On the concept of possibility-probability consistency
Fuzzy Sets and Systems
Fuzzy sets in approximate reasoning, part 1: inference with possibility distributions
Fuzzy Sets and Systems - Special memorial volume on foundations of fuzzy reasoning
Fuzzy engineering
Extracting rules from neural networks by pruning and hidden-unit splitting
Neural Computation
Unsupervised Bayesian visualization of high-dimensional data
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
FERNN: An Algorithm for Fast Extraction of Rules fromNeural Networks
Applied Intelligence
Supervised fuzzy clustering for the identification of fuzzy classifiers
Pattern Recognition Letters
A k-order fuzzy OR operator for pattern classification with k -order ambiguity rejection
Fuzzy Sets and Systems
Chaotic Time Series Prediction Using a Neuro-Fuzzy System with Time-Delay Coordinates
IEEE Transactions on Knowledge and Data Engineering
A fuzzy neural network with fuzzy impact grades
Neurocomputing
Type-2 fuzzy sets and systems: an overview
IEEE Computational Intelligence Magazine
Fuzzy classifications using fuzzy inference networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Dynamic fuzzy neural networks-a novel approach to functionapproximation
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
What does a probabilistic interpretation of fuzzy sets mean?
IEEE Transactions on Fuzzy Systems
Decision making with fuzzy probability assessments
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
IEEE Transactions on Fuzzy Systems
Data-driven linguistic modeling using relational fuzzy rules
IEEE Transactions on Fuzzy Systems
A neuro-fuzzy system modeling with self-constructing rule generationand hybrid SVD-based learning
IEEE Transactions on Fuzzy Systems
A probabilistic fuzzy logic system for modeling and control
IEEE Transactions on Fuzzy Systems
Soft transition from probabilistic to possibilistic fuzzy clustering
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
A Fuzzy Probabilistic Approach for Determining Safety Integrity Level
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
Unity and diversity of fuzziness-from a probability viewpoint
IEEE Transactions on Fuzzy Systems
Neuro-fuzzy rule generation: survey in soft computing framework
IEEE Transactions on Neural Networks
Interpretation of artificial neural networks by means of fuzzy rules
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
An Experimental Study on Nonlinear Function Computation for Neural/Fuzzy Hardware Design
IEEE Transactions on Neural Networks
A Fuzzy Min-Max Neural Network Classifier With Compensatory Neuron Architecture
IEEE Transactions on Neural Networks
Support Vector Echo-State Machine for Chaotic Time-Series Prediction
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Self-Organizing Adaptive Fuzzy Neural Control for a Class of Nonlinear Systems
IEEE Transactions on Neural Networks
Generation of a probabilistic fuzzy rule base by learning from examples
Information Sciences: an International Journal
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Recently, the study of incorporating probability theory and fuzzy logic has received much interest. To endow the traditional fuzzy rule-based systems (FRBs) with probabilistic features to handle randomness, this paper presents a probabilistic fuzzy neural network (ProFNN) by introducing the probability of input linguistic terms and providing linguistic meaning into the connectionist architecture. ProFNN integrates the probabilistic information of fuzzy rules into the antecedent parts and quantifies the impacts of the rules on the consequent parts using mutual subsethood, which work in conjunction with volume defuzzification in a gradient descent learning frame work. Despite the increase in the number of parameters, ProFNN provides a promising solution to deal with randomness and fuzziness in a single frame. To evaluate the performance and applicability of the proposed approach, ProFNN is carried out on various benchmarking problems and compared with other existing models with a performance better than most of them.