S. Lawrence, C. L. Giles and A. C. Tsoi, “Lessons in Neural Network Training: Overfitting May Be Harder than Expected,” Proceedings of the 14th National Conference on Artificial Intelligence, AAAI-97, AAAI Press, Menlo Park, California, 1997, pp. 540-545.

has been cited by the following article:

• TITLE: Fast Fading Channel Neural Equalization Using Levenberg-Marquardt Training Algorithm and Pulse Shaping Filters

  AUTHORS: Tiago Mota, Jorgean Leal, Antônio Lima

  KEYWORDS: Decision Feedback Equalizers; Levenberg-Marquardt Algorithm; Pulse Shaping; Recurrent Neural Networks

  JOURNAL NAME: International Journal of Communications, Network and System Sciences, Vol.7 No.2, February 8, 2014

  ABSTRACT: Artificial Neural Network (ANN) equalizers have been successfully applied to mitigate Inter symbolic Interference (ISI) due to distortions introduced by linear or nonlinear communication channels. The ANN architecture is chosen according to the type of ISI produced by fixed, fast or slow fading channels. In this work, we propose a combination of two techniques in order to minimize ISI yield by fast fading channels, i.e., pulse shape filtering and ANN equalizer. Levenberg-Marquardt algorithm is used to update the synaptic weights of an ANN comprise only by two recurrent perceptrons. The proposed system outperformed more complex structures such as those based on Kalman filtering approach.