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Title: FCMAC: A fuzzified cerebellar model articulation controller with self-organizing capacity
Authors: Nie, J. 
Linkens, D.A.
Keywords: Biomedical
Fuzzy control
Learning systems
Model reference control
Multivariable systems
Neural nets
Self-organizing systems
Issue Date: 1994
Citation: Nie, J., Linkens, D.A. (1994). FCMAC: A fuzzified cerebellar model articulation controller with self-organizing capacity. Automatica 30 (4) : 655-664. ScholarBank@NUS Repository.
Abstract: The Albus's Cerebellar Model Articulation Controller (CMAC) network has been used in many practical areas with considerable success. This paper presents a fuzzified CMAC network (FCMAC) acting as a multivariable adaptive controller with the feature of self-organizing association cells and the further ability of self-learning the required teacher signals in real-time. In particular, the original CMAC has been reformulated within a framework of a simplified fuzzy control algorithm (SFCA) and the associated self-learning algorithms have been developed as a result of incorporating the schemes of competitive learning and iterative learning control into the system. By using a similarity-measure-based, instead of coding-algorithm-based, content-addressable scheme, FCMAC is capable of dealing with arbitrary-dimensional continuous input space in a simple manner without involving complicated discretizing, quantizing, coding, and hashing procedures used in the original CMAC. The learning control system described here can be thought of as either a completely unsupervised fuzzy-neural control strategy without relying on the process model or equivalently an automatic real-time knowledge acquisition scheme for the implementation of fuzzy controllers. The proposed approach has been applied to a multivariable blood pressure control problem which is characterized by strong interaction between variables and large time delays.
Source Title: Automatica
ISSN: 00051098
DOI: 10.1016/0005-1098(94)90154-6
Appears in Collections:Staff Publications

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