Nonlinear Rectification of Sensor Based on Particle Swarm Optimization with Chaos
A Chaotic Particle Swarm Optimization (CPSO) algorithm is proposed. In this method, the known tent map is incorporated into PSO algorithm to gain balance between global search and local search, thus improving the speed and accuracy of the algorithm convergence. A nonlinear rectification principle of sensor is introduced which is different from the traditional methods, then the CPSO algorithm is applied to parameter estimation of nonlinear rectification of sensor. Finally, the effectiveness of the proposed algorithm is tested through simulation experiments on the nonlinear rectification of electric-eddy-type micro displacement sensor. The experiment results show that, compared with traditional PSO, with the CPSO, the parameter estimation of nonlinear rectification of sensor is enabled to achieve a better global optimization and a higher converging speed, the rectification curve is more accurate, and the rectification value has an excellent linear relation with input signal. So it can be concluded that this proposed algorithm can be seen as a very promising option to solve nonlinear rectification of sensor.
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Document Type: Research Article
Publication date: 2011-10-01
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