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 601–700 of 999
On The Control of Mechanical Manipulators 1
Lee, С. S. G., Chung, M. J., Turney, J. L., et al. (1982) · IFAC Proceedings Volumes · doi:10.1016/s1474-6670(17)63233-6 · Cited by 15
NIST support to the next generation controller program:
Albus, J. S., Quintero, R., Proctor, F. M., Michaloski, J. L., Dagalakis, N. G., Tarnoff, N., & Kramer, T. (1992) · doi:10.6028/nist.ir.4888 · Cited by 15
Self-citationBehavior generation in intelligent systems
Albus, J. S., & Meystel, A. (1997) · doi:10.6028/nist.ir.6083 · Cited by 15
Self-citationHardware Implementation of CMAC Type Neural Network on FPGA for Command Surface Approximation
Brassai, S. T., & Bakó, L. (2007) · Cited by 14
Design and analysis of direct-action CMAC PID controller
Lu, H., Chang, J., & Yeh, M. (2007) · Neurocomputing · doi:10.1016/j.neucom.2006.07.018 · Cited by 14
Han, S.-I., & Lee, J. (2011) · IET Control Theory and Applications · doi:10.1049/iet-cta.2010.0389 · Cited by 14
Herzallah, R. (2011) · International Journal of Modelling Identification and Control · doi:10.1504/ijmic.2011.040076 · Cited by 14
“In direct inverse adaptive control, the controller is learning to recreate the input that created the desired output of the plant [1], [4], [26], [50].”
BackgroundMethodAdaptive Control of a Class of Nonlinear Systems Using Multiple Parameter Models
Lee, C.-Y. (2006) · International Journal of Control Automation and Systems · Cited by 14
Distribution Modeling of Nonlinear Inverse Controllers Under a Bayesian Framework
Herzallah, R., & Lowe, D. G. (2007) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2006.883721 · Cited by 14
“In conventional direct inverse control, the conditional expectation of the control signals is estimated directly by minimizing the mean square error between the actual control signals and the estimated control signals error [2], [19], [32], [35].”
BackgroundYan, H., Sun, Q., & Zhou, B. (2017) · Optimal Control Applications and Methods · doi:10.1002/oca.2384 · Cited by 14
Real time driving data collection and driver verification using CMAC-MFCC
Ahmad, M. K., Wahab, A., & Kamaruddin, N. (2008) · International Conference on Artificial Intelligence · Cited by 14
Learning Convergence of CMAC Algorithm
He, C., Xu, L., & Zhang, Y. (2001) · Neural Processing Letters · doi:10.1023/a:1011382225296 · Cited by 14
Two dimensional function learning using CMAC neural network with optimized weight smoothing
Pallotta, J., & Kraft, L. G. (1999) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.1109/acc.1999.782804 · Cited by 14
Design of a hybrid adaptive CMAC with supervisory controller for a class of nonlinear system
Lu, H., Chuang, C.-Y., & Yeh, M. (2008) · Neurocomputing · doi:10.1016/j.neucom.2008.07.004 · Cited by 14
“The cerebellar model articulation controller (CMAC) is regarded as partly connected perceptron-like associate memory network with overlapping receptive fields [1].”
BackgroundRobust adaptive deadzone and friction compensation of robot manipulator using RWCMAC network
Han, S.-I., Lee, K. S., Park, M. G., & Lee, J. M. (2011) · Journal of Mechanical Science and Technology · doi:10.1007/s12206-011-0328-9 · Cited by 14
“Several dynamic friction models have been proposed, such as Dahl’s model [6], the LuGre model [7], the Elasto-plastic model [8], the Leuven model [9], and the GMS model [10] to develop a further advanced friction model.”
