Modern Control Engineering
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
A fuzzy controller with evolving structure
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Bio-inspired systems (BIS)
Direct adaptive fuzzy control with a self-structuring algorithm
Fuzzy Sets and Systems
Adaptive fuzzy control of a non-linear servo-drive: Theory and experimental results
Engineering Applications of Artificial Intelligence
Fuzzy-identification-based adaptive backstepping control using a self-organizing fuzzy system
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Expert Systems with Applications: An International Journal
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews - Special issue on information reuse and integration
Knowledge-based parameter identification of TSK fuzzy models
Applied Soft Computing
TaSe, a Taylor series-based fuzzy system model that combines interpretability and accuracy
Fuzzy Sets and Systems
Direct adaptive self-structuring fuzzy controller for nonaffine nonlinear system
Fuzzy Sets and Systems
A type-2 fuzzy embedded agent to realise ambient intelligence in ubiquitous computing environments
Information Sciences: an International Journal
Polynomial fuzzy models for nonlinear control: a Taylor series approach
IEEE Transactions on Fuzzy Systems
A systematic approach to a self-generating fuzzy rule-table forfunction approximation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A highly interpretable form of Sugeno inference systems
IEEE Transactions on Fuzzy Systems
Self-organized fuzzy system generation from training examples
IEEE Transactions on Fuzzy Systems
Designing fuzzy inference systems from data: An interpretability-oriented review
IEEE Transactions on Fuzzy Systems
Online adaptive fuzzy neural identification and control of a class of MIMO nonlinear systems
IEEE Transactions on Fuzzy Systems
Multiobjective identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Fuzzy Systems
Stable auto-tuning of adaptive fuzzy/neural controllers for nonlinear discrete-time systems
IEEE Transactions on Fuzzy Systems
Online global learning in direct fuzzy controllers
IEEE Transactions on Fuzzy Systems
Effective optimization for fuzzy model predictive control
IEEE Transactions on Fuzzy Systems
Design for Self-Organizing Fuzzy Neural Networks Based on Genetic Algorithms
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Quick Design of Fuzzy Controllers With Good Interpretability in Mobile Robotics
IEEE Transactions on Fuzzy Systems
Self-Organizing Adaptive Fuzzy Neural Control for a Class of Nonlinear Systems
IEEE Transactions on Neural Networks
Functional equivalence between radial basis function networks and fuzzy inference systems
IEEE Transactions on Neural Networks
A New Methodology for the Online Adaptation of Fuzzy Self-Structuring Controllers
IEEE Transactions on Fuzzy Systems
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The online evolution and learning of fuzzy systems is highly important when dealing with changing environments over time. This capability is especially relevant in the field of control, due to the special characteristics of control problems. In this field, techniques capable of developing controllers with a minimum amount of prior knowledge about the plants to be controlled are desired. Furthermore, these controllers should provide a reduced number of interpretable rules. This paper presents a new Online Self-Evolving Neuro Fuzzy controller based on the Taylor Series Neuro Fuzzy (TaSe-NF) model. Under the assumption of no prior knowledge about the differential equations that define the plant to be controlled, this methodology is capable of incrementally evolving the structure of the controller and adapting its parameters online, while controlling the plant. The new methodology uses a scattered distribution of the fuzzy rules, thereby reducing the number of rules in the fuzzy controller. Moreover, the use of the TaSe-NF model to represent the antecedents of the rules enhances the interpretability of the obtained rules. The proposed evolving fuzzy controller is composed of two main blocks: On the one hand, the online local learning of the rule consequents tackles the task of providing a proper control at the present moment. On the other hand, a structure self-evolution method analyzes the error surface to determine which cluster/rule suffers the worst performance and therefore, needs to be further split. Simulation results are presented to illustrate the capabilities of this new online self-evolving controller.