RBF Network Based on Artificial Immune Algorithm for Regional Head Conductivity Estimation

Author

Dong, Guoya ; Zhou, Ying ; Qiu, Zhiliang ; Yan, Weili

Author_Institution

Dept. of Biomed. Eng., Hebei Univ. of Technol., Tianjin

fYear

2006

fDate

Aug. 30 2006-Sept. 3 2006

Firstpage

2470

Lastpage

2473

Abstract

This paper presents a novel Radial Basis Function (RBF) neural network model based on Artificial Immune principle, termed AI-based RBF, to estimate the regional head tissue conductivity. In this model, immune learning algorithm is used for determining the number and location of the centers of the hidden layer by regarding the input data of network as antigens, and the centers of the hidden layer as antibodies. The least square algorithm is adopted for achieving the weights of the output layer. With a 2-D concentric circular model of 3 layers, the higher precision and less computation time by this strategy are obtained than those by RBF model

Keywords

artificial immune systems; bioelectric phenomena; brain; electrical conductivity; learning (artificial intelligence); least squares approximations; medical computing; neurophysiology; radial basis function networks; 2-D concentric circular model; AI-based RBF; RBF network; antibodies; antigens; artificial immune algorithm; hidden layer; immune learning algorithm; least square algorithm; radial basis function neural network model; regional head tissue conductivity estimation; Artificial neural networks; Clustering algorithms; Conductivity measurement; Electric variables measurement; Evolution (biology); Immune system; Information processing; Least squares methods; Radial basis function networks; Tomography;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE

Conference_Location

New York, NY

ISSN

1557-170X

Print_ISBN

1-4244-0032-5

Electronic_ISBN

1557-170X

Type

conf

DOI

10.1109/IEMBS.2006.259792

Filename

4462295