CAJ | 학술논문

D-FNN的基本思想是构造一个基于扩展的RBF神经网络，它可以看成是一个TSK模糊系统，也可以看作是基于归一化的高斯RBF神经网络。D-FNN算法中，不仅参数可以在学习过程中调整，同时，也可以自动确定模糊神经网络的结构。非线性参数是由训练样本和高斯宽度直接决定的，只需一步训练就可以达到目标。由于修剪策略的应用，网络的结构不会持续增长，因而确保了系统的泛化能力。使用D-FNN对非线性动态系统辨识进行了仿真，并与相关算法作比较，从而发现了D-FNN算法的有效性和高效性。
D-FNN적기본사상시구조일개기우확전적RBF신경망락，타가이간성시일개TSK모호계통，야가이간작시기우귀일화적고사RBF신경망락。D-FNN산법중，불부삼수가이재학습과정중조정，동시，야가이자동학정모호신경망락적결구。비선성삼수시유훈련양본화고사관도직접결정적，지수일보훈련취가이체도목표。유우수전책략적응용，망락적결구불회지속증장，인이학보료계통적범화능력。사용D-FNN대비선성동태계통변식진행료방진，병여상관산법작비교，종이발현료D-FNN산법적유효성화고효성。
Dynamic Fuzzy Neural Network (D-FNN),which basic idea is to construct a RBF neural net-work based on extension,could be seen as a TSK fuzzy system,as well as a Gaussian RBF neural net-work based on normalized.Within D-FNN algorithms,not only parameters could be adjusted in the learn-ing process,but also the structure of fuzzy neural network could be automatically determined.Nonlinear parameters are directly decided by the training samples and Gaussian width,which only need one step training to achieve this goal.Due to the application of pruning strategies,network structure would not continue to grow,thus ensuring the generalization capability of the system.Simulations are performed on nonlinear dynamic system identification by using D-FNN,and the effectiveness and efficiency of D-FNN algorithm are proved by comparison with related algorithms.