Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Informatics and computer science intelligent systems applications
Stable Fourier neural networks with application to modeling lettuce growth
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
On-line modeling via fuzzy support vector machines and neural networks
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Recent Advances in Soft Computing: Theories and Applications
| Hi-index | 35.68 |
It is known that many discrete-time recurrent neural networks, such as e.g., neural state space models, multilayer Hopfield networks, and locally recurrent globally feedforward neural networks, can be represented as NLq systems. Sufficient conditions for global asymptotic stability and input/output stability of NLq systems are available, including three types of criteria: (1) diagonal scaling; (2) criteria depending on diagonal dominance; (3) condition number factors of certain matrices. The paper discusses how Narendra's (1990, 1991) dynamic backpropagation procedure, which is used for identifying recurrent neural networks from I/O measurements, can be modified with an NLq stability constraint in order to ensure globally asymptotically stable identified models. An example illustrates how system identification of an internally stable model corrupted by process noise may lead to unwanted limit cycle behavior and how this problem can be avoided by adding the stability constraint