MethodDynamics modeling of robotic manipulators using an artificial neural network
Eskandarian, A., Bedewi, N. E., Kramer, B. M., et al. (1994) · Journal of Robotic Systems · doi:10.1002/rob.4620110106 · Cited by 14
Enhancing MLP networks using a distributed data representation
Narayan, S., Tagliarini, G. A., & Page, E. W. (1996) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/3477.484446 · Cited by 14
The prediction of trust rating based on the quality of services using fuzzy linear regression
Mashinchi, M. H., Li, L., Orgun, M. A., et al. (2011) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzzy.2011.6007745 · Cited by 14
Holonic Approach for Control and Coordination of Distributed Sensors
Benaskeur, A. R., & Irandoust, H. (2008) · Cited by 14
More on climbing fiber signals and their consequence(s)
Simpson, J. I., WYLIE, D. R. W., & Zeeuw, C. I. D. (1996) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00081991 · Cited by 13
Neural optimal control of PEM-fuel cells with parametric CMAC networks
Almeida, P. E. M., & Simões, M. G. (2004) · Conference record · doi:10.1109/ias.2003.1257600 · Cited by 14
An approach to enlarge learning space coverage for robot learning control
Young, K., & Shiah, S.-J. (1997) · IEEE Transactions on Fuzzy Systems · doi:10.1109/91.649902 · Cited by 14
“In order to reduce the memory size of association cells for a practical mapping, Albus [1] proposed a procedure for hash-coding, which is basically a uniform random mapping from a larger memory to a smaller one.”
BackgroundMethodInfluentialRCMAC-Based Adaptive Control for Uncertain Nonlinear Systems
Peng, Y.-F., & Lin, C. (2007) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2006.888761 · Cited by 14
Intelligent Machines: Distributed vs. Hierarchical Intelligence
Saridis, G. N. (1988) · IFAC Proceedings Volumes · doi:10.1016/s1474-6670(17)54469-9 · Cited by 14
Stability and convergence of neurologic model based robotic controllers
Cılız, M. K., & Isik, C. (2003) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.1992.219979 · Cited by 14
Parallelism in manipulator dynamics
Lathrop, R. H. (2005) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.1985.1087228 · Cited by 14
Hierarchical fuzzy CMAC control for nonlinear systems
Rodríguez, F. O., Rubio, J. D. J., Gaspar, C. R. M., et al. (2013) · Neural Computing and Applications · doi:10.1007/s00521-013-1423-x · Cited by 13
“One subclass of neural networks, called the cerebellar model articulation controller (CMAC) presented by [5], is an auto-associative memory feedforward neural network, which is a simple mathematical model of the cerebellar based on the neurophysiological theory.”
BackgroundConvergence of a Q-learning Variant for Continuous States and Actions
Carden, S. W. (2014) · Journal of Artificial Intelligence Research · doi:10.1613/jair.4271 · Cited by 14
“Empirical convergence has been observed with CMACs (Sutton, 1996), neural networks (Rummery & Niranjan, 1994; ten Hagen, 1991), and regression trees (Ernst et al., 2005).”
MethodInfluentialCerebellar Model Articulation Controller (CMAC) for Suppression of Structural Vibration
Kim, D. H., Oh, J.-W., & Lee, I. (2002) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)0887-3801(2002)16:4(291 · Cited by 13
Robust intelligent backstepping tracking control for wheeled inverted pendulum
Chiu, C.-H., Peng, Y.-F., & Lin, Y.-W. (2011) · Soft Computing · doi:10.1007/s00500-011-0702-7 · Cited by 13
Minimizing the Cross Validation Error to Mix Kernel Matrices of Heterogeneous Biological Data
Tsuda, K., Uda, S., Kin, T., et al. (2004) · Neural Processing Letters · doi:10.1023/b:nepl.0000016845.36307.d7 · Cited by 13
Mai, T.-L., & Wang, Y. (2014) · Control Theory and Technology · doi:10.1007/s11768-014-3181-4 · Cited by 13
“It is an autoassociative memory feedforward NNs with overlapping receptive fields [14].”
BackgroundExtremely fast pRF mapping for real-time applications
Bhat, S., Lührs, M., Goebel, R., & Senden, M. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118671 · Cited by 13
Automatic Diagnosis of Asthma Using Neurofuzzy System
Ansari, A. Q., Gupta, N. K., & Ekata (2012) · International Conference on Computational Intelligence and Communication Networks (CICN) · doi:10.1109/cicn.2012.55 · Cited by 13
Moment Adaptive Fuzzy Control and Residue Compensation
Tao, T., & Su, S. (2013) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2013.2275168 · Cited by 13
“The cerebellar model articulation controller (CMAC) [44], [45] has been shown to have several advantages including local generalization [50], [51] and rapid learning convergence [52], [53].”
