brachiation Robot Using CMAC Between Different Distance and Height
Saito, F., Fukuda, T., & Arai, F. (1993) · Robotics, Mechatronics and Manufacturing Systems · doi:10.1016/b978-0-444-89700-8.50063-4 · Cited by 6
Reconfigurable learning control in large space structures
Yen, G. G., & Berg, J. L. (2002) · Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes · doi:10.1109/cdc.1993.325050 · Cited by 6
Antsaklis, P. J. (1993) · North-Holland mathematical library · doi:10.1016/s0924-6509(08)70033-3 · Cited by 6
A General Learning Scheme for CMAC-based Controller
Jiang, Z., & Wang, S. (2003) · Neural Processing Letters · doi:10.1023/a:1026262131675 · Cited by 6
On-line fault detection using integrated neural networks
Lee, J., & Tsai, J. (1992) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.140021 · Cited by 6
Torioka, T., & Ikeda, N. (1988) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/21.7486 · Cited by 6
A Precise Visual Control Method for Micromanipulator
Wang, H. M., & Zhu, J. (2004) · CIRP Annals · doi:10.1016/s0007-8506(07)60635-0 · Cited by 6
Benchmarking computer systems for robot control
Machado, J. A. T., & Galhano, A. M. (1995) · IEEE Transactions on Education · doi:10.1109/13.406495 · Cited by 6
Feldman, A. G., & Levin, M. F. (1996) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00081607 · Cited by 6
Adaptive supervisory WCMAC neural network controller (SWC) for nonlinear systems
Lee, C., & Wang, B.-H. (2008) · Soft Computing · doi:10.1007/s00500-008-0287-y · Cited by 6
“The cerebellar model articulation controller (CMAC) is a perceptron-like associative memory that formulates the processing characteristics of the cerebellum (Albus 1975a,b; Glanz et al. 1991; Jagannthan et al. 1998; Lee et al. 2004; Wang and Lu 2003).”
BackgroundMethodInfluentialAdaptive tracking control of a class of nonlinear systems using CMAC network
Horng, J., Hsieh, J., & Liao, T. (1996) · Journal of the Franklin Institute · doi:10.1016/0016-0032(96)00055-5 · Cited by 6
A Driver Model Based on the Cerebellar Model Articulation Controller
Hayhoe, G. F. (1979) · Vehicle System Dynamics · doi:10.1080/00423117908968568 · Cited by 6
Robot skill transfer based on B-spline fuzzy controllers for force-control tasks
Ferch, M., Zhang, J., & Knoll, A. (2003) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.1999.772520 · Cited by 6
Variable structure control of electrohydraulic servo systems using fuzzy CMAC neural network
Jiang, Z., Wang, S., & Ting-qi, L. (2003) · Transactions of the Institute of Measurement and Control · doi:10.1191/0142331203tm082oa · Cited by 6
Hwang, K., Yang, T.-W., & Lin, C. (2006) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2006.385115 · Cited by 6
“We adopt FAST [6] to generate CMAC-like [7] coarse coding with multiple, overlapped grid tilings.”
MethodRapid Reinforcement Learning for Reactive Control Policy Design in Autonomous Robots
Fagg, A. H., Lotspeich, D., Hoff, J., & Bekey, G. A. (1996) · doi:10.4324/9781315784076-23 · Cited by 7
A novel high-order associative memory system via discrete taylor series
Xu, N.-S., Bai, Y., & Li, Z. (2003) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2003.811700 · Cited by 6
Wu, J.-Y., & Lu, C.-J. (2009) · doi:10.1109/aciids.2009.51 · Cited by 6
“Albus [1, 2] first introduced the CMAC NN based on the functions of human cerebellum, which is responsible for muscle control and motor coordination.”
