We have recently shown that the widely known LMS algorithm is an H OO optimal estimator. The H OO criterion has been introduced, initially in the control theory ...

We have recently shown that the widely known LMS algorithm is an H OO optimal estimator. The H OO criterion has been introduced,.

We have recently shown that the widely known LMS algorithm is an H∞ optimal estimator. The H∞ criterion has been introduced, initially in the control theory ...

This paper introduces two model-based noise canceling techniques, that is, using the Moving Average (MA) model and a feedforward Neural Network (NN) to estimate ...

We have recently shown that the widely known LMS algorithm is an H∞ optimal estimator. The H∞ criterion has been introduced, initially in the control ...

Dec 19, 2017 · Type. book part or chapter ; Publication date. 1994 ; Published in. Advances in Neural Information Processing Systems ; Start page. 351 ; End page.

H-infinity optimality criteria for LMS and backpropagation, in Advances in Neural Information Processing Systems, Vol 6, J.D. Cowan, G. Tesauro and J ...

Hoo optimality criteria for LMS and backpropagation. Post navigation. Previous Article Hoo filtering is just Kalman filtering in Krein space.

The optimal LMS rule is based on an online calculation of the learning rate based on the minimum variance criteria. Then, using this rule, the neuron adaptively ...

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