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Y.-P. Liu, M.-G. Wu and J.-X. Qian, “Evolving neural networks using the hybrid of ant colony optimization and BP algorithms,” Lecture Notes in Computer Science, vol. 3971, 2006.

has been cited by the following article:

• TITLE: Evolutionary MPNN for Channel Equalization

  AUTHORS: Archana Sarangi, Bijay Ketan Panigrahi, Siba Prasada Panigrahi

  KEYWORDS: Channel Equalization, Probabilistic Neural Network, Bacteria Foraging, Ant Colony Optimization

  JOURNAL NAME: Journal of Signal and Information Processing, Vol.2 No.1, February 23, 2011

  ABSTRACT: This paper proposes a novel equalizer, termed here as Evolutionary MPNN, where a complex modified probabilistic Neural Networks (MPNN) acts as a filter for the detected signal pattern. The neurons were embedded with optimization algorithms. We have considered two optimization algorithms, Bacteria Foraging Optimization (BFO) and Ant Colony Optimization (ACO). The proposed structure have the ability to process complex signals also can perform for slowly varying channels. Also, Simulation results prove the superior performance of the proposed equalizer over the existing MPNN equalizers.