基于免疫机制的径向基函数网络动态训练方法

提出了一种针对动态系统的两级径向基函数（RBF）网络训练方法.该方法借鉴自然免疫系统的动态特性和自适应处理能力，将RBF网络的隐层节点分为粗、细拟合节点，分别对动态系统中的稳定部分和突变部分作不同强度的训练.通过自适应免疫机制，较大地降低了计算复杂度，提高了系统的动态追踪能力.仿真结果表明，所提出的方法能较好地平衡训练精度与收敛速度的矛盾，达到了很高的性能.

Dynamic RBF Network Training Method Based on Immune Mechanism

A two stage RBF network training method for dynamic systems was proposed. Taking its inspiration from dynamic nature and adaptability of natural immune system, in the method, the RBF hidden units are differentiated into two types, and undergo different training stages according to the stable and mutation part of dynamic systems. Through adaptive immune mechanism, the method greatly reduces the computational complexity and improves the dynamic tracing ability. The computer simulations demonstrate that the proposed method strikes a good balance between precision and convergence speed, and achieves very high performance.