Interval valued fuzzy sets based on normal forms
Fuzzy Sets and Systems
Structure identification of fuzzy model
Fuzzy Sets and Systems
Cortical computational maps for auditory imaging
Neural Networks
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
Construction of fuzzy inference rules by NDF and NDFL
International Journal of Approximate Reasoning - Special issue on fuzzy logic and neural networks for pattern recognition and control
A review and comparison of six reasoning methods
Fuzzy Sets and Systems
Fuzzy logic, neural networks, and soft computing
Communications of the ACM
Fuzzy neural networks: a survey
Fuzzy Sets and Systems
Fuzzy Sets and Systems - Special issue on fuzzy neural control
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
The nature of statistical learning theory
The nature of statistical learning theory
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Fuzzy logic: intelligence, control, and information
Fuzzy logic: intelligence, control, and information
An experiment in linguistic synthesis with a fuzzy logic controller
International Journal of Human-Computer Studies - Special issue: 1969-1999, the 30th anniversary
Expert Systems with Applications: An International Journal
IEEE Transactions on Neural Networks
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
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On fuzzy associative memory with multiple-rule storage capacity
IEEE Transactions on Fuzzy Systems
Designing fuzzy inference systems from data: An interpretability-oriented review
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Implicative Fuzzy Associative Memories
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
On neurobiological, neuro-fuzzy, machine learning, and statistical pattern recognition techniques
IEEE Transactions on Neural Networks
GenSoFNN: a generic self-organizing fuzzy neural network
IEEE Transactions on Neural Networks
Stock Trading Using RSPOP: A Novel Rough Set-Based Neuro-Fuzzy Approach
IEEE Transactions on Neural Networks
FCMAC-Yager: A Novel Yager-Inference-Scheme-Based Fuzzy CMAC
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
A BCM theory of meta-plasticity for online self-reorganizing fuzzy-associative learning
IEEE Transactions on Neural Networks
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
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The fuzzy associative conjuncted maps (FASCOM) is a fuzzy neural network that associates data of nonlinearly related inputs and outputs. In the network, each input or output dimension is represented by a feature map that is partitioned into fuzzy or crisp sets. These fuzzy sets are then conjuncted to form antecedents and consequences, which are subsequently associated to form IF-THEN rules. The associative memory is encoded through an offline batch mode learning process consisting of three consecutive phases. The initial unsupervised membership function initialization phase takes inspiration from the organization of sensory maps in our brains by allocating membership functions based on uniform information density. Next, supervised Hebbian learning encodes synaptic weights between input and output nodes. Finally, a supervised error reduction phase fine-tunes the network, which allows for the discovery of the varying levels of influence of each input dimension across an output feature space in the encoded memory. In the series of experiments, we show that each phase in the learning process contributes significantly to the final accuracy of prediction. Further experiments using both toy problems and real-world data demonstrate significant superiority in terms of accuracy of nonlinear estimation when benchmarked against other prominent architectures and exhibit the network's suitability to perform analysis and prediction on real-world applications, such as traffic density prediction as shown in this paper.