论文信息 - NNSYSID-Toolbox for System Identification with Neural Networks

NNSYSID-Toolbox for System Identification with Neural Networks

The NNSYSID toolset for System Identification has been developed as an add on to MATLAB®. The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains a number of nonlinear model structures based on neural networks, effective training algorithms and tools for model validation and model structure selection. This paper gives an overview of the design of NNSYSID and demonstrates its features in an example.

[1] M. Norgaard,et al. The NNSYSID toolbox-a MATLAB(R) toolbox for system identification with neural networks , 1996, Proceedings of Joint Conference on Control Applications Intelligent Control and Computer Aided Control System Design.

[2] Lennart Ljung,et al. Nonlinear black-box modeling in system identification: a unified overview , 1995, Autom..

[3] Niels Kjølstad Poulsen,et al. Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner’s Handbook , 2000 .

[4] S. Hyakin,et al. Neural Networks: A Comprehensive Foundation , 1994 .

[5] L. K. Hansen,et al. Pruning with generalization based weight saliencies: λOBD, λOBS , 1995, NIPS 1995.

[6] O. Sørensen. Neural Networks in Control Applications , 1994 .

[7] Tor Arne Johansen,et al. ORBIT – operating-regime-based modeling and identification toolkit , 1997 .

[8] Bernie Mulgrew,et al. IEEE Workshop on Neural Networks for Signal Processing , 1995 .

[9] L. Ljung,et al. Overtraining, Regularization, and Searching for Minimum in Neural Networks , 1992 .

[10] Lars Kai Hansen,et al. Cross validation in LULOO , 1996 .

[11] Babak Hassibi,et al. Second Order Derivatives for Network Pruning: Optimal Brain Surgeon , 1992, NIPS.

[12] Lars Kai Hansen,et al. Generalization performance of regularized neural network models , 1994, Proceedings of IEEE Workshop on Neural Networks for Signal Processing.