Structure identification of fuzzy model
Fuzzy Sets and Systems
Introduction to the theory of neural computation
Introduction to the theory of neural computation
A function estimation approach to sequential learning with neural networks
Neural Computation
Neuro-fuzzy systems for function approximation
Fuzzy Sets and Systems - Special issue on analytical and structural considerations in fuzzy modeling
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Adaptive Control
Foundations of Neuro-Fuzzy Systems
Foundations of Neuro-Fuzzy Systems
Digital Control of Dynamic Systems
Digital Control of Dynamic Systems
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Simplification of fuzzy-neural systems using similarity analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On generating FC3 fuzzy rule systems from data usingevolution strategies
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
Identification of complex systems based on neural and Takagi-Sugeno fuzzy model
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A TSK-type neurofuzzy network approach to system modeling problems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy perceptron neural networks for classifiers with numerical data and linguistic rules as inputs
IEEE Transactions on Fuzzy Systems
A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Neuro-fuzzy rule generation: survey in soft computing framework
IEEE Transactions on Neural Networks
Approximation of nonlinear systems with radial basis function neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Orthogonal least squares learning algorithm for radial basis function networks
IEEE Transactions on Neural Networks
Stability analysis and robustness design of nonlinear systems: An NN-based approach
Applied Soft Computing
Mitigating the obsolescence of specification models of service-based systems
Proceedings of the 2013 International Conference on Software Engineering
Hi-index | 0.00 |
An algorithm for the generation of a TS-type neuro-fuzzy system is presented. There are two stages in the generation: in the first stage, an initial structure adapted from an empty neuron or fuzzy rule set, based on the geometric growth criterion and the @?-completeness of fuzzy rules; in the second stage, the obtained initial structure is refined by a hybrid learning algorithm based on backpropagation and a proposed recursive weight learning algorithm to minimize the system error. The similarity analysis applied throughout the entire learning process attempts both to alleviate overlap among membership functions and to reduce the complexity of the obtained system. Benchmark examples, comparing the proposed algorithm with previous approaches, show the proposed algorithm is more effective in terms of both model accuracy and compactness.