Design of Neural Networks for Fast Convergence and Accuracy : Dynamics and Control  

Oleh:

Maghami, P. G.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 11 no. 1 (2000), page 113-123.
Topik: accuracy; design; neural networks; accuracy

Ketersediaan

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

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Isi artikel

A procedure for the design and training of artificial neural networks, used for rapid and efficient controls and dynamics design and analysis for flexible space systems, has been developed. Artificial neural networks are employed, such that once property trained, they provide a means of evaluating the impact of design changes rapidly. Specifically, two - layer feedforward neural networks are designed to approximate the functional relationship between the component / spacecraft design changes and measures of its performance or nonlinear dynamics of the system/components. A training algorithm, based on statistical sampling theory is presented, which guarantees that the trained networks provide a designer - specified degree of accuracy in mapping the functional relationship. Within each iteration of this statistical - based algorithm, a sequential design algorithm is used for the design and training of the feedforward network to provide rapid convergence to the network goals. Here, at each sequence a new network is trained to minimize the error of previous network. The proposed method should work for applications wherein an arbitrary large source of training data can be generated. Two numerical examples are performed on a spacecraft application in order to demonstrate the feasibility of the proposed approach.

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