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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.
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“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]
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Items 201–300 of 999
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“To illustrate the di erences between some proposed neural network representations and a memory-based representation, two neural network methods, CMAC [2, 1] and sigmoidal feedforward neural networks, were compared to the approach explored in this paper.”
MethodInfluentialNeighborhood sequential and random training techniques for CMAC
Thompson, D. E., & Kwon, S. (1995) · IEEE Transactions on Neural Networks · doi:10.1109/72.363437 · Cited by 62
An overview of the CMAC neural network
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“The model of CMAC was first proposed in [1], [2] and employed in various applications [3]–[11].”
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“The fuzzy semantic association created by the local training is more akin to the neocortex semantic association instead of the pure numeric lookup table association (Albus, 1975) modeled using the cerebellum.”
BackgroundMethodInfluentialParametric CMAC networks: Fundamentals and applications of a fast convergence neural structure
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“Another control computation architecture, called cerebellar model articulation controller (CMAC), was also proposed by Albus [3, 2] to model control computations in”
MethodLin, C., & Li, H.-Y. (2014) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2014.2325897 · Cited by 58
“A cerebellar model articulation controller (CMAC) is a learning mechanism that imitates the human brain [6]–[8].”
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“An FCMNN can be thought of as a learning mechanism imitating the cerebellum of a human being [21].”
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“Yet another promising remedy may be to use local (but more time-consuming)function approximators such as CMACS (Albus, 1975; Sutton, 1996).”
BackgroundInfluentialFeature Selection Using a Piecewise Linear Network
Jiang, L., MANRY, M. T., Narasimha, P. L., et al. (2006) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2006.877531 · Cited by 55
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FCMAC-BYY: Fuzzy CMAC Using Bayesian Ying–Yang Learning
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“HE Cerebellar Model Articulation Controller (CMAC) [1, 2], is a type of associative memory neural network that models how a human cerebellum would take inputs, organize its memory and compute outputs.”
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“In this space, each block performs a receptive-field basis function, which can be defined as rectangular [17], [18] or triangular or any continuously bounded function (e.”
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Missile Control Using Fuzzy Cerebellar Model Arithmetic Computer Neural Networks
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“The cerebellar model articulation controller (CMAC) has the advantages of fast learning, good generalization, and simple calculation [10] in the approximation of unknown nonlinear functions.”
BackgroundA tutorial survey of reinforcement learning
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“In this respect, CMAC and methods based on clustering, such as RBF, statistical clustering, etc., are more suitable than multi{layer perceptrons.”
MethodNeural networks in robotics: A survey
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“More sophisticated representations such as kd-trees (see Omohundro (1987) for a review) or CMACs (Albus, 1975) may lead to faster learning and better generalization, but each requires a separate convergence proof. Dayan (1991) compares the qualities of certain different representations for Barto, Sutton and Watkins' g…”
BackgroundInfluentialEfficient reinforcement learning: model-based Acrobot control
Boone, G. (2002) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.1997.620043 · Cited by 51
Lu, M., Wei, X., Che, Y., Wang, J., & Loparo, K. A. (2019) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2019.2952637 · Cited by 51
“CMAC is a kind of neural network based on the neuro-physiological theory about human cerebellar, it was first proposed by Albus [34] and has been widely used in automatic control and function approximation [35].”
A Sigma-Pi-Sigma Neural Network (SPSNN)
Li, C.-K. (2003) · Neural Processing Letters · doi:10.1023/a:1022967523886 · Cited by 51
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A Multi-Fault Diagnosis Method for Sensor Systems Based on Principle Component Analysis
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“Conventional CMAC Algorithm The CMAC neural network algorithm [12,13], which is built by imitating the behavior by which cerebella control human limbs, was proposed by Albus.”
MethodExploring discrepancies in findings obtained with the KDD Cup '99 data set
Engen, V., Vincent, J., & Phalp, K. T. (2011) · Intelligent Data Analysis · doi:10.3233/ida-2010-0466 · Cited by 50
“Several researchers propose methods that allow incremental learning, for example, Cannady [12] proposes an IDS with an adaptive ANN, a Cerebellar Model Articulation Controller (CMAC) [2], which is capable of learning new intrusive behaviour during run-time.”
Mai, T., & Wang, Y. (2014) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2013.2297405 · Cited by 50
“Although the controllerbased CMAC techniques have many advantages in the control applications [18]–[23], they still represent a conventional drawback that is theirs application domain is limited to the static problem.”
