A fuzzy controller with evolving structure
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Bio-inspired systems (BIS)
On-line identification of computationally undemanding evolving fuzzy models
Fuzzy Sets and Systems
Identification of fuzzy systems by means of genetic optimization and data granulation
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Data-driven fuzzy modeling for Takagi-Sugeno-Kang fuzzy system
Information Sciences: an International Journal
SOFMLS: online self-organizing fuzzy modified least-squares network
IEEE Transactions on Fuzzy Systems
An evolving fuzzy neural network based on the mapping of similarities
IEEE Transactions on Fuzzy Systems
Adaptive inferential sensors based on evolving fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A new systematic design for Habitually Linear Evolving TS Fuzzy Model
Expert Systems with Applications: An International Journal
Density-based averaging - A new operator for data fusion
Information Sciences: an International Journal
Computers and Electrical Engineering
A fast learning algorithm for evolving neo-fuzzy neuron
Applied Soft Computing
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An approach to identification of evolving fuzzy rule-based (eR) models is proposed. eR models implement a method for the noniterative update of both the rule-base structure and parameters by incremental unsupervised learning. The rule-base evolves by adding more informative rules than those that previously formed the model. In addition, existing rules can be replaced with new rules based on ranking using the informative potential of the data. In this way, the rule-base structure is inherited and updated when new informative data become available, rather than being completely retrained. The adaptive nature of these evolving rule-based models, in combination with the highly transparent and compact form of fuzzy rules, makes them a promising candidate for modeling and control of complex processes, competitive to neural networks. The approach has been tested on a benchmark problem and on an air-conditioning component modeling application using data from an installation serving a real building. The results illustrate the viability and efficiency of the approach.