基于神经网络LMBP算法的正则模糊文法推导

Regular fuzzy grammatical inference based on LMBP algorithm of neural network

用实时间回馈(RTRL)算法和实编码基因遗传(RCGA)算法训练二阶递归神经网络进行模糊文法推导,表现出了精度高的良好性能,但速度较慢。然而作为目前最快的递归神经网络算法Levenberg-Marquard(tLMBP)算法在模糊文法推导中的应用却很少引起学者们的关注。通过实验对LMBP算法在正则模糊文法推导中的优势与缺陷等性能进行分析,实验显示了LMBP算法在模糊文法推导中的快速收敛能力。

The second-order recurrent neural nerworks,based on the Real-Time Recurrent Learning（RTRL） algorithm and the Real-Coded Genetical Algorithm（RCGA）,Fuzzy Grammar Inference（FGI） has the advantage of high accuracy,yet has the drawback of low convergence rate.However,the Levenberg-Marquard（LMBP） which is the fast algorithm of neural networks at present has not been paid more attention.The algorithm is described and the advantages and drawbacks are analysed by experiments.It is obvious that the convergence rate is high to perform the FGI.It also has the ability of processing super-long strings, too.