１.利用GA算法优化RBFＮＮ的各种权值； ２.利用RBFNN进行函数跟踪； 3.比较测试未用GA优化的和使用GA优化的RBFNN的结果。-1.Use GA algorithm to optimize a variety of weights in RBFNN 2. Use RBFNN to realize tracking function 3. Compare and test the results of using GA and not using GA.

基于BP和RBF的函数逼近编程及仿真，含仿真结果和分析比较-Approximation programming and simulation-based BP and RBF functions, including analysis of the simulation results and compare

为了评价ELM的性能，试分别将ELM应用于基于近红外光谱的汽油辛烷值测定和鸢尾花种类识别两个问题中,并将其结果与传统前馈网络(BP、RBF、PNN、GRNN等)的性能和运行速度进行比较,并探讨隐含层神经元个数对ELM性能的影响。(n order to evaluate the performance of ELM, test the application of ELM in the near infrared spectroscopy based on the determination of