Discrete-time control systems
International Journal of Approximate Reasoning
Applying genetics to fuzzy logic
AI Expert
Neural-Network-Based Fuzzy Logic Control and Decision System
IEEE Transactions on Computers - Special issue on artificial neural networks
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Genetic algorithms for fuzzy controllers
AI Expert
Implementing fuzzy logic controllers using a neural network
Fuzzy Sets and Systems
Neurocontrol and fuzzy logic: connections and designs
International Journal of Approximate Reasoning - Special issue on fuzzy logic and neural networks for pattern recognition and control
Fuzzy time series and its models
Fuzzy Sets and Systems
Handling forecasting problems using fuzzy time series
Fuzzy Sets and Systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Temperature prediction using fuzzy time series
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
GA-based fuzzy reinforcement learning for control of a magneticbearing system
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An ART-based fuzzy adaptive learning control network
IEEE Transactions on Fuzzy Systems
GA-fuzzy modeling and classification: complexity and performance
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Selecting fuzzy if-then rules for classification problems using genetic algorithms
IEEE Transactions on Fuzzy Systems
Using interval singleton type 2 fuzzy logic system in corrupted time series modelling
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV
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In this paper, we propose an Adaptive Neuro-Fuzzy Network (ANFN) to deal with forecasting problems. The ANFN model is inherently a modified Takagi-Sugeno-Kang-type fuzzy-rule-based model possessing a neural network's learning ability. We propose a hybrid learning algorithm which combines the Genetic Algorithm (GA) and the Least-Squares Estimate (LSE) method to construct the ANFN model. The GA is used to tune membership functions at the precondition part of Juzzy rules, while the LSE method is used to tune parameters at the consequent part of fuzzy rules. Simulations demonstrate that the proposed ANFN model has a good predictive capability.