Information Technology Reference
In-Depth Information
[38]
Roubos H, Setnes M (2001) Compact and transparent fuzzy models and classifiers
through iterative complexity reduction, IEEE Trans. on Fuzzy System, 9(4): 516-524
[39]
Setnes M, Babuška R, Kaymark U, et al. , (1998) Similarity measures in fuzzy rule
base simplification, IEEE trans. on SMC., B-28: 376-386
[40]
Setnes M, Roubos JA (2000) GA-fuzzy modelling and classification: complexity and
performance, IEEE Trans. on Fuzzy Systems, 8(5): 509-522
[41]
Takagi and Hayashi (1991) NN-driven fuzzy reasoning, Internat. J. of Approximate
Reasoning, 5(3): 191-212.
[42]
Wang L and Yen J (1999) Extracting fuzzy rules for system modelling using a hybrid
of genetic algorithms and Kalman filter, Fuzzy Sets System, 101: 353-362
[43]
Wang LX (1994) Adaptive fuzzy systems and control: design and stability analysis,
Englewood Cliffs, New Jersey: Prentice Hall.
[44]
Wang LX and Mendel JM (1992a) Fuzzy basis functions, universal approximation,
and orthogonal least squares learning, IEEE Trans. on Neural Network, 3: 807 - 814.
[45]
Wang LX and Mendel JM (1992b) Back-propagation fuzzy system as nonlinear
dynamic system identifiers, Proc. of FUZZ-IEEE, vol. 2: 1409-1418.
[46]
Wang LX and Mendel JM (1992c) Generating fuzzy rules by learning from examples,
IEEE Trans. on SMC, 22(6): 1414-1427.
[47]
Xiaosong D, Popovic D, Schulz-Ekloff G (1995) Oscillation resisting in the learning
of backpropagation neural networks, Proc. of 3rd IFAC/IFIP workshop on algorithm
and architectures for real-time control, Ostend, Belgium.
[48]
Yen J and Wang L (1998) Application of statistical information criteria for optimal
fuzzy model construction, IEEE Trans. on Fuzzy System, 6(3): 362-371.
[49]
Yen J and Wang L (1999) Simplifying fuzzy rule-based models using orthogonal
transformation methods, IEEE Trans. on SMC, 29(1): 13-24.
[50]
Zhang J and Morris AJ (1999) Recurrent neuro-fuzzy networks for nonlinear process
modelling, IEEE Trans. on Neural Networks, 10: 313-326.
Search WWH ::




Custom Search