Neuro-Fuzzy Control Applications in Pressurized Water Reactors

Na, Man Gyun

doi:10.1007/978-3-7908-1866-6_9

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…Artificial neuro-fuzzy inference systems have been applied in different nuclear fields (Guimarães et al, 2006;Guimarães and Lapa, 2007). Many studies (Hines et al, 1997;Na, 1999;Na and Oh, 2002) on signal validation using artificial neuro-fuzzy inference system have been realized recently. Most of them (Hines et al, 1997;Na, 1999) use a gradient descendent technique for optimizing the antecedent parameters and a least means square method for the consequent parameters of the ANFIS.…”

Section: Introductionmentioning

confidence: 99%

“…Many studies (Hines et al, 1997;Na, 1999;Na and Oh, 2002) on signal validation using artificial neuro-fuzzy inference system have been realized recently. Most of them (Hines et al, 1997;Na, 1999) use a gradient descendent technique for optimizing the antecedent parameters and a least means square method for the consequent parameters of the ANFIS. More recently (Na and Oh, 2002), an optimization technique based on genetic algorithm was proposed for training the parameters in the antecedent part of a fuzzy system.…”

Section: Introductionmentioning

confidence: 99%

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Applying particle swarm optimization algorithm for tuning a neuro-fuzzy inference system for sensor monitoring

Oliveira

Schirru²

2009

Progress in Nuclear Energy

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A neuro-fuzzy inference system (ANFIS) tuned by particle swarm optimization (PSO) algorithm has been developed for monitoring the relevant sensor in a nuclear power plant (NPP) using the information of other sensors. The antecedent parameters of the ANFIS that estimates the relevant sensor signal are optimized by a PSO algorithm and consequent parameters use a least-squares algorithm. The proposed methodology to monitor sensor output signals was demonstrated through the estimation of the nuclear power value in a pressurized water reactor using as input to the ANFIS six other correlated signals. The obtained results are compared to two similar ANFIS using one gradient descendent (GD) and other genetic algorithm (GA), as antecedent parameters' training algorithm.

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