Learning Neural Networks With Noisy Inputs Using The Errors-in-Variables Approach  

Oleh:

Gorp, J. Van ; Schoukens, J. ; Pintelon, R.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 11 no. 2 (2000), page 402-414.
Topik: approach; learning; neural networks; noisy inputs; errors - in - variables; approach

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.4 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

Currently, most learning algorithms for neural-network modeling are based on the output error approach, using a least squares cost function. This method provides good results when the network is trained with noisy output data and known inputs. Special care must be taken, however, when training the network with noisy input data, or when both inputs and outputs contain noise. This paper proposes a novel cost function for learning NN with noisy inputs, based on the errors - in - variables stochastic framework. A learning scheme is presented and examples are given demonstrating the improved performance in neural - network curve fitting, at the cost of increased computation time.

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