【摘要】 针对电磁力平衡传感器在实际应用中受温度等非目标参量影响而导致其输出数据不能准确反映目标参量的问题 ,提出了应用硬件和神经网络技术相结合的方法对其进行补偿。神经网络具有以任意精度逼近任何非线性函数的能力 ,本文利用神经网络中的 BP网络较强的非线性映射能力 ,采用 BP算法对传感器的数据进行修正。理论分析和仿真结果表明了该方法的可行性和有效性。由此说明 ,此种结合方法对于电磁力平衡系统的设计和应用具有较强的现实意义更多还原

【Abstract】 Because electromagnetic balance sensor is affected by non objection parameters in applications, such as temperature, the output of it can not reflect objection parameter precisely. The method compensating electromagnetic balance sensor based on hardware and neural network is put forward. Artificial neural network has the ability to approximate any nonlinear function at arbitrary precision. This paper utilizes BP arithmetic to rectify data of the sensor according to BP network’s nonlinear mapping ability. It is proved by theoretic analysis and simulation results that this method is feasible and validity. Therefore, this method is of great practical significance for the design and application of electromagnetic balance system.更多还原