Support vector machines for disruption prediction and novelty detection at JET

被引:31
作者
Cannas, B.
Delogu, R. S.
Fannia, A.
Sonato, P.
Zedda, M. K.
机构
[1] Univ Cagliari, Dept Elect Engn & Elect, I-09123 Cagliari, Italy
[2] Assoc Euratom ENEA Fus, Consorzio RFX, I-35127 Padua, Italy
关键词
disruption predictiow neural networks; novelty detection; support vector machines;
D O I
10.1016/j.fusengdes.2007.07.004
中图分类号
TL [原子能技术]; O571 [原子核物理学];
学科分类号
0827 ; 082701 ;
摘要
In the last years there has been a growing interest on black box approaches to disruption prediction. The drawback of these approaches is that the system could deteriorate its performance once it does not get updated. This could be the case of a disruption predictor for JET, where new plasma configurations might present features completely different from those observed in the experiments used during the training phase. This 'novelty' can be incorrectly classified by the system. A novelty detection method, which determines the novelty of the input of the prediction system, can be used to assess the system reliability. This paper presents a support vector machines disruption predictor for JET, wherein multiple plasma diagnostic signals are combined to provide a composite impending disruption warning indicator. In a support vector machine the analysis of the decision function value gives useful information about the novelty of an input and, on the reliability of the predictor output, during on-line applications. Results show the suitability of support vector machines both for prediction and novelty detection tasks at JET. (C) 2007 Elsevier B.V. All rights reserved.
引用
收藏
页码:1124 / 1130
页数:7
相关论文
共 14 条
[1]   NOVELTY DETECTION AND NEURAL-NETWORK VALIDATION [J].
BISHOP, CM .
IEE PROCEEDINGS-VISION IMAGE AND SIGNAL PROCESSING, 1994, 141 (04) :217-222
[2]   Automatic disruption classification at JET: comparison of dinerent pattern recognition techniques [J].
Cannas, B. ;
Cau, F. ;
Fanni, A. ;
Sonato, P. ;
Zedda, M. K. .
NUCLEAR FUSION, 2006, 46 (07) :699-708
[3]  
CANNAS B, 2002, NUCL FUSION, V37, P100
[4]  
CANNAS B, 2004, P 31 EPS C PLASM P G, V28
[5]  
CANNAS B, 2005, P 32 EPS C CONTR FUS
[6]   LIBSVM: A Library for Support Vector Machines [J].
Chang, Chih-Chung ;
Lin, Chih-Jen .
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY, 2011, 2 (03)
[7]   SELF-ORGANIZED FORMATION OF TOPOLOGICALLY CORRECT FEATURE MAPS [J].
KOHONEN, T .
BIOLOGICAL CYBERNETICS, 1982, 43 (01) :59-69
[8]   Novelty detection: a review - part 2: neural network based approaches [J].
Markou, M ;
Singh, S .
SIGNAL PROCESSING, 2003, 83 (12) :2499-2521
[9]   Novelty detection: a review - part 1: statistical approaches [J].
Markou, M ;
Singh, S .
SIGNAL PROCESSING, 2003, 83 (12) :2481-2497
[10]  
Shawe-Taylor J, 2000, An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods, DOI DOI 10.1017/CB09780511801389