Development of Fuzzy Neural Networks: Current Framework and Trends
Fan Liu, Meng Joo · Sciyo eBooks · 2010
Fuzzy systems have been demonstrated their ability to solve different kinds of problems in classification, modeling control and in a considerable number of industry applications. It has been shown as a powerful methodology for dealing with imprecision and nonlinearity efficiently (Wang, 1994). However, one of the shortcomings of fuzzy logic is the lack of learning and adaptation capabilities. As we know, neural network (NN) is one of the important technologies towards realizing artificial intelligence and machine learning. Many types of neural networks with different learning algorithms have been designed and developed (Deng et al., 2002; Levin & Narendra, 1996; Narendra P Huang et al., 2004; Jang, 1993; Juang L Leng et al., 2004; Lin L Qiao & Wang, 2008). The technology of FNNs combines the profound learning capability of neural networks with the mechanism of explicit and easily interpretable knowledge presentation provided by fuzzy logic. In a word, FNN is able to represent meaningful real-world concepts in