BackgroundNeural network based threat assessment for automated visual surveillance
Jan, T. (2005) · IEEE International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2004.1380133 · Cited by 50
PSO-Self-Organizing Interval Type-2 Fuzzy Neural Network for Antilock Braking Systems
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“Following that, in 2013, Lin and Li introduced a self-organizing function-link fuzzy CMAC for ABS [8], where CMAC is a type of neural network based on a model of the mammalian cerebellum [12].”
MethodInfluentialEmergent behaviors and scalability for multi-agent reinforcement learning-based pedestrian models
Martínez-Gil, F., Lozano, M., & Fernández, F. (2017) · Simulation Modelling Practice and Theory · doi:10.1016/j.simpat.2017.03.003 · Cited by 50
Adaptive design of a fuzzy cerebellar model arithmetic controller neural network
Chen, J.-H., Tsai, P.-S., & Wong, C. (2005) · IEE Proceedings - Control Theory and Applications · doi:10.1049/ip-cta:20041117 · Cited by 50
Adaptive fuzzy CMAC control for a class of nonlinear systems with smooth compensation
Wu, T.-F., Tsai, P.-S., Chang, F.-R., et al. (2006) · IEE Proceedings - Control Theory and Applications · doi:10.1049/ip-cta:20050362 · Cited by 50
“One subclass of NNs, introduced by Albus [10], called the cerebellar model articulation controller (CMAC), has attracted much attention because of faster learning, better generalisation and simpler computation.”
BackgroundControl Strategies for Unmanned Underwater Vehicles
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“If the fuzzy rules and wavelet mother function are omitted , hen it is reduced to a conventional CMAC [15-18].”
BackgroundThe interpolation capabilities of the binary CMAC
Brown, M., Harris, C., & Parks, P. C. (1993) · Neural Networks · doi:10.1016/0893-6080(93)90010-t · Cited by 49
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BackgroundMethodInfluentialA comparison of five algorithms for the training of CMAC memories for learning control systems
Parks, P. C., & Militzer, J. (1992) · Automatica · doi:10.1016/0005-1098(92)90158-c · Cited by 49
An analytical comparison of a neural network and a model-based adaptive controller
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Intelligent motion control of linear ultrasonic motor with H ∞ tracking performance
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Indirect hierarchical FCMAC control for the ball and plate system
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Lin, C., & Li, H.-Y. (2013) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2013.2241769 · Cited by 48
“A CMAC is classified as a nonfully connected perceptron-like associative memory network with overlapping receptive fields [8].”
BackgroundControl of a muscle-like soft actuator via a bioinspired approach
Cao, J., Liang, W., Zhu, J., & Ren, Q. (2018) · Bioinspiration & Biomimetics · doi:10.1088/1748-3190/aae1be · Cited by 48
Chaotic time series. Part II. System Identification and Prediction
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“This hash table scheme was originally invented by Albus [4] and [3] for real time motion control in robotics.”
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Continuous valued Q-learning for vision-guided behavior acquisition
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Multisensory robots and sensor-based path generation
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Reconfigurable learning control in large space structures
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CMAC-based adaptive backstepping synchronization of uncertain chaotic systems
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A literature review of sensor heads for humanoid robots
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“For this approach CMAC [1] and Kohonen networks [9] are used.”
MethodStable adaptive control of a bipedal walking; robot with CMAC neural networks
Hu, J., Pratt, J. E., & Pratt, G. A. (2003) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.1999.772456 · Cited by 44
The CMAC and a theorem of Kolmogorov
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Projective simulation with generalization
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Gaussian networks for fuel injection control
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Distributed Computer Architecture and Fast Parallel Algorithms in Real-Time Robot Control
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“Albus first introduced this approach in what he called cerebellar model articulation controller (CMAC), [1].”
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“In this space, each block performs a receptive-field basis function, which can be defined as rectangular (Albus, 1975; Albus, 1975) or triangular or any continuously bounded function (e.”
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“The cerebellar model articulation controller (CMAC) as a kind of neural network is developed by [18].”
BackgroundAn efficient CMAC neural network for stock index forecasting
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Valve fault detection and diagnosis based on CMAC neural networks
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“CMAC, proposed originally by Albus [9,10], is a neural paradigm.”
BackgroundMethodApplying CMAC-based online learning to intrusion detection
Cannady, J. (2000) · doi:10.1109/ijcnn.2000.861503 · Cited by 42
“This paper presents the results of a research effort that investigated the application of a cerebellar model articulation controller (CMAC) neural network (Albus, 1975) in the detection of denial-of-service (DoS) attacks.”
