Fuzzy sets in pattern recognition: methodology and methods
Pattern Recognition
Neural-Network-Based Fuzzy Logic Control and Decision System
IEEE Transactions on Computers - Special issue on artificial neural networks
Fuzzy regression analysis using neural networks
Fuzzy Sets and Systems
Fuzzy Sets and Systems - Special issue on fuzzy neural control
Fuzzy engineering
Learning fuzzy rules and approximate reasoning in fuzzy neural networks and hybrid systems
Fuzzy Sets and Systems - Special issue on connectionist and hybrid connectionist systems for approximate reasoning
Neuro-fuzzy systems for function approximation
Fuzzy Sets and Systems - Special issue on analytical and structural considerations in fuzzy modeling
Unsupervised Bayesian visualization of high-dimensional data
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing
Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing
A Principal Components Approach to Combining Regression Estimates
Machine Learning
An Implementation of Logical Analysis of Data
IEEE Transactions on Knowledge and Data Engineering
Fuzzified neural network based on fuzzy number operations
Fuzzy Sets and Systems - Fuzzy intervals
Rule Extraction from Recurrent Neural Networks: A Taxonomy and Review
Neural Computation
Adaptive Mamdani fuzzy model for condition-based maintenance
Fuzzy Sets and Systems
International Journal of Intelligent Systems Technologies and Applications
Chaotic Time Series Prediction Using a Neuro-Fuzzy System with Time-Delay Coordinates
IEEE Transactions on Knowledge and Data Engineering
Incremental learning of dynamic fuzzy neural networks for accurate system modeling
Fuzzy Sets and Systems
IEEE Transactions on Evolutionary Computation
A neural fuzzy system with fuzzy supervised learning
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POPFNN-AAR(S): a pseudo outer-product based fuzzy neural network
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A neural-fuzzy system for congestion control in ATM networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Online elicitation of Mamdani-type fuzzy rules via TSK-based generalized predictive control
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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 evolutionary artificial neural networks approach for breast cancer diagnosis
Artificial Intelligence in Medicine
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
On the stability issues of linear Takagi-Sugeno fuzzy models
IEEE Transactions on Fuzzy Systems
Comment on “Stability issues on Takagi-Sugeno fuzzy model-parametric approach” [and reply]
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Effect of rule weights in fuzzy rule-based classification systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Data-driven linguistic modeling using relational fuzzy rules
IEEE Transactions on Fuzzy Systems
Computing derivatives in interval type-2 fuzzy logic systems
IEEE Transactions on Fuzzy Systems
Rule Weight Specification in Fuzzy Rule-Based Classification Systems
IEEE Transactions on Fuzzy Systems
NFI: a neuro-fuzzy inference method for transductive reasoning
IEEE Transactions on Fuzzy Systems
Artificial Neural Networks are Zero-Order TSK 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
Neural networks that learn from fuzzy if-then rules
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Neural Networks
Neuro-fuzzy rule generation: survey in soft computing framework
IEEE Transactions on Neural Networks
Subsethood-product fuzzy neural inference system (SuPFuNIS)
IEEE Transactions on Neural Networks
GenSoFNN: a generic self-organizing fuzzy neural network
IEEE Transactions on Neural Networks
Orthogonal least squares learning algorithm for radial basis function networks
IEEE Transactions on Neural Networks
Asymmetric subsethood-product fuzzy neural inference system (ASuPFuNIS)
IEEE Transactions on Neural Networks
Observer-based direct adaptive fuzzy-neural control for nonaffine nonlinear systems
IEEE Transactions on Neural Networks
Self-Organizing Adaptive Fuzzy Neural Control for a Class of Nonlinear Systems
IEEE Transactions on Neural Networks
Fuzzy wavelet neural network models for prediction and identification of dynamical systems
IEEE Transactions on Neural Networks
A probabilistic fuzzy approach to modeling nonlinear systems
Neurocomputing
Review: Hybrid expert systems: A survey of current approaches and applications
Expert Systems with Applications: An International Journal
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Fuzzy rule derivation is often difficult and time-consuming, and requires expert knowledge. This creates a common bottleneck in fuzzy system design. In order to solve this problem, many fuzzy systems that automatically generate fuzzy rules from numerical data have been proposed. In this paper, we propose a fuzzy neural network based on mutual subsethood (MSBFNN) and its fuzzy rule identification algorithms. In our approach, fuzzy rules are described by different fuzzy sets. For each fuzzy set representing a fuzzy rule, the universe of discourse is defined as the summation of weighted membership grades of input linguistic terms that associate with the given fuzzy rule. In this manner, MSBFNN fully considers the contribution of input variables to the joint firing strength of fuzzy rules. Afterwards, the proposed fuzzy neural network quantifies the impacts of fuzzy rules on the consequent parts by fuzzy connections based on mutual subsethood. Furthermore, to enhance the knowledge representation and interpretation of the rules, a linear transformation from consequent parts to output is incorporated into MSBFNN so that higher accuracy can be achieved. In the parameter identification phase, the backpropagation algorithm is employed, and proper linear transformation is also determined dynamically. To demonstrate the capability of the MSBFNN, simulations in different areas including classification, regression and time series prediction are conducted. The proposed MSBFNN shows encouraging performance when benchmarked against other models.