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| 2. |
- Gustafsson, Fredrik, et al.
(author)
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On Simultaneous System and Input Sequence Estimation
- 1992
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In: Proceedings of the 4th IFAC Symposium on Adaptive Systems in Control and Signal Processing. - Linköping : Linköping University. - 9780080425962 ; , s. 93-98
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Conference paper (peer-reviewed)abstract
- Equalization is concerned with estimation of the input sequence of a linear system given noisy measurements of the output signal. In case the system description is unknown we have the problem of blind equalization. A scheme for blind equalization which is based on the assumption that the input signal belongs to a finite alphabet is proposed. A finite impulse response model can be directly estimated by the least-squares method if the input sequence is known. Since we know that the number of possible input sequences is limited, we can associate one system estimate to each possible input sequence. This allows us to determine the a posteriori probability of an input sequence given output observations. The maximum a posteriori (MAP) input sequence estimate is then taken as the most probable input sequence. Sufficient conditions for identifiability of the input signal and the system are given. The complexity of this scheme increases exponentially with time. A recursive approximate MAP estimator of fixed complexity is obtained by, at each time update, only keeping the K most probable input sequences. This method is evaluated on a Rayleigh fading communication channel.
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| 3. |
- Ljung, Lennart, 1946-, et al.
(author)
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A Comment on Leakage in Adaptive Algorithms
- 1992
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In: Proceedings of the 4th IFAC International Symposium on Adaptive Systems in Control and Signal Processing. - Linköping : Linköping University. - 9780080425962 ; , s. 377-382
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Conference paper (peer-reviewed)abstract
- By "leakage" in adaptive control and adaptive signal processing algorithm is understood that a pull term towards a given parameter value is introduced. Leakage has been introduced both as trick to be able to prove certain convergence results as an ad hoc means for obtaining less drifting parameters. Leakage is the same as regularization and we explain what benefits - from an estimation point of view - this gives.
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| 4. |
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| 5. |
- Ljung, Lennart, 1946-, et al.
(author)
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Overtraining, Regularization and Searching for Minimum in Neural Networks
- 1992
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In: 4th IFAC Symposium on Adaptive Systems in Control and Signal Processing. - 9780080425962 ; , s. 669-674
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Conference paper (peer-reviewed)abstract
- Neural network models for dynamical systems have been subject of considerable interest lately. They are often characterized by the fact that they use a fairly large amount of parameters. Here we address the problem why this can be done without the usual penalty in terms of a large variance error. We show that reguralization is a key explanation, and that terminating a gradient search ("backpropagation") before the true criterion minimum is found is a way of achieving regularization. This, among other things, also explains the concept of "overtraining" in neural nets.
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| 6. |
- Nagy, Peter, et al.
(author)
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System Identification using Bond Graphs
- 1992
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In: Proceedings of the 4th IFAC Symposium on Adaptive Systems in Control and Signal Processing. - 9780080425962 ; , s. 415-420
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Conference paper (peer-reviewed)
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