Artificial intelligence for monitoring and supervisory control of process systems
Engineering Applications of Artificial Intelligence
Performance Monitoring of Closed-Loop Controlled Systems Using dFasArt
IWINAC '07 Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II
dFasArt: Dynamic neural processing in FasArt model
Neural Networks
Fault detection and fuzzy rule extraction in AC motors by a neuro-fuzzy ART-based system
Engineering Applications of Artificial Intelligence
IEEE Transactions on Intelligent Transportation Systems
Diagnosis of poor control-loop performance using higher-order statistics
Automatica (Journal of IFAC)
IEEE Transactions on Neural Networks
ART-EMAP: A neural network architecture for object recognition by evidence accumulation
IEEE Transactions on Neural Networks
Information Sciences: an International Journal
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This paper presents the neuro-fuzzy dFasArt (dynamic FasArt) architecture as an extension of the FasArt model including a dynamic algorithm formulation. This allows dFasArt to deal with identification and clustering problems using the temporal information of the signals. The focus is placed on the application of dFasArt to the control systems field for monitoring the controller performance. It is presented through two selected experiments covering some interesting control issues. The first one shows the use of dFasArt to decide when the parameters adaption is needed in a classic adaptive control scheme. The second one analyzes the behaviour of closed-loop controlled systems to establish a classification of the system operational states, starting from the measured data. Digital signal processing is used to represent the temporal signals with spatial patterns and dFasArt is proposed to classify these patterns on-line. Real scale plants have been used to carry out several experiments with good results. This shows dFasArt as a feasible tool to deal with control loop performance monitoring and controller performance assessment in industrial processes.