Title :
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
fDate :
Aug. 30 2006-Sept. 3 2006
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;
Conference_Titel :
Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE
Conference_Location :
New York, NY
Print_ISBN :
1-4244-0032-5
Electronic_ISBN :
1557-170X
DOI :
10.1109/IEMBS.2006.259792
