CMAC with General Basis Functions

Chiang, Ching-Tsan; Lin, Chun-Shin

doi:10.1016/0893-6080(96)00132-3

Cited by 183 publications

(18 citation statements)

References 7 publications

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“…However, it has the drawback that its derived information is not retained. To obtain the derivative information of the input and output variables, the CMAC network used a differentiable Gaussian acceptance field basis function and analyzed its convergence [12]. The advantages of CMAC networks over NNs have been well recorded in many applications [13,14].…”

Section: Of 21mentioning

confidence: 99%

New Intelligent Control Strategy Hybrid Grey–RCMAC Algorithm for Ocean Wave Power Generation Systems

Hong

Han

et al. 2020

Energies

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In this article, the characteristics of the wave energy converter are considered and a novel dynamic controller (NDC) for a permanent magnet synchronous generator (PMSG) is proposed for Wells turbine applications. The proposed NDC includes a recursive cerebellum model articulation controller (RCMAC) with a grey predictor and innovative particle swarm optimization (IPSO). IPSO is developed to adjust the learning speed and improve learning capability. Based on the supervised learning method, online adjustment law of RCMAC parameters is derived to ensure the system’s stability. The NDC scheme is designed to maintain a supply–demand balance between intermittent power generation and grid power supply. The proposed NDC exhibits an improved power regulation and dynamic performance of the wave energy system under various operation conditions. Furthermore, better results are obtained when the RCMAC is used with the grey predictive model method.

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Section: Of 21mentioning

confidence: 99%

New Intelligent Control Strategy Hybrid Grey–RCMAC Algorithm for Ocean Wave Power Generation Systems

Hong

Han

et al. 2020

Energies

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“…During the first two weeks of the project plan, the high level behavioural model has been adapted from [20] in MATLAB by stripping it off of any PID functionality to emulate a Implementation? Features [9] No Simulation Q-learning [10] No Real System Solved instability in practical applications [11] No Simulation Variable set-up [18] No Simulation Reduced memory requirements [15] No Theoretical Differentiable [1], [5] No The measurement takes place for the ranges of C = {1, 2 . .…”

Section: Phase I: Literature Review and Tradeoffmentioning

confidence: 99%

“…These two being based on the CMAC which is a cerebellum-based neural network originally proposed in 1975. It is considered superior to traditional neural networks due to its fast learning abilities of non-linear or unpredictable systems [1]- [5]. It is typically used to model the behavior of unknown systems with reasonable accuracy.…”

Section: Introductionmentioning

confidence: 99%

Design and implementation of an adaptive learning system: An MSc project experience

Martinez

Shafik

Acharyya

et al. 2016

2016 11th European Workshop on Microelectronics Education (EWME)

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Individual project is a major component of MSc degrees in the UK in terms of allocated marks and overall weight. An MSc project typically lasts for up to 14-16 weeks, which makes it challenging to carry out elaborate research works. In this paper we highlight our experience in setting up one such project, which addresses the design and implementation of a bio-inspired adaptive learning system using cerebellar model articulation controller (CMAC) principles. To systematically execute the project, initially a guided and comprehensive literature survey was carried out to analyze in detail the CMAC design tradeoffs. Underpinning this analysis an adaptive design methodology was developed, leading to the implementation on a field programmble gate array (FPGA). The implementation was then validated using a real-time audio compression application. The validations demonstrated the advantages of adaptive learning systems in terms of reduced energy consumption with improved throughput and convergence. Moreover, it underlined the importance of a good plan and systematic delivery for a successful short-term research project.

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“…However, the derivative information is not preserved. To acquire derivative information of input and output variable, Chiang and Lin developed a CMAC with a differentiable Gaussian receptive-field basis function and provided convergence analysis results for this network [43]. As mentioned above, we will approximate action and evaluation functions by RBF neural networks in our proposed algorithm.…”

Section: ) Cb-bg-based Motor Learning Algorithm With the Rbf Networkmentioning

confidence: 99%

Motor Learning Based on the Cooperation of Cerebellum and Basal Ganglia for a Self-Balancing Two-Wheeled Robot

Ruan¹,

Chen²,

Dai³

2011

ICA

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A novel motor learning method is present based on the cooperation of the cerebellum and basal ganglia for the behavior learning of agent. The motor learning method derives from the principle of CNS and operant learning mechanism and it depends on the interactions between the basal ganglia and cerebellum. The whole learning system is composed of evaluation mechanism, action selection mechanism, tropism mechanism. The learning signals come from not only the Inferior Olive but also the Substantia Nigra in the beginning. The speed of learning is increased as well as the failure time is reduced with the cerebellum as a supervisor. Convergence can be guaranteed in the sense of entropy. With the proposed motor learning method, a motor learning system for the self-balancing two-wheeled robot has been built using the RBF neural networks as the actor and evaluation function approximator. The simulation experiments showed that the proposed motor learning system achieved a better learning effect, so the motor learning based on the coordination of cerebellum and basal ganglia is effective.

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CMAC with General Basis Functions

Cited by 183 publications

References 7 publications

New Intelligent Control Strategy Hybrid Grey–RCMAC Algorithm for Ocean Wave Power Generation Systems

New Intelligent Control Strategy Hybrid Grey–RCMAC Algorithm for Ocean Wave Power Generation Systems

Design and implementation of an adaptive learning system: An MSc project experience

Motor Learning Based on the Cooperation of Cerebellum and Basal Ganglia for a Self-Balancing Two-Wheeled Robot

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