A genetic algorithm as the learning procedure for neural networks
Gonzalez-Seco, J. (2003) · 2022 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.1992.287083 · Cited by 6
Modeling a photovoltaic power system by CMAC-GBF
Chiang, C., Chiang, T., & Huang, H. (2003) · 3rd World Conference onPhotovoltaic Energy Conversion, 2003. Proceedings of · Cited by 6
Experimental validation of a robust control strategy for the robot RABBIT
Sabourin, C., Bruneau, O., & Buche, G. (2006) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2005.1570471 · Cited by 6
Discrete-time CMAC neural networks for control applications
Commuri, S., Lewis, F. L., & Jagannathan, S. (2002) · Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes · doi:10.1109/cdc.1995.478453 · Cited by 6
The treatment of image boundary effects in CMAC networks
Lu, H., & Tao, T. (2005) · IEEE International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2004.1380043 · Cited by 6
Robust CMAC backstepping longitudinal control of vehicle platoons
Peng, Y.-F., Hsu, C., Lin, C., et al. (2004) · IEEE International Conference on Networking, Sensing and Control (ICNSC) · doi:10.1109/icnsc.2004.1297502 · Cited by 6
A naive method of applying fuzzy logic to CMAC in electric load simulator
Zhang, M., & Yang, B. S. (2016) · Transactions of the Institute of Measurement and Control · doi:10.1177/0142331216659335 · Cited by 6
“Albus proposed the cerebellar model articulation controller (CMAC) in 1975 (Albus, 1975), and it is widely used for its simple structure, rapid learning speed, insensitivity of data sequence and non-existence of local minimum (Pallotta and Kraft, 1999; Liu and Ma, 2011; Wong and Sideris, 1992).”
BackgroundThe one‐time learning hierarchical CMAC and the memory limited CA‐CMAC for image data compression
Tao, T., Lu, H., Hsu, C., et al. (2003) · Journal of the Chinese Institute of Engineers · doi:10.1080/02533839.2003.9670764 · Cited by 6
“The first method is the one-time learning hierarchical CMAC (cerebellar model arithmetic computer) method (Albus, 1975a; Albus, 1975b; Iiguni, 1996) and the second is the memory limited CA-CMAC (credit assigned cerebellar model arithmetic computer) method (Smalz and Conrod, 1994; Su et al ., 2002)…”
MethodA Bayesian approach to modeling the conditional density of the inverse controller
Herzallah, R., & Lowe, D. G. (2005) · The proceedings of the IEEE Conference on Control Applications/The proceedings of the ... IEEE Conference on Control Applications · doi:10.1109/cca.2004.1387310 · Cited by 6
Modular Neural Architectures for Robotics
Buessler, J. L., & Urban, J.-P. (2003) · Studies in fuzziness and soft computing · doi:10.1007/978-3-7908-1775-1_10 · Cited by 6
“We will also mention Albus' CMAC (Cerebellar Model Articulation Controller) [Albus, 1975] which gives rise to interesting robotic applications [e.g. Miller, 1994; Kun & Miller, 1999], and the RBF (Radial Basis Function) [e.g. Poggio & Girosi, 1990; Haykin, 1994].”
Optimal attitude control of satellites by artificial neural networks: a pilot study
Schram, G., Karsten, L., Kröse, B., et al. (1994) · Annual Review in Automatic Programming · doi:10.1016/0066-4138(94)90061-2 · Cited by 6
Accelerated reinforcement learning control using modified CMAC neural networks
Xu, X., Hu, D., & He, H. (2002) · International Conference on Neural Information Processing · doi:10.1109/iconip.2002.1201960 · Cited by 6
A MEMORY-BASED SELF-GENERATED BASIS FUNCTION NEURAL NETWORK
Lin, C.-S., & Li, C.-K. (1999) · International Journal of Neural Systems · doi:10.1142/s0129065799000058 · Cited by 6
Metadata
- Authors
- Albus, J. S.
- Affiliations
- National Institute of Standards and Technology (US)
- Corresponding author
- Albus, J. S.
- Venue
- Journal of Dynamic Systems, Measurement, and Control
- Publisher
- ASME International
- Published
- 1975-09-01
- Volume
- 97
- Issue
- 3
- Pages
- 220-227
- ISSN
- 0022-0434 (ISSN-L)
- 1528-9028
- OpenAlex
- W1993740947
- Cited by
- 2,177
- References
- 0
- Citations by year
- 2026: 9 · 2025: 18 · 2024: 18 · 2023: 19 · 2022: 21 · 2021: 19 · 2020: 30 · 2019: 39 · 2018: 44 · 2017: 59 · 2016: 33 · 2015: 38 · 2014: 55 · 2013: 58 · 2012: 63
- FWCI
- 1.57
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Dynamic Systems Measurement and Control (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Engineering › Control and Systems Engineering › Adaptive Control of Nonlinear Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Fuzzy Logic and Control Systems
- Keywords
- cerebellar model articulation controller, CMAC, adaptive control, robot manipulator control, multivariable control, memory addressing, look-up table, feedback control, neural network control
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review