Neural networks in designing fuzzy systems for real world applications
Fuzzy Sets and Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
A neuro-fuzzy method to learn fuzzy classification rules from data
Fuzzy Sets and Systems - Special issue: application of neuro-fuzzy systems
A neural network classifier with disjunctive fuzzy information
Neural Networks
A new method for constructing membership functions and fuzzy rulesfrom training examples
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An efficient fuzzy classifier with feature selection based on fuzzyentropy
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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
FuGeNeSys-a fuzzy genetic neural system for fuzzy modeling
IEEE Transactions on Fuzzy Systems
Self-adaptive neuro-fuzzy inference systems for classification applications
IEEE Transactions on Fuzzy Systems
Neural-network feature selector
IEEE Transactions on Neural Networks
Quantum neural networks (QNNs): inherently fuzzy feedforward neural networks
IEEE Transactions on Neural Networks
Subsethood-product fuzzy neural inference system (SuPFuNIS)
IEEE Transactions on Neural Networks
Fuzzy min-max neural networks. I. Classification
IEEE Transactions on Neural Networks
Knowledge and intelligent computing system in medicine
Computers in Biology and Medicine
Development of quantum-based adaptive neuro-fuzzy networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Construction of a neuron-fuzzy classification model based on feature-extraction approach
Expert Systems with Applications: An International Journal
Deep adaptive networks for image classification
ICIMCS '10 Proceedings of the Second International Conference on Internet Multimedia Computing and Service
A novel approach to classificatory problem using neuro-fuzzy architecture
International Journal of Systems, Control and Communications
A fuzzy intelligent approach to the classification problem in gene expression data analysis
Knowledge-Based Systems
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In this paper, an entropy-based quantum neuro-fuzzy inference system (EQNFIS) for classification applications is proposed. The EQNFIS model is a five-layer structure, which combines the traditional Takagi-Sugeno-Kang (TSK). Layer 2 of the EQNFIS model contains quantum membership functions, which are multilevel activation functions. Each quantum membership function is composed of the sum of sigmoid functions shifted by quantum intervals. A self-constructing learning algorithm, which consists of the self-clustering algorithm (SCA), quantum fuzzy entropy, and the backpropagation algorithm, is also proposed. The proposed SCA method is a fast, one-pass algorithm that dynamically estimates the number of clusters in an input data space. Quantum fuzzy entropy is employed to evaluate the information on pattern distribution in the pattern space. With this information, we can determine the number of quantum levels. The backpropagation algorithm is used to tune the adjustable parameters. Simulations were conducted to show the performance and applicability of the proposed model.