Snyder S D, Tanaka N
Dept. of Mech. Eng., Adelaide Univ., SA.
IEEE Trans Neural Netw. 1995;6(4):819-28. doi: 10.1109/72.392246.
Feedforward control of sound and vibration using a neural network-based control system is considered, with the aim being to derive an architecture/algorithm combination which is capable of supplanting the commonly used finite impulse response filter/filtered-x least mean square (LMS) linear arrangement for certain nonlinear problems. An adaptive algorithm is derived which enables stable adaptation of the neural controller for this purpose, while providing the capacity to maintain causality within the control scheme. The algorithm is shown to be simply a generalization of the linear filtered-x LMS algorithm. Experiments are undertaken which demonstrate the utility of the proposed arrangement, showing that it performs as well as a linear control system for a linear control problem and better for a nonlinear control problem. The experiments also lead to the conclusion that more work is required to improve the predictability and consistency of the performance before the neural network controller becomes a practical alternative to the current linear feedforward systems.
本文考虑了使用基于神经网络的控制系统对声音和振动进行前馈控制，目的是推导一种架构/算法组合，该组合能够在某些非线性问题上取代常用的有限脉冲响应滤波器/滤波x最小均方（LMS）线性装置。为此，推导了一种自适应算法，该算法能够使神经控制器实现稳定自适应，同时具备在控制方案中保持因果关系的能力。结果表明，该算法只是线性滤波x LMS算法的一种推广。进行的实验证明了所提出装置的实用性，表明它在解决线性控制问题时表现与线性控制系统相当，而在解决非线性控制问题时表现更佳。实验还得出结论，在神经网络控制器成为当前线性前馈系统的实用替代方案之前，还需要做更多工作来提高性能的可预测性和一致性。
