RBF network based on artificial immune algorithm for regional head conductivity estimation.
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.
Dong
Guoya
G
Dept. of Biomed. Eng., Hebei Univ. of Technol., Tianjin, China. donggya@jsmail.hebut.edu.cn
Zhou
Ying
Y
Qiu
Zhiliang
Z
Yan
Weili
W
D000465:Algorithms; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods; D003198:Computer Simulation; D004553:Electric Conductivity; D004569:Electroencephalography / Q000379:methods*; D006257:Head / Q000502:physiology*; D006801:Humans; D008954:Models, Biological; D018448:Models, Immunological; D016571:Neural Networks, Computer; D010992:Plethysmography, Impedance / Q000379:methods*