Hybrid neural network approach in description and prediction of dynamic behavior of chaotic chemical reaction systems

被引：5
|
作者：
Kim, HJ [1 ]
Chang, KS [1 ]
机构：
[1] Pohang Univ Sci & Technol, Dept Chem Engn, Pohang 790784, South Korea
来源：
KOREAN JOURNAL OF CHEMICAL ENGINEERING | 2000年 / 17卷 / 06期
关键词：
chaos; hybrid neural network; modified error back propagation; system identification; validation;
D O I：
10.1007/BF02699120
中图分类号：
O6 [化学];
学科分类号：
0703 ;
摘要：
A chaotic system with available prior knowledge is identified with both the sequential hybrid neural network and the standard Artificial Neural Network (ANN). The identified models are validated with phase portrait, return map, the largest Lyapunov exponent and correlation dimension instead of using Sum of Square Errors (SSE). Interpolation and Extrapolation capability of the models ate compared. This is demonstrated for nonisothermal, irreversible, first-order, series reaction A-->B-->C in a CSTR.
引用
收藏
页码：696 / 703
页数：8
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