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Lu, Y., Roychowdhury, V. and Vanderberghe, L. (2007) Distributed Parallel Support Vector Machines in Strongly Connected Networks. Neural Networks. A Publication of the IEEE Computational Intelligence Society.

has been cited by the following article:

• TITLE: Biological Inspiration—Theoretical Framework Mitosis Artificial Neural Networks Unsupervised Algorithm

  AUTHORS: Lácides Pinto Mindiola, Gelvis Melo Freile, Carlos Socarras Bertiz

  KEYWORDS: Mitosis, Artificial Neuron, Node, Structural Analysis, Neural Networks, Output, Layer, Simulation

  JOURNAL NAME: International Journal of Communications, Network and System Sciences, Vol.8 No.9, September 30, 2015

  ABSTRACT: The modified approach to conventional Artificial Neural Networks (ANN) described in this paper represents an essential departure from the conventional techniques of structural analysis. It has four main distinguishing features: 1) it introduces a new simulation algorithm based on the biology; 2) it performs relatively simple arithmetic as massively parallel, during analysis of a structure; 3) it shows that it is possible to use the application of the modified approach to conventional ANN to solve problems of any complexity in the field of structural analysis; 4) the Neural Topologies for Structural Analysis (NTSA) system are recurrent networks and its outputs are connected to its inputs [1] and [2]. In NTSA system the DNA of the neuron mother and daughters would be defined by: 1) the same entry, from the corresponding neuron in the previous layer; 2) the same trend vector; 3) the same transfer function (purelin). The mother’s neuron and her daughter’s neuron differ only in the connection weight and its output signal.