Revista: | Journal of applied research and technology |
Base de datos: | PERIÓDICA |
Número de sistema: | 000394044 |
ISSN: | 1665-6423 |
Autores: | Wang, P1 Lin, J.S2 Wang, M1 |
Instituciones: | 1Xi'an University of Science and Technology, School of Electric and Control, Xi'an, Shaanxi. China 2National Chin-Yi University of Technology, Department of Computer Science and Information Engineering, Taichung. Taiwán |
Año: | 2015 |
Periodo: | Abr |
Volumen: | 13 |
Número: | 2 |
Paginación: | 197-204 |
País: | México |
Idioma: | Inglés |
Tipo de documento: | Artículo |
Enfoque: | Aplicado, descriptivo |
Resumen en inglés | In this paper, we introduce a novel image reconstruction algorithm with Least Squares Support Vector Machines (LS-SVM) and Simulated Annealing Particle Swarm Optimization (APSO), named SAP. This algorithm introduces simulated annealing ideas into Particle Swarm Optimization (PSO), which adopts cooling process functions to replace the inertia weight function and constructs the time variant inertia weight function featured in annealing mechanism. Meanwhile, it employs the APSO procedure to search for the optimized resolution of Electrical Capacitance Tomography (ECT) for image reconstruction. In order to overcome the soft field characteristics of ECT sensitivity field, some image samples with typical flow patterns are chosen for training with LS-SVM. Under the training procedure, the capacitance error caused by the soft field characteristics is predicted, and then is used to construct the fitness function of the particle swarm optimization on basis of the capacitance error. Experimental results demonstrated that the proposed SAP algorithm has a quick convergence rate. Moreover, the proposed SAP outperforms the classic Landweber algorithm and Newton-Raphson algorithm on image reconstruction |
Disciplinas: | Ciencias de la computación, Ingeniería |
Palabras clave: | Procesamiento de datos, Ingeniería de control, Tomografía de capacitancia eléctrica, Algoritmos, Recocido simulado, Máquinas vectoriales de soporte, Optimización por enjambre de partículas |
Keyword: | Computer science, Engineering, Data processing, Control engineering, Electrical capacitance tomography, Algorithms, Simulated annealing, Support vector machines, Particle swarm optimization |
Texto completo: | Texto completo (Ver HTML) |