International Journal of Computational Cognition (http://www.YangSky.com/yangijcc.htm) Volume 1, Number 4, Pages 79–90, December 2003 Publisher Item Identifier S 1542-5908(03)10404-6/$20.00 Article electronically published on December 25, 2002 at http://www.YangSky.com/ijcc14.htm. Please cite this paper as: hChing-Hung Lee, Jang-Lee Hong, Yu-Ching Lin, and Wei-Yu Lai, “Type- 2 Fuzzy Neural Network Systems and Learning”, International Journal of Computational Cognition (http://www.YangSky.com/yangijcc.htm), Volume 1, Number 4, Pages 79–90, December 2003i. TYPE-2 FUZZY NEURAL NETWORK SYSTEMS AND LEARNING CHING-HUNG LEE, JANG-LEE HONG, YU-CHING LIN, AND WEI-YU LAI Abstract. This paper presents a type-2 fuzzy neural network system (type-2 FNN) and its learning using genetic algorithm. The so-called type-1 fuzzy neural network (FNN) has the properties of parallel com- putation scheme, easy to implement, fuzzy logic inference system, and parameters convergence. And, the membership functions (MFs) and the rules can be designed and trained from linguistic information and numeric data. However, there is uncertainty associated with infor- mation or data. Therefore, the type-2 fuzzy sets are used to treat it. Type-2 fuzzy sets let us model and minimizes the effects of uncertain- ties in rule-base fuzzy logic systems (FLS). In this paper, the previous results of type-1 FNN are extended to a type-2 one. In addition, the corresponding learning algorithm is derived by real-code genetic algo- rithm. Copyright c 2002 Yang’s Scientific Research Institute, LLC. All rights reserved. 1. Introduction Recently, intelligent systems including fuzzy logic systems, neural net- works, and genetic algorithm, have been successfully used in widely various applications. The fuzzy neural network systems (neuro-fuzzy systems) com- bine the advantages of fuzzy logic systems and neural networks have become a very active subject in many scientific and engineering areas, such as, model reference control problems, PID controller tuning, signal processing, etc. [2,3,6-11]. In our previous results, the FNN has the properties of parallel computation scheme, easy to implement, fuzzy logic inference system, and parameters convergence. The membership functions (MFs) and the rules can be designed and trained from linguistic information and numeric data. Thus, it is then easy to design an FNN system to achieve a satisfactory level Received by the editors December 18, 2002 / final version received December 23, 2002. Key words and phrases. Fuzzy neural network, type-2 fuzzy sets, genetic algorithm. This work is supported by the National Science Council, Taiwan, R.O.C., under Grant NSC-91-2213-E155-012. c 2002 Yang’s Scientific Research Institute, LLC. All rights reserved. 79