A Training Rule Which Guarantees Finite-Region Stability for A Class of Closed-Loop Neural-Network Control Systems  

Oleh:

Kuntanapreeda, S. ; Fullmer, R. R.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 7 no. 3 (1996), page 745-751.
Topik: neural network; training rule; guarantees; finite - region; stability; closed - loop; neural network; control systems

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.1 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

A training method for a class of neural network controllers is presented which guarantees closed - loop system stability. The controllers are assumed to be nonlinear, feedforward, sampled - data, full - state regulators implemented as single hidden - layer neural networks. The controlled systems must be locally hermitian and observable. Stability of the closed - loop system is demonstrated by determining a Lyapunov function, which can be used to identify a finite stability region about the regulator point.

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