This paper presents a modeling scheme for nonlinear black-box systems based on Universal Learning Networks (ULN). The ULN, a superset of all kinds of neural networks, consists of two kinds of elements: nodes and branches corresponding to equations and their relations in traditional description of dynamic systems. Following the idea of ULN, a nonlinear black-box system is first represented by a set of related unknown equations, and then treated as the ULN with nodes and branches. Each unknown node function in the ULN is re-parameterized by using an adaptive fuzzy model. One of distinctive features of the black-box model constructed in this way is that it can incorporate prior knowledge obtained from input-output data into its modeling and thus its parameters to be trained have explicit meanings useful for estimation and application.
|出版ステータス||Published - 1998 1月 1|
|イベント||Proceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3) - Anchorage, AK, USA|
継続期間: 1998 5月 4 → 1998 5月 9
|Other||Proceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3)|
|City||Anchorage, AK, USA|
|Period||98/5/4 → 98/5/9|
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