A Simplification of The Backpropagation - Through - Time Algorithm For Optimal Neuro Control  

Oleh:

Bersini, H. ; Gorrini, V.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 8 no. 2 (1997), page 437-441.
Topik: Neuro fuzzy; backpropagation; time algorithm; neuro control

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.2 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

Backpropagation - through - time (BPTT) is the temporal extension of backpropagation which allows a multilayer neural network to approximate an optimal state - feedback control law provided some prior knowledge (Jacobian matrices) of the process is available. In this paper, a simplified version of the BPTT algorithm is proposed which more closely respects the principle of optimality of dynamic programming. Besides being simpler, the new algorithm is less time - consuming and allows in some cases the discovery of better control laws. A formal justification of this simplification is attempted by mixing the Lagrangian calculus underlying BPTT with Bellman - Hamilton - Jacobi equations. The improvements due to this simplification are illustrated by two optimal control problems : the rendezvous and the bioreactor.

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