基于PSO-SVM的循环热扰动湿度传感器设计

o solve the problems of hysteresis error and measurement instability existing in traditional humidity sensors in dynamic temperature and humidity environments, this paper designs a high-precision humidity sensor integrating the mechanism of "cyclic thermal disturbance - dual-path decoupling - intelligent compensation". Dual humidity sensors are adopted to alternately measure the heating and cooling paths. Combined with the dew point inversion technology, the humidity data at different temperature points are inverted to achieve the unified mapping of humidity values to the reference temperature. Combined with Computational Fluid Dynamics (CFD) thermal simulation, the optimal heating power and structural parameters are determined to achieve efficient and stable periodic heating and natural cooling. The Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) regression algorithm are introduced to establish a multi-feature nonlinear correction model, achieving accurate modeling of hysteresis error and adaptive fusion of path weights. The experimental results show that the average absolute error of the high-precision humidity measurement system proposed in this paper for humidity measurement is 0.52%RH, and the root mean square error is 0.57%RH.