Fuzzy optimal solution of electric tomography imaging: Modelling and application

In this paper, efforts are made to find the fuzzy optimal solutions for electric tomography imaging (ETI) process. Most of the conventional ETI methods depend on an optimization process with a deterministic objective function and constraint conditions. The effective application of these methods requires the availability of measured data. However, in practical ETI possess, the assessable data are uncertain, imprecise, incomplete, and characterized by fuzziness. In this paper, the fuzzy characteristics hidden in the ETI process are extracted, and a new model of fuzzy optimization is built up, whose optimal solution is obtained by using fuzzy linear programming (FLP). The FLP model can provide a robust methodology to boost the ETI spatial resolution. Thus it creates a new and promising research direction for the optimization of the ETI process. Experimental results prove that compared with other existing ETI algorithms, the proposed method is more effective in reconstructing complex and discretely distributed objects.