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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 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]
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“The Cerebellar Model Arithmetic Computer (CMAC) developed by Albus 8,9 is a type of neural network particularly suited to robotic control.”
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“Cerebellar models were £rst applied for robot dynamics control after Albus' CMAC model was published in 1975 [3].”
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“In these approaches, the boxes system and a CMAC require proper partition of the input space, and a fuzzy-neural network structure requires setting up of the fuzzy rules and membership functions [14].”
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“Therefore, input command patterns of the operator should be predicted in real-time CMAC (Cerebellar Model Articulation Controller) [10] is therefore applied as the real-time learning algorithms.”
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“Since Albus[3] proposed CMAC in 1975, CMAC has earned widespread interest because of its rapid learning convergence[4][5].”
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“Being a still-popular approach in ANN research [3], [4], the Cerebellar Model Articulation Controller (CMAC) network applies the structure and function of the cerebellum of a human brain, in the sense that it is a local network, i.”
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“The CMAC-PID controller is based on the highly reliable PID controller[5], and the controller parameters are adjusted by the cerebellar model articulation controller (CMAC)[6], a kind of artificial neural network[7].”
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“On a typical Albus CMAC neurons are called receptive fields and are organized as follows.”
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“Drawing on Marr’s model, Albus established a cerebellar model known as the “cerebella model articulation controller” (CMAC) [5].”
MethodToward a Computational Theory of Mind
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“…associated with some real-world problems, such as chaotic time series prediction [2, 3] and adaptive decision-making modelling and control systems [4] require computational frameworks that are able to adapt incrementally in an online manner [5], learn and generalise from data automatically, and…”
BackgroundRobust Adaptive Recurrent Cerebellar Model Neural Network for Non-linear System Based on GPSO
Guan, J., Hong, S.-J., Kang, S.-B., Zeng, Y., Sun, Y., & Lin, C. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00390 · Cited by 8
“In 1975, Albus proposed the concept of the cerebellar model articulation controller (CMAC) for the first time (Albus, 1975), which was an imitation of the cerebellum learning structure, also one of the local approximations in the neural network system.”
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“Our B-spline model provides an ideal implementation of ‘‘Cerebellar Model Articulation Controller’’ (CMAC) proposed by Albus [1].”
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“The affinity rate between a PSi and a PSj can be defined by ai =ag , a CZ[0] [1] (3) with { ° if goal X { goalseti n goalset1 } i LI if goal E {goalset, n goalset1} and II if pi better than pj Ii= 1-k 0 k max(pi,pj) I max(Pi, pj) (4) (5) The operator "better than" is for example max if p is the accuracy of the executi…”
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CMAC spectral subtraction for speech enhancement
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CMAC neural network with improved generalization property for system modeling
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“Although the CMAC architecture was proposed in the middle of the seventies [9] and it has been mentioned as a real alternative of MLP [10], quite a lot open questions have been left even for today.”
BackgroundA comparison of neural network models for pattern recognition
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“In particular, he proposed an artificial neural network architecture known as the Cerebellar Model Arithmetic Computer (CMAC) based on the idea that the cerebellum is a pattern classifier (Albus 1975).”
Wang, S.-Y., Tseng, C.-L., Chang, C.-Y., et al. (2013) · Journal of the Chinese Institute of Engineers · doi:10.1080/02533839.2013.799930 · Cited by 7
“Because of its fast learning capability, the cerebellar model articulation controller (CMAC) neural network is suitable for tuning parameters for proportional integral controllers (Albus 1975a, 1975b; Lin and Peng 2004; Tsai and Yeh 2009).”
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“We take inspiration from the Cerebellar Model Articulation Controller (CMAC) from autonomous robotics [37].”
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“INTRODUCTION The cerebellar model articulation controller (CMAC) was firstly proposed by Albus in 1975 [1].”
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“For example, CMAC function approximators [1] adapt the weight of each region in each of multiple partitions of the input space.”
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“When used in an adaptive control scheme, the artificial neural network deemed cerebellar model articulation controller (CMAC) [3] adapts more quickly than a multilayer perceptron (MLP) and can handle more inputs than a radial-basis-function (RBF) network.”
Evaluation of physical damage associated with action selection strategies in reinforcement learning
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“The value function is represented by a linear function approximator using binary features defined by tile coding (Albus, 1975).”
MethodDynamic TOPSIS fuzzy cerebellar model articulation controller for magnetic levitation system
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“Acerebellarmodelarticulationcontroller(CMAC) is classified as a non-fully connected perceptron-like associative memory network with overlapping receptive fields [12].”
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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 ›
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