Fuzzy neural networks: a survey
Fuzzy Sets and Systems
Fuzzy Sets and Systems - Special issue on fuzzy neural control
On-line learning in neural networks
On-line learning in neural networks
Effective diagnosis of heart disease through neural networks ensembles
Expert Systems with Applications: An International Journal
MED '09 Proceedings of the 2009 17th Mediterranean Conference on Control and Automation
Designing the Self-Adaptive Fuzzy Neural Networks
IJCBS '09 Proceedings of the 2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing
A recurrent self-evolving interval type-2 fuzzy neural network for dynamic system processing
IEEE Transactions on Fuzzy Systems
The Implementation of Fuzzy RBF Neural Network on Indoor Location
KESE '09 Proceedings of the 2009 Pacific-Asia Conference on Knowledge Engineering and Software Engineering
A Neuro-Fuzzy Inference System Through Integration of Fuzzy Logic and Extreme Learning Machines
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy polynomial neural networks: hybrid architectures of fuzzy modeling
IEEE Transactions on Fuzzy Systems
Heterogeneous fuzzy logic networks: fundamentals and development studies
IEEE Transactions on Neural Networks
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Computational intelligence combines fuzzy systems, neural network and evolutionary computing. In this paper, architecture of a neuro-fuzzy integrated system is presented. A new kind of error backpropagation algorithm to adjust the membership functions of each variable and optimise fuzzy rules is developed. To minimise the output error, a variational method for determining globally optimal learning parameters and learning rules for online gradient descent training of multilayer neural network has been proposed. In order to show the effectiveness of the proposed system, simulation for different variety of domain has been performed. The controller for inverted pendulum has been demonstrated. The controller uses error backpropagation algorithm to adjust the membership functions of each variable, optimise fuzzy rules, and identify the inverted pendulum. Neuro-fuzzy integrated system for coronary heart disease has also been simulated. The results suggest that this kind of hybrid system is also suitable for the identification of patients with high/low cardiac risk.