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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Harris, C.J. Hong, X. |
| Copyright Year | 2001 |
| Description | Author affiliation: Dept. of Electron. & Comput. Sci., Southampton Univ., UK (Harris, C.J.) |
| Abstract | Model based methods for state estimation and control of linear systems is well established and applied. In practice the systems are non-linear, stochastic, temporal and only partially known. An alternative approach based upon empirical data based methods, which incorporate prior knowledge utilising linear additive 'non-linear' models based upon neurofuzzy algorithms are introduced. For control and tracking, there is a surfeit of techniques, which could be applicable to nonlinear problems if appropriate linearisation is achieved; here various forms of local neurofuzzy networks are discussed via a class of adaptive neurofuzzy networks. It is shown that they have good approximation, convergence and stability properties, as well as parametric parsimony making them ideal in control and tracking. It is then shown how these algorithms have been successfully applied to collision avoidance problems in cars, helicopters and ships. |
| Sponsorship | Int. Fuzzy Syst. Assoc. |
| Starting Page | 517 |
| Ending Page | 522 |
| File Size | 448599 |
| Page Count | 6 |
| File Format | |
| ISBN | 0780370783 |
| DOI | 10.1109/NAFIPS.2001.944306 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2001-07-25 |
| Publisher Place | Canada |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Collision avoidance State estimation Control system synthesis Linear systems Stochastic systems Adaptive systems Programmable control Adaptive control Convergence Stability |
| Content Type | Text |
| Resource Type | Article |
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