Data processing: artificial intelligence – Neural network – Learning method
Patent
1995-05-02
2000-10-17
Alam, Hosain T.
Data processing: artificial intelligence
Neural network
Learning method
706 16, G06F 1518
Patent
active
061345385
ABSTRACT:
A neural network is provided for equalizing distorted data signals. The data signal to be equalized is coupled via time-delay elements to a group of networks for weighting. The output signals of the networks for weighting are coupled to the input terminals of a plurality of neurons whose outputs are coupled, via a respective amplifier, to input terminals of a further neuron having an output terminals where the equalized data signal can be tapped.
REFERENCES:
patent: 5272656 (1993-12-01), Genereux
patent: 5272723 (1993-12-01), Kimoto et al.
patent: 5274744 (1993-12-01), Yu et al.
patent: 5283746 (1994-02-01), Cummings et al.
patent: 5361327 (1994-11-01), Takahashi
patent: 5467428 (1995-11-01), Ulug
patent: 5524124 (1996-06-01), Koenig
Kechriotis et al., Using Recurrent Neural Networks for adaptive communication channel equalization, IEEE Transaction on Neural Networks vol. 5, lss. 2, pp. 267-278, Mar. 1994.
Al-Mashouq et al., The use of neural nets to combine equalization with decoding, ICASSP-93, pp. 469-472, Apr. 30, 1993.
Wang et al., A novel approach to the speaker identification over telephone networks, ICASSP-93, pp. 407-410, Apr. 30, 1993.
Valdovinos et al., Neural Network techniques applied to channel equalization in indoor radio enviornment, Proceedings: 7th Mediterranean electrotechnical conference, pp. 1139-1142, Apr. 14, 1994.
Peng et al., Performance improvement of neural network equalizers, Signals, systems and computers, 1993 27th Asilomar conference, pp. 396-400, Nov. 3, 1993.
Cid-Seuiro et al., Recurrent radial basis function networks for optimal blind equalization, proceedings of the 1993 IEEE-SP workshop, pp. 562-571, Sep. 9, 1993.
Gibson et al., The application of non-linear structures to the reconstruction of binary signals, IEEE transactions on signal processing, pp. 1877-1884, Aug. 1991.
Kimoto et al., Inverse modeling of dynamical system-network architecture with identification network and adaptation network, 1991 IEEE IJCNN, pp. 566-571, Nov. 21, 1991.
Lo et al., Neural Network channel equalization, IJCNN 1992, pp. 981-986, Jun. 11, 1992.
P. Lee; Neural Net Equalization for a Magnetic Recording Channel IEEE Computer Society Press, Nov. 1, 1993, pp. 369-374.
Nair et al.; Nonlinear Equalization for Data Storage Channels, IEEE Communications Socity, May 1, 1994, pp. 250-254.
G. Gibson et al.; Multilayer Perceptron Structures Applied to Adaptive Equalisers for Data Communications, IEEE, May 23, 1989 pp. 1183-1186.
L. Holmstrom; Using Additive Noise in Back-Propagation Training, IEEE Transactions on Eural Networks, Jan. 1, 1992, pp. 24-38.
M. Caudill; Neural Network Training Tips and Techniques; A1 Expert, Jan. 1, 1991, pp. 56-61.
K. Mager; Einsatz Neuronaler Netze Zur Tekonstruktion von Verzerrten Digitalsignalen, Fernseh-Und Kino-Technik, Aug. 1994, pp. 357-363.
Search Report, EPO, Mar. 13, 1996.
Mager Klaus
Wursthorn Edgar
Alam Hosain T.
Shah Sanjiv
Shedd Robert D.
Tripoli Joseph S.
Wein Frederick A.
LandOfFree
Procedure for equalizing distorted data signals does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Procedure for equalizing distorted data signals, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Procedure for equalizing distorted data signals will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-478633