Combining linear equalization and self-organizing adaptation in dynamic discrete-signal detection

Kohonen, Teuvo; Raivio, Kimmo; Simula, Olli; Venta, O.; Henriksson, J.

doi:10.1109/ijcnn.1990.137573

Cited by 35 publications

(21 citation statements)

References 3 publications

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“…Since then, many authors have done a lot of research into its application and theory. It has demonstrated its efficiency in various engineering problems (Kohonen, Raivio, Simula, Venta, & Henriksson, 1990), including clustering, pattern recognition, the reduction of dimensions and the extraction of features. In addition, SOM has been applied in the visualization of complex processes and systems and discovery dependencies and abstractions from raw data in semiconductor manufacturing.…”

Section: Unsupervised Neural Network Sommentioning

confidence: 99%

Defect spatial pattern recognition using a hybrid SOM–SVM approach in semiconductor manufacturing

Huang

2009

Expert Systems with Applications

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Section: Unsupervised Neural Network Sommentioning

confidence: 99%

Defect spatial pattern recognition using a hybrid SOM–SVM approach in semiconductor manufacturing

Huang

2009

Expert Systems with Applications

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“…Detection of nonlinearly distorted, multi-path propagated signal is the major problem of all telecommunication systems [1][2][3][4][5]. Multi-path propagation introduces inter-symbol interference.…”

Section: Introductionmentioning

confidence: 99%

A neural approach for overall improvement of OFDM system

Rathkanthiwar

Dethe

Kulat

2011

International Conference on Sustainable Energy and Intelligent Systems (SEISCON 2011)

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This paper presents a neural network based, high power amplifi er (HPA) characteristics independent adaptive method for compensation of nonlinear distortions for orthogonal frequency division multiplexing signals. Major elements that contribute to multipath fading effects are also considered and to account for major degradation categories, Rayleigh and Rician channel models are considered in an OFDM system. System performance is tested for OFDM system with and without neural block and it is proved through spectral regrowth as well as bit error rate (BER) plots that the proposed model brings in overall improvement in OFDM system.

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“…routing, admission control, switching and mobile communications. Applications in the following areas will not be described in detail in this paper: coding theory [1][2][3][4][5][6], data transmission [7][8][9][10] and signal processing related to telecommunications [11][12][13][14][15][16][17]. We first introduce some basic neural network structures, i.e.…”

Section: Introductionmentioning

confidence: 99%