BackgroundMethodIslanding Detection Method of a Photovoltaic Power Generation System Based on a CMAC Neural Network
Chao, K., Yang, M.-S., & Hung, C.-P. (2013) · Energies · doi:10.3390/en6084152 · Cited by 13
Peng, Y.-F. (2010) · Applied Soft Computing · doi:10.1016/j.asoc.2010.12.016 · Cited by 13
Artificial neural network based modeling and controlling of distillation column system
Biyanto, T. R., Widjiantoro, B. L., Jabal, A. A., et al. (2011) · International Journal of Engineering Science and Technology · doi:10.4314/ijest.v2i6.63709 · Cited by 13
“Some of the most common neural networks are functional mapping nets like multilayer perceptron (MLP) and cerebellar model articulation computer (CMAC) (Albus,1975), adaptive resonance nets (ART) that form input categories from input data, input feature categorization nets of Kohonen (Kohonen,1984), bilinear associativ…”
BackgroundNeural control based on RBF network implemented on FPGA
Brassai, S. T., Bakó, L., Pana, G., et al. (2008) · International Conference on Optimization of Electrical and Electronic Equipment · doi:10.1109/optim.2008.4602496 · Cited by 13
“Using CMAC (Cerebellar Model Articulation Controller) [7] and RBF (Radial Basis Function Network) networks with only a single trainable layer, the learning speed can be significantly increased.”
MethodVijayan, A., Nutakki, C. S., Kumar, D. P., et al. (2017) · International Journal of Online and Biomedical Engineering (iJOE) · doi:10.3991/ijoe.v13i01.6288 · Cited by 13
“The whole network was build based on a lookup table which adapts the iJOE ‒ Vol. 13, No. 1, 2017 65 Cerebellar Motor Articulation Control (CMAC) [15].”
InfluentialIntelligent failure-tolerant control
Stengel, R. F. (2002) · IEEE International Symposium on Intelligent Control · doi:10.1109/isic.1990.128511 · Cited by 13
On the Dynamics and Control of Robotic Manipulators with an Automatic Balancing Mechanism
Chung, W. K., & Cho, H. S. (1987) · Proceedings of the Institution of Mechanical Engineers Part B Management and engineering manufacture · doi:10.1243/pime_proc_1987_201_041_02 · Cited by 13
Automatic Vision-Based Optical Fiber Alignment Using Multirate Technique
Chuang, H.-S., Wen, C.-M., Cheng, M.-Y., et al. (2009) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2009.2014750 · Cited by 13
“In this paper, a cerebellar model articulation controller (CMAC) [12], [13] is exploited to replace the B-spline inverse hysteresis model and acts as a feedforward compensator.”
MethodStable neural network control for manipulators
Jin, Y., Pipe, T., & Winfield, A. F. T. (2005) · doi:10.1109/ijcnn.1993.714299 · Cited by 13
FCMAC-AARS: A Novel FNN Architecture for Stock Market Prediction and Trading
Guo, Z., Quek, C. H., & Maskell, D. L. (2006) · doi:10.1109/cec.2006.1688602 · Cited by 13
“A particular class of neural network called the Cerebellar Model Articulation Controller (CMAC) [16] was developed to provide an efficient computational algorithm for use in manipulator control [17].”
BackgroundAdaptive control of robot manipulators using multiple neural networks
Lee, C.-Y., & Lee, J.-J. (2004) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2003.1241735 · Cited by 13
Stock Trading using PSEC and RSPOP: A novel evolving rough set-based neuro-fuzzy approach
Ang, K. K., & Quek, C. H. (2005) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2005.1554804 · Cited by 13
“Two exisiting algorithms that offered additional inspiration are: Cerebellar Model Articulation Controller (CMAC) in [30,31] and density-based clustering algorithm DBSCAN in [32].”
MethodExplicit Manifold Representations for Value-Function Approximation in Reinforcement Learning.
Smart, W. D. (2004) · Cited by 13
Albus, J. S. (2010) · International Journal of Machine Consciousness · doi:10.1142/s1793843010000448 · Cited by 13
“(Marr 1969; Albus 1971, 1975a, 1975b, 1981).”
Self-citationKatić, D., & VUKOBRATOVIC, M. K. (1994) · Journal of Intelligent & Robotic Systems · doi:10.1007/bf01276703 · Cited by 13
Product Demand Forecasting with a Novel Fuzzy CMAC
Shi, D., Quek, C. H., Tilani, R., et al. (2006) · Neural Processing Letters · doi:10.1007/s11063-006-9031-8 · Cited by 13
“The Cerebellar Model Articulation Controller (CMAC) first proposed by Wen and Yao [15] and Albus [16], is a type of associative memory neural network that models how a human cerebellum takes inputs, organize its memory and compute outputs.”