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Forecasting ATM cash demands using a local learning model of cerebellar associative memory network
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Building a world model with structure-sensitive sparse binary distributed representations
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“Our implementations of this technique are similar to CMAC of Albus (1975). We used hyper-rectangular receptive fields, however with random size, position, and probed subspace (Kussul, Baidyk, Lukovich, & Rachkovskij, 1994; Rachkovskij, Slipchenko, Kussul, & Baidyk, 2005a).”
MethodHierarchical Fuzzy CMAC for Nonlinear Systems Modeling
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“In the testing phase, the initial condition is set as x0 = [1, 2, 1] .”
MethodOn Robot Compliance: A Cerebellar Control Approach
Abadía, I., Naveros, F., Garrido, J. A., Ros, E., & Luque, N. R. (2019) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2019.2945498 · Cited by 41
“Diverse computational models of the cerebellum have been proposed in robotic control (e.g., CMAC [18], APG [19], MPFIM [20], Schweighofer–Arbib model [21], etc.).”
MethodAn Adaptive Model Following Control of Industrial Manipulators
Kim, B. G., & Shin, K. G. (1983) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.1983.309392 · Cited by 41
Rawat, M., & Ghannouchi, F. M. (2011) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2011.2170915 · Cited by 40
“[21] J. S. Albus, “A new approach to manipulator control: The cerebellar model articulation controller (CMAC),” J. Dyn.”
BackgroundInfluentialAdaptive coupling of inferior olive neurons in cerebellar learning
Tokuda, I. T., Hoang, H., Schweighofer, N., & Kawato, M. (2012) · Neural Networks · doi:10.1016/j.neunet.2012.12.006 · Cited by 42
“Structures and functions of the cerebellum have been modeled in a variety of artificial learning machines such as a cerebellar model for articulation controller (Albus, 1975), which learns inverse or forward models from errors.”
BackgroundMultisensor process performance assessment through use of autoregressive modeling and feature maps
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Real-time monitoring of water quality using temporal trajectory of live fish
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Ovarian cancer diagnosis by hippocampus and neocortex-inspired learning memory structures
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Adaptive fuzzy cerebellar model articulation control for switched reluctance motor drive
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Neural network based process optimization and control
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A review on gait generation of the biped robot on various terrains
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“The cerebellar nuclei are integrating the output of the cerebellar cortex with information from other brain areas to which they are also projecting (Eccles et al., 1967; Marr, 1969; Albus, 1975).”
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“Because of the huge state space, a CMAC based function approximation with hash coding is used to store the Q-values.”
BackgroundAdaptive Filter Design Using Recurrent Cerebellar Model Articulation Controller
Lin, C., Chen, L., & Yeung, D. (2010) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2010.2050700 · Cited by 38
Control of a flexible-joint robot using neural networks
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“In [17] and [18], a cerebellar model articulation controller (CMAC) which is a network that imitates cerebellums of human is used to control uncertain nonlinear system.”
BackgroundMethodNeural Network Based Vibration Control of Seismically Excited Civil Structures
Błachowski, B., & Pnevmatikos, N. G. (2018) · Periodica Polytechnica Civil Engineering · doi:10.3311/ppci.11601 · Cited by 38
“Cerebellar Model Articulation Controller (CMAC) used originally by Albus (1975) [24] in robotics has been applied to reduce vibration of seismically excited structures by Kim et al (2002) [25].”
MethodApplication of a multi-DOF ultrasonic servomotor in an auditory tele-existence robot
Kawano, H., Ando, H., Hirahara, T., et al. (2005) · IEEE Transactions on Robotics · doi:10.1109/tro.2005.851372 · Cited by 38
ROTEX - The First Robot in Space
Hirzinger, G., Landzettel, K., Heindl, J., et al. (1994) · SAE technical papers on CD-ROM/SAE technical paper series · doi:10.4271/941444 · Cited by 38
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“The paradigm of machine degradation assessment was first introduced by Lee [2], [3], who used a Cerebellar Model Articulation Controller (CMAC) neural network [4], [5], to produce a quantitative confidence”
MethodCMAC Neural Network Control for High Precision Motion Control in the Presence of Large Friction
Larsen, G. A., Cetinkunt, S., & Donmez, A. (1995) · Journal of Dynamic Systems Measurement and Control · doi:10.1115/1.2799133 · Cited by 38
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.
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