The Extreme Learning Machine (ELM) is a simple hidden layer feedforward neural network algorithm that is simple in structure, easy to use, and effective. In this paper, ELM-based capacitance tomography (ECT) image reconstruction methods are studied. Firstly, in order to ensure the representativeness of samples, various training sample models are randomly generated, including: single object, two objects, three objects, ring and layer. Secondly, the simulated capacitance value and its gray value of the training sample and the test sample model are normalized, and the ultimate learning machine is used for training and testing. Finally, the simulation results and analysis are given, and the ELM algorithm is discussed. The advantages and disadvantages show that compared with the LBP algorithm and the Landweber iterative algorithm, the ELM based ECT image reconstruction algorithm has superior generalization ability and learning speed, and the imaging accuracy is greatly improved.
