Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
An introduction to fuzzy control (2nd ed.)
An introduction to fuzzy control (2nd ed.)
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
PRICAI '02 Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Detecting and tracking regional outliers in meteorological data
Information Sciences: an International Journal
Application of Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis
IEEE Transactions on Computers
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
eFSM: a novel online neural-fuzzy semantic memory model
IEEE Transactions on Neural Networks
An interval type-2 fuzzy-neural network with support-vector regression for noisy regression problems
IEEE Transactions on Fuzzy Systems
On the robustness of Type-1 and Interval Type-2 fuzzy logic systems in modeling
Information Sciences: an International Journal
Identification of transparent, compact, accurate and reliable linguistic fuzzy models
Information Sciences: an International Journal
Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures
Information Sciences: an International Journal
A survey of fuzzy clustering algorithms for pattern recognition. I
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
NeuroFAST: on-line neuro-fuzzy ART-based structure and parameterlearning TSK model
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolving fuzzy neural networks for supervised/unsupervised onlineknowledge-based learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An approach to online identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Type-2 Self-Organizing Neural Fuzzy System and Its FPGA Implementation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An ART-based fuzzy adaptive learning control network
IEEE Transactions on Fuzzy Systems
An online self-constructing neural fuzzy inference network and its applications
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Self-organized fuzzy system generation from training examples
IEEE Transactions on Fuzzy Systems
Interval type-2 fuzzy logic systems: theory and design
IEEE Transactions on Fuzzy Systems
Designing fuzzy inference systems from data: An interpretability-oriented review
IEEE Transactions on Fuzzy Systems
A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
A Self-Evolving Interval Type-2 Fuzzy Neural Network With Online Structure and Parameter Learning
IEEE Transactions on Fuzzy Systems
Neuro-fuzzy rule generation: survey in soft computing framework
IEEE Transactions on Neural Networks
GenSoFNN: a generic self-organizing fuzzy neural network
IEEE Transactions on Neural Networks
Learning and tuning fuzzy logic controllers through reinforcements
IEEE Transactions on Neural Networks
SaFIN: A Self-Adaptive Fuzzy Inference Network
IEEE Transactions on Neural Networks - Part 1
A novel brain-inspired neuro-fuzzy hybrid system for artificial ventilation modeling
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
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There are two main approaches to design a neural fuzzy system; namely, through expert knowledge, and through numerical data. While the computational structure of a system is manually crafted by human experts in the former case, self-organizing neural fuzzy systems that are able to automatically extract generalized knowledge from batches of numerical training data are proposed for the latter. Nevertheless, both of these approaches are static where only parameters of a system are updated during training. On the other hand, the demands and complexities of real-life applications often require a neural fuzzy system to adapt both its parameters and structure to model the changing dynamics of the environment. To counter these modeling bottlenecks, intense research efforts are subsequently channeled into the studies of evolving/online neural fuzzy systems. There are generally two classes of evolving neural fuzzy systems: the Takagi-Sugeno-Kang (TSK) systems and the Mamdani systems. While most existing literature consists of evolving Type-1 TSK-typed and Type-1 Mamdani-typed models, they may not perform well in noisy environment. To improve the robustness of these neural fuzzy systems, recent efforts have been directed to extend evolving Type-1 TSK-typed neural fuzzy systems to Type-2 models because of their better known noise resistance abilities. In contrast, minimum similar effort has been made for evolving Mamdani-typed models. In this paper, we present a novel evolving Type-2 Mamdani-typed neural fuzzy system to bridge this gap. The proposed system is named evolving Type-2 neural fuzzy inference system (eT2FIS), and it employs a data-driven incremental learning scheme. Issues involving the online sequential learning of the eT2FIS model are carefully examined. A new rule is created when a newly arrived data is novel to the present knowledge encrypted; and an obsolete rule is deleted when it is no longer relevant to the current environment. Highly over-lapping fuzzy labels in the input-output spaces are merged to reduce the computational complexity and improve the overall interpretability of the system. By combining these three operations, eT2FIS is ensured a compact and up-to-date fuzzy rule base that is able to model the current underlying dynamics of the environment. Subsequently, the proposed eT2FIS model is employed in a series of benchmark and real-world applications to demonstrate its efficiency as an evolving neural fuzzy system, and encouraging performances have been achieved.