Learning convergence of CMAC technique

Lin, Chun-Shin; Chiang, Ching-Tsan

doi:10.1109/72.641451

Cited by 102 publications

(2 citation statements)

References 27 publications

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“…However, the CMAC presents many attractive features and advantages, and is useful for real time applications. The CMAC has been used to solve various robotic problems, and applied in the field of controls, medical science, pattern recognition, signal processing and image processing [ 23 , 24 ].…”

Section: Cerebellar Model Articulation Controller (Cmac) Neural Netwomentioning

confidence: 99%

Hazardous Odor Recognition by CMAC Based Neural Networks

Bucak

Karlık

2009

Sensors

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Electronic noses are being developed as systems for the automated detection and classification of odors, vapors, and gases. Artificial neural networks (ANNs) have been used to analyze complex data and to recognize patterns, and have shown promising results in recognition of volatile compounds and odors in electronic nose applications. When an ANN is combined with a sensor array, the number of detectable chemicals is generally greater than the number of unique sensor types. The odor sensing system should be extended to new areas since its standard style where the output pattern from multiple sensors with partially overlapped specificity is recognized by a neural network or multivariate analysis. This paper describes the design, implementation and performance evaluations of the application developed for hazardous odor recognition using Cerebellar Model Articulation Controller (CMAC) based neural networks.

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Section: Cerebellar Model Articulation Controller (Cmac) Neural Netwomentioning

confidence: 99%

Hazardous Odor Recognition by CMAC Based Neural Networks

Bucak

Karlık

2009

Sensors

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“…Therefore, the application of CMAC in desulphurization system is reasonably expected to achieve satisfying prediction accuracy, which will be further favorable for process optimization. However, the studies on the error bound of CMAC are insufficient (Lin & Chiang, 1997;Lin & Wang, 1996). Physical storage size on the computer required by data is usually the first parameter to be determined (Lin & Wang, 1996;Tamura et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Prediction and Optimization of a Desulphurization System Using Cmac Neural Network and Genetic Algorithm

Kong

Zhang

Wang

et al. 2020

Journal of Environmental Engineering and Landscape Management

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In this paper, taking desulphurizing ratio and economic cost as two objectives, a ten-input two-output prediction model was structured and validated for desulphurization system. Cerebellar model articulation controller (CMAC) neural network and genetic algorithm (GA) were used for model building and optimization of cost respectively. In the model building process, the grey relation entropy analysis and uniform design method were used to screen the input variables and study the model parameters separately. Traditional regression analysis and proposed location number analysis method were adopted to analyze output errors of experiment group and predict the results of test group. Results show that regression analyses keep high fit degree with experiment group results while the fitting accuracies for test group are quite different. As for location number analysis, a power function between output errors and location numbers was fitted well with the data of experiment group and test group for SO2. Prediction model was initialized by location number analysis method. Model was validated and cost optimization case was performed with GA subsequently. The result shows that the optimal cost obtained from GA could be reduced by more than 30% compared with original optimal operating parameters under same constraints.

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Cerebellar Model Arithmetic Computer

Horväth

2009

Wiley Encyclopedia of Computer Science and Engineering

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Cerebellar Model Arithmetic Computer (CMAC) is a special neural network architecture that was proposed as a functional model of a certain part of the mammalian brain, which is called the cerebellum. Cerebellum (from the Latin word “little brain”) is a fist‐sized structure located at the lower back of the brain. It is believed that its main function is to coordinate movements of muscles and joints, but recent evidence suggests that the cerebellum is partially responsible for speech, learning, emotions, and attention. CMAC as an artificial neural network architecture was introduced by Albus in 1975. This architecture is based on a theory developed independently by D. Marr and J. Albus around 1970. As it was developed as a simple model of the cerebellum, its main task was to control manipulators. Its original name Cerebellar Model Articulation Controller also comes from this first application.

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Learning convergence of CMAC technique

Cited by 102 publications

References 27 publications

Hazardous Odor Recognition by CMAC Based Neural Networks

Hazardous Odor Recognition by CMAC Based Neural Networks

Prediction and Optimization of a Desulphurization System Using Cmac Neural Network and Genetic Algorithm

Cerebellar Model Arithmetic Computer

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