Neural networks for control systems: a survey
Automatica (Journal of IFAC)
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy systems for diagnosis
Fuzzy Sets and Systems - Special issue: application of neuro-fuzzy systems
Robust model-based fault diagnosis for dynamic systems
Robust model-based fault diagnosis for dynamic systems
Diagnosis and Fault-Tolerant Control
Diagnosis and Fault-Tolerant Control
Neural Computation
A learning algorithm for continually running fully recurrent neural networks
Neural Computation
Combining FDI and AI approaches within causal-model-based diagnosis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On fuzzy logic applications for automatic control, supervision, and fault diagnosis
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Fuzzy logic = computing with words
IEEE Transactions on Fuzzy Systems
Recurrent neuro-fuzzy networks for nonlinear process modeling
IEEE Transactions on Neural Networks
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
Comparison of four neural net learning methods for dynamic system identification
IEEE Transactions on Neural Networks
Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks
IEEE Transactions on Neural Networks
Gradient calculations for dynamic recurrent neural networks: a survey
IEEE Transactions on Neural Networks
Using SVM based method for equipment fault detection in a thermal power plant
Computers in Industry
A new strategy for automotive off-board diagnosis based on a meta-heuristic engine
Engineering Applications of Artificial Intelligence
A global modular framework for automotive diagnosis
Advanced Engineering Informatics
A multi-model approach for long-term runoff modeling using rainfall forecasts
Expert Systems with Applications: An International Journal
An improved decision-making rule of Dempster-Shafer theory application on fault diagnosis system
International Journal of Computer Applications in Technology
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This paper presents an application of recurrent neuro-fuzzy systems to fault detection and isolation in nuclear reactors. A general framework is adopted, in which a fuzzification module is linked to an inference module that is actually a neural network adapted to the recognition of the dynamic evolution of process variables and related faults. Process data is fuzzified in order to reason rather on qualitative than on quantitative values. The fuzzified attributes feed the neural network. Two different network topologies are tested over data simulated by a commissioned simulator of a nuclear reactor: a feed-forward topology and a recurrent topology, where the additional network inputs are considered as delayed activation of output units. The later approach shows better generalization performance for the detection and isolation of a number of security related faults. A graphic interface presents a qualitative representation of symptoms and diagnostic results by colored shades that evolve with time allowing a friendly and efficient communication with operators in charge of the process security.