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On the dynamical modeling with neural fuzzy networks.
1Dept. of Electr. Eng., Nat. Taiwan Univ. of Sci. and Technol., Taipei, Taiwan.
IEEE Transactions on Neural Networks
|February 5, 2008
Summary
Additive delay feedback neural-fuzzy networks (ADFNFN) offer superior modeling accuracy for dynamical systems compared to existing delay feedback networks. The number of delays in these networks functions similarly to the order in nonlinear autoregressive models.
Area of Science:
- Dynamical Systems Modeling
- Artificial Intelligence
- Fuzzy Systems
Background:
- Delay feedback (recurrent) networks are used for modeling dynamical systems without prior knowledge of system order.
- Existing delay feedback networks have limitations in achieving optimal modeling accuracy.
Purpose of the Study:
- To investigate the performance of existing delay feedback networks.
- To propose and evaluate an improved model: additive delay feedback neural-fuzzy networks (ADFNFN).
Main Methods:
- Comparative simulation study of ADFNFN against existing delay feedback networks.
- Analysis of the role of delays in delay feedback networks versus order in NARX models.
Main Results:
- ADFNFN demonstrated superior modeling accuracy compared to other delay feedback networks.
- Delay feedback networks were shown to achieve accuracy comparable to second-order nonlinear autoregressive with exogenous inputs (NARX) models.
Conclusions:
- ADFNFN represents a significant advancement in modeling accuracy for dynamical systems.
- The number of delays in delay feedback networks is analogous to the order in NARX models, influencing achievable accuracy.
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