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Simulation of superconductive fault current limiter (SFCL) using modular neural networks
Makki, B ; Sharif University of Technology | 2006
368
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- Type of Document: Article
- DOI: 10.1109/IECON.2006.347367
- Publisher: 2006
- Abstract:
- Modular Neural Networks have had significant success in a wide range of applications because of their superiority over single non-modular ones in terms of proper data representation, feasibility of hardware implementation and faster learning. This paper presents a constructive multilayer neural network (CMNN) in conjunction with a Hopfield model using a new cost function to simulate the behavior of superconductive fault current limiters (SFCLs). The results show that the proposed approach can efficiently simulate the behavior of SFCLs. ©2006 IEEE
- Keywords:
- Artificial intelligence ; Electric fault currents ; Electronics industry ; Hardware ; Industrial electronics ; Ketones ; Limiters ; Multilayer neural networks ; Vegetation ; Annual conference ; Data representations ; Faster learning ; Fault current limiter ; Fault current limiters ; Hardware implementations ; Hopfield models ; Modular neural networks ; Multi layering ; Neural networks
- Source: IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics, Paris, 6 November 2006 through 10 November 2006 ; 2006 , Pages 4415-4419 ; 1424401364 (ISBN); 9781424401369 (ISBN)
- URL: https://ieeexplore.ieee.org/document/4153244