Neural-Network-Based Fuzzy Logic Control and Decision System
IEEE Transactions on Computers - Special issue on artificial neural networks
Fuzzy logic, neural networks, and soft computing
Communications of the ACM
Fuzzy neural networks: a survey
Fuzzy Sets and Systems
Structure optimization of fuzzy neural network by genetic algorithm
Fuzzy Sets and Systems - Special issue on fuzzy neural control
A learning algorithm of fuzzy neural networks with triangular fuzzy weights
Fuzzy Sets and Systems - Special issue on fuzzy neural control
Fuzzy Sets and Systems - Special issue on fuzzy neural control
Manufacturing process control through integration of neural networks and fuzzy model
Fuzzy Sets and Systems
LANDMARC: indoor location sensing using active RFID
Wireless Networks - Special issue: Pervasive computing and communications
Application areas of AIS: The past, the present and the future
Applied Soft Computing
A novel approach for ANFIS modelling based on full factorial design
Applied Soft Computing
Identification using ANFIS with intelligent hybrid stable learning algorithm approaches
Neural Computing and Applications
Information Sciences: an International Journal
Neural networks that learn from fuzzy if-then rules
IEEE Transactions on Fuzzy Systems
A neural fuzzy system with linguistic teaching signals
IEEE Transactions on Fuzzy Systems
Neuro-fuzzy rule generation: survey in soft computing framework
IEEE Transactions on Neural Networks
Computers and Industrial Engineering
Hybrid adaptive wavelet-neuro-fuzzy system for chaotic time series identification
Information Sciences: an International Journal
Information Sciences: an International Journal
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Because of the advantages of radio frequency identification (RFID), this study uses an integrated optimization artificial immune network (Opt-aiNET) and a particle swarm optimization (PSO)-based fuzzy neural network (IOAP-FNN) to determine the relationship between the RFID signals and the position of a picking cart for an RFID-based positioning system. The results for the three benchmark functions indicate that the proposed IOAP-FNN performs better than the other algorithms. In addition, model evaluation results also demonstrate that the proposed algorithm really can predict the picking cart's position more accurately. Moreover, unlike artificial neural networks, the proposed approach allows much easier interpretation of the training results, since they are in the form of fuzzy IF-THEN rules.