Trajectory tracking with dynamic neural networks
1997 IEEE International Symposium on Intelligent Control, İstanbul, Türkiye, 16 - 18 Temmuz 1997, ss.173-180, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/isic.1997.626448
- Basıldığı Şehir: İstanbul
- Basıldığı Ülke: Türkiye
- Sayfa Sayıları: ss.173-180
- Kocaeli Üniversitesi Adresli: Hayır
Özet
The application of artificial neural networks to dynamical systems has been constrained by the non-dynamical nature popular network architectures. Many of-the difficulties that ensue-large network sizes, long training times, the need to predetermine buffer lengths- can be overcomed with dynamic neural networks. The minimization of a quadratic performance index is considered for trajectory tracking or process simulation applications. Two approaches for gradient computation are discussed: forward and adjoint sensitivity analysis. The computational complexity of the latter is significantly less, but it requires a backward integration capability. We also discuss two parameter updating methods: gradient descent and a Levenberg-Marquardt approach.