A resource-allocating network for function interpolation
Neural Computation
Fuzzy neural networks: a survey
Fuzzy Sets and Systems
A function estimation approach to sequential learning with neural networks
Neural Computation
Fuzzy Sets and Systems - Special issue on fuzzy neural control
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
POPFNN: a pseudo outer-product based fuzzy neural network
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Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Foundations of Neuro-Fuzzy Systems
Foundations of Neuro-Fuzzy Systems
Artificial Intelligence in Medicine
On-line identification of computationally undemanding evolving fuzzy models
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A brain-inspired fuzzy semantic memory model for learning and reasoning with uncertainty
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Application of Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis
IEEE Transactions on Computers
Expert Systems with Applications: An International Journal
Financial market trading system with a hierarchical coevolutionary fuzzy predictive model
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A decade of Kasabov's evolving connectionist systems: a review
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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A two-stage evolutionary process for designing TSK fuzzy rule-basedsystems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On generating FC3 fuzzy rule systems from data usingevolution strategies
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
POPFNN-AAR(S): a pseudo outer-product based fuzzy neural network
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
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
Falcon: neural fuzzy control and decision systems using FKP and PFKP clustering algorithms
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
Improving the interpretability of TSK fuzzy models by combining global learning and local learning
IEEE Transactions on Fuzzy Systems
Designing fuzzy inference systems from data: An interpretability-oriented review
IEEE Transactions on Fuzzy Systems
Fuzzy CoCo: a cooperative-coevolutionary approach to fuzzy modeling
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Design for Self-Organizing Fuzzy Neural Networks Based on Genetic Algorithms
IEEE Transactions on Fuzzy Systems
Fuzzy-XCS: A Michigan Genetic Fuzzy System
IEEE Transactions on Fuzzy Systems
SGERD: A Steady-State Genetic Algorithm for Extracting Fuzzy Classification Rules From Data
IEEE Transactions on Fuzzy Systems
Evolving Fuzzy-Rule-Based Classifiers From Data Streams
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
FLEXFIS: A Robust Incremental Learning Approach for Evolving Takagi–Sugeno Fuzzy Models
IEEE Transactions on Fuzzy Systems
Fully Evolvable Optimal Neurofuzzy Controller Using Adaptive Critic Designs
IEEE Transactions on Fuzzy Systems
An Evolving Fuzzy Predictor for Industrial Applications
IEEE Transactions on Fuzzy Systems
Efficient Self-Evolving Evolutionary Learning for Neurofuzzy Inference Systems
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
FCMAC-Yager: A Novel Yager-Inference-Scheme-Based Fuzzy CMAC
IEEE Transactions on Neural Networks
Hierarchically Clustered Adaptive Quantization CMAC and Its Learning Convergence
IEEE Transactions on Neural Networks
DCT-Yager FNN: A Novel Yager-Based Fuzzy Neural Network With the Discrete Clustering Technique
IEEE Transactions on Neural Networks
PSECMAC: A Novel Self-Organizing Multiresolution Associative Memory Architecture
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A BCM theory of meta-plasticity for online self-reorganizing fuzzy-associative learning
IEEE Transactions on Neural Networks
A novel application of a neuro-fuzzy computational technique in event-based rainfall-runoff modeling
Expert Systems with Applications: An International Journal
An evolving type-2 neural fuzzy inference system
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Expert Systems with Applications: An International Journal
Stock trading with cycles: A financial application of ANFIS and reinforcement learning
Expert Systems with Applications: An International Journal
Cultural dependency analysis for understanding speech emotion
Expert Systems with Applications: An International Journal
A novel brain-inspired neuro-fuzzy hybrid system for artificial ventilation modeling
Expert Systems with Applications: An International Journal
eT2FIS: An Evolving Type-2 Neural Fuzzy Inference System
Information Sciences: an International Journal
kENFIS: kNN-based evolving neuro-fuzzy inference system for computer worms detection
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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Fuzzy rule-based systems (FRBSs) have been successfully applied to many areas. However, traditional fuzzy systems are often manually crafted, and their rule bases that represent the acquired knowledge are static and cannot be trained to improve the modeling performance. This subsequently leads to intensive research on the autonomous construction and tuning of a fuzzy system directly from the observed training data to address the knowledge acquisition bottleneck, resulting in well-established hybrids such as neural-fuzzy systems (NFSs) and genetic fuzzy systems (GFSs). However, the complex and dynamic nature of real-world problems demands that fuzzy rule-based systems and models be able to adapt their parameters and ultimately evolve their rule bases to address the nonstationary (time-varying) characteristics of their operating environments. Recently, considerable research efforts have been directed to the study of evolving Tagaki-Sugeno (T-S)-type NFSs based on the concept of incremental learning. In contrast, there are very few incremental learning Mamdani-type NFSs reported in the literature. Hence, this paper presents the evolving neural-fuzzy semantic memory (eFSM) model, a neural-fuzzy Mamdani architecture with a data-driven progressively adaptive structure (i.e., rule base) based on incremental learning. Issues related to the incremental learning of the eFSM rule base are carefully investigated, and a novel parameter learning approach is proposed for the tuning of the fuzzy set parameters in eFSM. The proposed eFSM model elicits highly interpretable semantic knowledge in the form of Mamdani-type IF-THEN fuzzy rules from low-level numeric training data. These Mamdani fuzzy rules define the computing structure of eFSM and are incrementally learned with the arrival of each training data sample. New rules are constructed from the emergence of novel training data and obsolete fuzzy rules that no longer describe the recently observed data trends are pruned. This enables eFSM to maintain a current and compact set of Mamdani-type IF-THEN fuzzy rules that collectively generalizes and describes the salient associative mappings between the inputs and outputs of the underlying process being modeled. The learning and modeling performances of the proposed eFSM are evaluated using several benchmark applications and the results are encouraging.