An adaptive neurofuzzy network for identification of the complicated nonlinear system
Ying Li, Bendu Bai, Licheng Jiao · 2000
This paper presents a compound neural network model, i.e., adaptive neurofuzzy network (ANFN), which can be used for identifying the complicated nonlinear system. The proposed ANFN has a simple structure and exploits a hybrid algorithm combining supervised learning and unsupervised learning. In addition, ANFN is capable of overcoming the error of system identification due to the existence of some changing points and improving the accuracy of identification of the whole system. The effectiveness of the model and its algorithm is tested on the identification results of missile attacking area.