|Title:||Electrical Impedance Tomography: A Reconstruction Method Based on Neural Networks and Particle Swarm Optimization|
Choi, Charles T. M.
National Chiao Tung University
|Keywords:||Electrical impedance tomography;neural network;particle swarm optimization;finite element method;inverse problems|
|Abstract:||Electrical Impedance Tomography (EIT) is a non-invasive image reconstruction technique. Typically, an EIT scheme involves the solution to an inverse problem, which usually gives a poor resolution, due to linearization and ill-posedness of the problem. An alternative approach based on Artificial Neural Networks (ANN) has been used as a replacement of the inverse problem, giving correct results without linearizing the problem. However, training an ANN may be time consuming and usually requires a large amount of iterations before achieving a correct answer to the input stimulation. Several studies focused on training ANNs, and Evolutionary Algorithms (EA) gives a faster global convergence. In this paper, a novel approach based on Artificial Neural Networks and Particle Swarm Optimization (PSO) is proposed to improve the training process. A training method based on PSO algorithm achieves a faster global convergence.|
|Journal:||1ST GLOBAL CONFERENCE ON BIOMEDICAL ENGINEERING & 9TH ASIAN-PACIFIC CONFERENCE ON MEDICAL AND BIOLOGICAL ENGINEERING|
|Appears in Collections:||Conferences Paper|