A multilayer feedforward neural network having N/4 nodes in two hidden layers

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2 Citations (Scopus)

Abstract

In order to reduce the complexity of a single hidden layered multilayer neural network, a new two hidden layered MFNN (THL-MFNN) with a combined structure of a RBFN and MLPs is proposed, and its associated training method is discussed. The proposed THL-MFNN can be easily constructed, and can be efficiently trained by online recursive methods. The performance of the proposed THL-MFNN with P/4+2 = 18 hidden nodes and 34 weights is equal to that of an optimaum Bayesian equalizer using an RBFN with P = 64 hidden nodes and 64 weights. The role of each layer in the proposed THL-MFNN is presented by theoretical approach, and the feasibility of a more reduced structure is given.

Original languageEnglish
Pages1675-1680
Number of pages6
Publication statusPublished - 2001 Jan 1
EventInternational Joint Conference on Neural Networks (IJCNN'01) - Washington, DC, United States
Duration: 2001 Jul 152001 Jul 19

Other

OtherInternational Joint Conference on Neural Networks (IJCNN'01)
CountryUnited States
CityWashington, DC
Period01/7/1501/7/19

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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  • Cite this

    Choi, S., Ko, K., & Hong, D. (2001). A multilayer feedforward neural network having N/4 nodes in two hidden layers. 1675-1680. Paper presented at International Joint Conference on Neural Networks (IJCNN'01), Washington, DC, United States.