计算机科学 ›› 2017, Vol. 44 ›› Issue (2): 239-243.doi: 10.11896/j.issn.1002-137X.2017.02.039
司文杰,杨飞飞
SI Wen-jie and YANG Fei-fei
摘要: 神经网络已经广泛应用于系统建模和模式识别领域。但为了逼近未知的参数或者系统动态,需要大量的神经元达到足够的逼近精度,因此导致了计算负荷的增大。运算量制约着大规模神经网络计算,无法使其应用到实际的在线系统中。CPU处理无法保证在线数据的同步运算,需要借助图形处理单元GPU(Graphic Processing Unit)来解决实时性同步运算问题。首先,利用RBF神经网络的持续激励PE(Persistent Excitation)特性对系统输入进行分析,减少神经元的数目且优化设计算法,从而提高逼近精度。其次,基于LabVIEW平台,利用LabVIEW的GPU高性能分析工具包实现神经网络算法和并行计算。最后,在一台航空低速轴流压气机中开发基于大规模训练神经网络的LabVIEW系统。实验结果表明,提出的方法可以实现对系统的在线实时运行,满足航空失速检测的要求。
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