BackgroundCoFD: An Algorithm for Non-distance Based Clustering in High Dimensional Spaces*
Shenghuo Zhu, B., Li, T., & Ogihara, M. (2002) · Lecture notes in computer science · doi:10.1007/3-540-46145-0_6 · Cited by 13
A self-organized CMAC controller
Chow, M., & Menozzi, A. (2002) · IEEE International Conference on Industrial Technology (ICIT) · doi:10.1109/icit.1994.467181 · Cited by 13
Hardware realization of higher-order CMAC model for color calibration
Ker, J., Kuo, Y.-H., & Liu, B.-D. (2002) · doi:10.1109/icnn.1995.488867 · Cited by 13
APPLICATION OF NEURAL NETWORKS TO ROBOTICS
Harston, C. T. (1990) · Elsevier eBooks · doi:10.1016/b978-0-12-546090-3.50027-9 · Cited by 13
Integration of knowledge-based system and neural network techniques for autonomous learning machines
Handelman, Lane, & Gelfand (1989) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.1989.118652 · Cited by 12
Comparative Aspects of Neural Network Algorithms for On-Line Modelling of Dynamic Processes
An, P. E., Brown, M., Harris, C. J., et al. (1993) · Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering · doi:10.1243/pime_proc_1993_207_345_02 · Cited by 13
“The misadjustment can often be reduced using a stochastic approximation technique in which the learning rate for wi is made to vary as in equation (15) based on the frequency of its update.”
BackgroundSimulation study of CMAC control for the robot joint actuated by McKibben muscles
Zhao, H., & Sugisaka, M. (2008) · Applied Mathematics and Computation · doi:10.1016/j.amc.2008.05.021 · Cited by 12
Control of Antagonistic McKibben Muscles via a Bio-inspired Approach
Chen, X., Zhu, W., Liang, W., et al. (2022) · Journal of Bionic Engineering · doi:10.1007/s42235-022-00225-w · Cited by 12
“In the proposed control scheme, different from the traditional Cerebellar Model Articulation Controller (CMAC) first introduced in [27] in 1975, a cerebellar model based on spiking dynamics that is similar to the neurobiological properties of the biological neurons is established.”
MethodSeppi, K. D., & Carroll, J. L. (2010) · Cited by 12
Machine degradation analysis using fuzzy CMAC neural network approach
Xu, R.-Z., Xie, L., Zhang, M.-C., et al. (2007) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-006-0887-6 · Cited by 12
Neural-adaptive control using alternate weights
Macnab, C. J. B. (2010) · Neural Computing and Applications · doi:10.1007/s00521-010-0366-8 · Cited by 12
“The CMAC (originally cerebellar model articulation controller in [2]) is a more computationally efficient Table 1 Examples of robust adaptive-parameter (weight) update methods used in approximate (neural) adaptive control literature”
MethodCMAC-based previous step supervisory control schemes for relaxing bound in adaptive fuzzy control
Tao, T., & Su, S. (2011) · Applied Soft Computing · doi:10.1016/j.asoc.2011.03.019 · Cited by 12
Anti-swing control with hierarchical fuzzy CMAC compensation for an overhead crane
Rodríguez, F. O., Yu, W., & Moreno-Armendáriz, M. A. (2007) · IEEE International Symposium on Intelligent Control · doi:10.1109/isic.2007.4450907 · Cited by 12
“The output of the CMAC can be expressed in a vector notation as y = lX k=1 wkϕk, y (x) =W Tϕ (x) (7) where wk plays the role of connective weight, W is adjustable weight values, ϕ (x) is base function defined as ϕk = Yn i=1 λk ³ µAij , ´ / lX k=1 Yn i=1 λk ³ µAij , ´ .”
Medical Sample Classifier Design Using Fuzzy Cerebellar Model Neural Networks
(2016) · Acta Polytechnica Hungarica · doi:10.12700/aph.13.6.2016.6.1 · Cited by 12
“The Cerebellar Model Articulation Controller (CMAC), was first proposed by Marr [5] in 1969.”
