R/D activities on MMICs in Europe

Rumelhard, Christian

doi:10.1109/issse.1995.497927

Cited by 1 publication

(1 citation statement)

References 16 publications

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“…The three-layer neural network can be trained easily by any appropriate algorithm, e.g. backpropagation (Werbos, 1977; Rumelhard et al, 1986). Here we use the conjugate gradient learning method (Leonard & Kramer, 1990;Fletcher & Powell, 1963) to train the neural network because of the two nice features of this method: (1) the learning speed of the conjugate method is much faster than that of general backpropagation; and (2) the learning rate constants are calculated automatically and adaptively so that they do not need to be specified before training.…”

Section: Neural Network Implementationmentioning

confidence: 99%

Nonlinear principal component analysis-based on principal curves and neural networks

Dong

McAvoy

Proceedings of 1994 American Control Conference - ACC '94

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Many applications of principal component analysis (PCA) can be found in recently published papers. But principal component analysis is a linear method, and most engineering problems are nonlinear. Sometimes using the linear PCA method in nonlinear problems can bring distorted and misleading results. So there is a need for a nonlinear principal component analysis (NLPCA) method. The principal curve algorithm was a breakthrough of solving the NLPCA problem, but the algorithm does not yield an NLPCA model which can be used for predictions. In this paper we present an NLPCA method which integrates the principal curve algorithm and neural networks. The results on both simulated and real problems show that the method is excellent for solving nonlinear principal component problems. Potential applications of NLPCA are also discussed in this paper.

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Section: Neural Network Implementationmentioning

confidence: 99%

Nonlinear principal component analysis-based on principal curves and neural networks

Dong

McAvoy

Proceedings of 1994 American Control Conference - ACC '94

View full text Add to dashboard Cite

show abstract

R/D activities on MMICs in Europe

Cited by 1 publication

References 16 publications

Nonlinear principal component analysis-based on principal curves and neural networks

Nonlinear principal component analysis-based on principal curves and neural networks

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