Neural-Network-Based Fuzzy Logic Control and Decision System
IEEE Transactions on Computers - Special issue on artificial neural networks
On the principles of fuzzy neural networks
Fuzzy Sets and Systems
Neural Networks
Fuzzy engineering
A course in fuzzy systems and control
A course in fuzzy systems and control
Universal approximation by hierarchical fuzzy systems
Fuzzy Sets and Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Nonsingleton fuzzy logic systems: theory and application
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
A highly interpretable form of Sugeno inference systems
IEEE Transactions on Fuzzy Systems
On multistage fuzzy neural network modeling
IEEE Transactions on Fuzzy Systems
Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement
IEEE Transactions on Fuzzy Systems
A note on smooth approximation capabilities of fuzzy systems
IEEE Transactions on Fuzzy Systems
Cascaded fuzzy neural network model based on syllogistic fuzzy reasoning
IEEE Transactions on Fuzzy Systems
Linguistic modeling by hierarchical systems of linguistic rules
IEEE Transactions on Fuzzy Systems
Fuzzy basis functions, universal approximation, and orthogonal least-squares learning
IEEE Transactions on Neural Networks
Modelling plant control strategies and their applications into a knowledge-based system
Applied Soft Computing
Zero-order TS fuzzy model to predict hydro turbine speed in closed loop operation
Applied Soft Computing
| Hi-index | 0.00 |
When applying fuzzy systems for data analysis, their approximation and interpretation capabilities are two important aspects. Cascaded fuzzy system (CFS) is a new special class of hierarchical fuzzy systems in architectures proposed by Duan and Chung [IEEE Trans. Fuzzy Syst. 9 (2) (2001) 293] but its universal approximation capability is still not proved. When CFS is utilized in fuzzy data analysis/modeling, it seems very difficult to give a reasonable interpretation for intermediate variables and the corresponding fuzzy rules. A new cascaded centralized TSK fuzzy system (CCTSKFS) is presented in this paper, whose universal approximation capability is proved in detail, and what's more, we can interpret CCTSKFS more rationally. Finally, our experimental results demonstrate that CCTSKFS outperforms the classical cascaded TSK fuzzy system (CTSKFS) in approximation capability and robustness.