Essentials of fuzzy modeling and control
Essentials of fuzzy modeling and control
Learning in the presence of concept drift and hidden contexts
Machine Learning
Evolving rule-based models: a tool for design of flexible adaptive systems
Evolving rule-based models: a tool for design of flexible adaptive systems
Detecting Concept Drift with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Soft Sensors for Monitoring and Control of Industrial Processes (Advances in Industrial Control)
Soft Sensors for Monitoring and Control of Industrial Processes (Advances in Industrial Control)
An approach to online identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Fuzzy Systems
Identification of evolving fuzzy rule-based models
IEEE Transactions on Fuzzy Systems
Handling drifts and shifts in on-line data streams with evolving fuzzy systems
Applied Soft Computing
Evolving intelligent system for the modelling of nonlinear systems with dead-zone input
Applied Soft Computing
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A new technique to the design and use of inferential sensors in the process industry is proposed in this paper, which is based on the recently introduced concept of evolving fuzzy models (EFMs). They address the challenge that the modern process industry faces today, namely, to develop such adaptive and self-calibrating online inferential sensors that reduce the maintenance costs while keeping the high precision and interpretability/ transparency. The proposed new methodology makes possible inferential sensors to recalibrate automatically, which reduces significantly the life-cycle efforts for their maintenance. This is achieved by the adaptive and flexible open-structure EFM used. The novelty of this paper lies in the following: 1) the overall concept of inferential sensors with evolving and self-developing structure from the data streams); 2) the new methodology for online automatic selection of input variables that are most relevant for the prediction; 3) the technique to detect automatically a shift in the data pattern using the age of the clusters (and fuzzy rules); 4) the online standardization technique used by the learning procedure of the evolving model; and 5) the application of this innovative approach to several real-life industrial processes from the chemical industry (evolving inferential sensors, namely, eSensors, were used for predicting the chemical properties of different products in The Dow Chemical Company, Freeport, TX). It should be noted, however, that the methodology and conclusions of this paper are valid for the broader area of chemical and process industries in general. The results demonstrate that well-interpretable and with-simple-structure inferential sensors can automatically be designed from the data stream in real time, which predict various process variables of interest. The proposed approach can be used as a basis for the development of a new generation of adaptive and evolving inferential sensors that can address the challenges of the modern advanced process industry.