MethodJiang, Y., Yang, C., Wang, M., et al. (2018) · Advances in Mechanical Engineering · doi:10.1177/1687814018794349 · Cited by 12
CBMC: A Biomimetic Approach for Control of a 7-Degree of Freedom Robotic Arm
Li, Q., Pang, Y., Wang, Y., et al. (2023) · Biomimetics · doi:10.3390/biomimetics8050389 · Cited by 12
“Network Structure Many computational models of the cerebellum have already been proposed, such as CMAC [25] and the Schweighofer–Arbib model [26].”
MethodLin, C. K., & Chen, F. C. (2003) · Journal of Guidance Control and Dynamics · doi:10.2514/2.5125 · Cited by 12
Hazardous Odor Recognition by CMAC Based Neural Networks
Bucak, İ. Ö., & Karlık, B. (2009) · Sensors · doi:10.3390/s90907308 · Cited by 12
“The CMAC was firstly proposed during the 1970s by James Albus, whose idea was based on a model of the cerebellum which is a part of the brain responsible for learning process [14].”
MethodAdaptive fuzzy CMAC-based nonlinear control with dynamic memory architecture
Cheng, K.-H. (2011) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2011.07.011 · Cited by 12
Speed generalization capabilities of a cerebellar model on a rapid navigation task
Herreros, I., Maffei, G., Brandi, S., Sánchez-Fibla, M., & Verschure, P. F. M. J. (2013) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2013.6696377 · Cited by 12
“Since the first theories of cerebellar learning were proposed [1], [2], computational models of the cerebellum have been applied to robotic control tasks [3].”
BackgroundVieira, D. C. L., Adeodato, P. J. L., & Goncalves, P. M. (2010) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2010.5642280 · Cited by 12
Parks, P. C. (1993) · International Journal of Control · doi:10.1080/00207179308934445 · Cited by 12
Yang, B. S., Liu, F., & Zhang, M. (2019) · Measurement and Control · doi:10.1177/0020294018823028 · Cited by 12
Adaptive neurofuzzy system for tuberculosis
Ansari, A. Q., Gupta, N. K., & Ekata (2012) · doi:10.1109/pdgc.2012.6449883 · Cited by 12
A cerebellar associative memory approach to option pricing and arbitrage trading
Teddy, S. D., Lai, E. M.-K., & Quek, C. H. (2008) · Neurocomputing · doi:10.1016/j.neucom.2008.04.039 · Cited by 12
“The benchmarking models studied in this paper are: the basic CMAC network [3]; three neuro-fuzzy systems, namely: (1) the Fuzzy CMAC with Yager Inference Scheme (FCMAC-Yager) [47]; (2) the Generic Self-Organizing Fuzzy Neural Network (GensoFNN) [50]; and (3) the Rough Set-Based Pseudo-OuterProduct Fuzzy Neural Network…”
MethodTrajectory tracking control of robot manipulators using a neural-network-based torque compensator
Li, Q., Tso, S. K., & Zhang, W. (1998) · Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering · doi:10.1243/0959651981539532 · Cited by 12
Biped Locomotion: Stability, Analysis and Control
Vadakkepat, P., & Goswami, D. (2008) · International Journal on Smart Sensing and Intelligent Systems · doi:10.21307/ijssis-2017-286 · Cited by 12
Bai, Y., & Zhuang, H. (2005) · IEEE Conference on Robotics, Automation and Mechatronics (RAM) · doi:10.1109/ramech.2004.1438036 · Cited by 12
Procedure neural networks with supervised learning
Jiuzhen, L., & Jiaqing, Z. (2004) · International Conference on Neural Information Processing · doi:10.1109/iconip.2002.1202225 · Cited by 12
Torioka, T. (1978) · Biological Cybernetics · doi:10.1007/bf00337368 · Cited by 12
Using localizing learning to improve supervised learning algorithms
Weaver, S., Baird, L. C., & Polycarpou, M. M. (2001) · IEEE Transactions on Neural Networks · doi:10.1109/72.950133 · Cited by 12
GCMAC-based predistortion for digital modulations
González-Serrano, F. J., Murillo-Fuentes, J. J., & Artés-Rodrı́guez, A. (2001) · IEEE Transactions on Communications · doi:10.1109/26.950354 · Cited by 12
General learning algorithm for robot vision
Nayar, S. K., Murase, H., & Nene, S. A. (1994) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.179225 · Cited by 12
Stronach, A. F., & Vas, P. (1998) · IEE Proceedings - Control Theory and Applications · doi:10.1049/ip-cta:19981894 · Cited by 12
BIOLOGICAL LEARNING AND ARTIFICIAL INTELLIGENCEPreprint
Balkenius, C. (1994) · CogPrints (University of Southampton) · Cited by 12
NEURALLY INSPIRED ASSOCIATIVE MEMORIES FOR LEARNING CONTROL. A COMPARISON
Mischo, W. S., Hormel, M., & Tolle, H. (1991) · Elsevier eBooks · doi:10.1016/b978-0-444-89178-5.50061-0 · Cited by 12
Appendix G: Thirty Years of Adaptive Neural Networks: Perceptron, Madaline, and Backpropagation
Widrow, B. C., & Walach, E. (2007) · doi:10.1002/9780470231616.app7 · Cited by 12
An Interval-Valued Fuzzy Cerebellar Model Neural Network Based on Intuitionistic Fuzzy Sets
Zhao, J., & Lin, C. (2017) · International Journal of Fuzzy Systems · doi:10.1007/s40815-017-0321-2 · Cited by 11
“Since a traditional CMNN uses local constant binary receptive-field basis functions, its output is constant within each quantized state and the derivative information is not preserved [2].”
