An algorithm for automatic rule induction

A pattern-recognition algorithm has been used to search for edge operational space diagrams which discriminate well between the low-mode (L-mode) and high-mode (H-mode) states in the DIII-D [J. L. Luxon, R. Anderson, F. Batty et al., in Proceedings of the 11th Conference on Plasma Physics and Controlled Nuclear Fusion Research, 1986 (International Atomic Energy Agency, Vienna, 1987), Vol. I, p. 159] tokamak. The study uses a large database of electron temperatures Te, electron densities ne, electron pressures Pe, and their gradients, obtained for a fixed magnetic configuration. The data are evaluated at the edge of the plasma in the region where the H-mode transport barrier forms. An inductive classification algorithm shows that operational space diagrams which include either ∇Pe or ∇Te provide significantly better discrimination between the L-mode and H-mode states than diagrams based on Te and ne alone. These conclusions are based on standard metrics of pattern recognition. The successful application of a classification algorithm to this simple system provides confidence that classification techniques can be applied to larger and more complex databases of edge parameters. Moreover, these results suggest that edge pressure or temperature gradients should be considered on an equal footing with edge temperature as potential control parameters for the H-mode transition.

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“…The latest in a series of RULES inductive learning algorithms, RULES-4 is different from its predecessors 15 …”

Section: Inductive Learning Techniquementioning

confidence: 96%

Inductive classification of low-mode and high-mode edge parameters

2000

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“…Pham and Aksoy have improved RULES-1 and produced RULES-2 [19]. The rule forming strategy of RULES-2 is almost the same as that of RULES-1.…”

Section: Rules-2mentioning

confidence: 96%

A Review of Rules Family of Algorithms

Aksoy

2008

MCA

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In recent years, there has been a growing amount of research on inductive learning. Out of this research a number of promising algorithms have surfaced. In the paper after a brief description of knowledge acquisition, induction and inductive learning; RULES family of inductive learning algorithms, their strengths as well as weaknesses are explained and discussed. The applications of inductive learning and particularly the applications of RULES family of algorithms are overviewed.

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“…RULES-3 [14] is a simple algorithm for extracting a set of classification rules from a collection of examples for objects belonging to one of a number of known classes. An object must be described in terms of a fixed set of attributes, each with its own range of possible values which could be nominal or numerical.…”

Section: Rules-3mentioning

confidence: 99%

Dynamic System Modeling Using Rules3 Induction Algorithm

Aksoy

2005

MCA

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This paper describes the use of a new induction algorithm to derive production rules for modelling dynamic systems. The algorithm, called RULES-3, is an efficient tool for extracting a compact set of IF-THEN rules from a collection of examples to classify objects into known categories. The paper summarises the operation of RULES-3 and presents the results obtained in the modelling of two linear systems and two non-linear systems.

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An algorithm for automatic rule induction

Cited by 28 publications

References 1 publication

Inductive classification of low-mode and high-mode edge parameters

Inductive classification of low-mode and high-mode edge parameters

A Review of Rules Family of Algorithms

Dynamic System Modeling Using Rules3 Induction Algorithm

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