一种利用函数链神经网络的传感器建模新方法

讨论基于函数链神经网络 (FLNN)的传感器建模新方法 ,其结构简单、使用灵活、建模容易 ,易于实时硬件实现。两个算例说明网络的训练和非线性逼近方法 ,显示出网络的自适应能力、学习能力 ,基于FLNN的传感器模型可同时实现温度补偿和非线性校正。实际上 ,利用这种模型可以跟踪补偿环境改变引起的传感器特性的各种变化 ,在测控系统中具有良好的应用前景。

New approach to sensor modeling based on FLNN

A new approach to sensor modeling based on a functional link neural network is discussed in this paper. Its structure offers less computational complexity. In this paper two algorithm examples are given to illustrate the proposed method. The proposed inverse model approach compensates the effects of the nonlinearity and temperature. In fact , the flexibility of the technique automatically compensates any variation of the sensor response occurring due to change in environmental conditions. The approach for sensor modeling is superior to the existing techniques. It has a potential future in the field of measurement and control.