BackgroundDinh, T. X., & Ahn, K. K. (2018) · Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering · doi:10.1177/0959651818771810 · Cited by 11
“0, then K(t) is the solution of equation (43) If jsj ł es, then K(t) is calculated as follows”
BackgroundInfluential(2015) · Acta Polytechnica Hungarica · doi:10.12700/aph.12.4.2015.4.3 · Cited by 11
“– 40 – A cerebellar model articulation controller (CMAC) is a network model that uses an associated memory network [8].”
BackgroundVijayan, A., & Diwakar, S. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.909146 · Cited by 14
“Other hypotheses include the cerebellum as an adaptive filter (Fujita, 1982), a motor pattern classifier (Albus, 1975), and a neuronal timing machine that adapts to motor tasks (D’Angelo and Zeeuw, 2008).”
BackgroundMohajeri, K., Zakizadeh, M., Moaveni, B., et al. (2009) · Proceedings of ... IEEE International Conference on Fuzzy Systems · doi:10.1109/fuzzy.2009.5277185 · Cited by 11
Design of dynamic positioning systems using hybrid CMAC-based PID controller for a ship
Xia, G., Shi, X., Fu, M., et al. (2006) · doi:10.1109/icma.2005.1626657 · Cited by 11
“Albus in 1975, which is the neural network that models the structure and function of the part of human brain [12].”
BackgroundIntelligent brain emotional learning control system design for nonlinear systems
Lin, C., & Muthusamy, P. K. (2017) · Asian Control Conference (ASCC) · doi:10.1109/ascc.2017.8287300 · Cited by 11
“[7] J. C. Jan and S. L. Hung, "High-Order MS_CMAC Neural Network,” IEEE Trans. on Neural Network, vol. 12, no. 3, pp. 598-603, 2001.”
BackgroundInfluentialZhou, X., Li, Y., Yue, H., et al. (2018) · Mechatronics · doi:10.1016/j.mechatronics.2018.06.001 · Cited by 11
Framework for Building Self-Adaptive Component Applications Based on Reinforcement Learning
Belhaj, N., Belaïd, D., & Mukhtar, H. (2018) · IEEE International Conference on Services Computing · doi:10.1109/scc.2018.00010 · Cited by 11
“Authors used the standard one-step learning combined with the Cerebellar Model Articulation Controller (CMAC) [21] to enhance their learning speed.”
MethodIntegrated Computer Vision and Type-2 Fuzzy CMAC Model for Classifying Pilling of Knitted Fabric
Lee, C., & Lin, C. (2018) · Electronics · doi:10.3390/electronics7120367 · Cited by 11
“Recently, a cerebellar model articulation controller (CMAC) [13] has become a prevalent research topic.”
BackgroundHeart Sounds Classification with a Fuzzy Neural Network Method with Structure Learning
Jia, L., Song, D., Tao, L., et al. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-31362-2_15 · Cited by 11
Modeling and intelligent chatter control strategies for a lathe machine
Pan, G., Xu, H., Kwan, C., et al. (2002) · doi:10.1109/cca.1996.558636 · Cited by 11
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
