Title :
A recurrent self-organizing neural fuzzy inference network
Author :
Juang, Chia-Feng ; Lin, Chin-Teng
Author_Institution :
Dept. of Control Eng., Nat. Chiao Tung Univ., Hsinchu, Taiwan
Abstract :
A recurrent self-organizing neural fuzzy inference network (RSONFIN) is proposed in this paper. The RSONFIN is constructed from a series of dynamic fuzzy rules. The temporal relations embedded in the network are built by adding some feedback connections representing the memory elements to a feedforward neural fuzzy network. Each weight as well as node in the RSONFIN has its own meaning and represents a special element in a fuzzy rule. There are no hidden nodes (i.e., no membership functions and fuzzy rules) initially in the RSONFIN. They are created online via concurrent structure identification (the construction of dynamic fuzzy if-then rules) and parameter identification (the tuning of the free parameters of membership functions). The structure learning together with the parameter learning forms a fast learning algorithm for building a small, yet powerful, dynamic neural fuzzy network. Simulations on temporal problems are performed
Keywords :
feedforward neural nets; fuzzy neural nets; identification; inference mechanisms; learning (artificial intelligence); recurrent neural nets; self-organising feature maps; dynamic fuzzy rules; feedforward neural network; fuzzy reasoning; neural fuzzy inference network; parameter identification; parameter learning; recurrent neural network; self-organizing neural network; structure learning; Buildings; Control engineering; Feedforward neural networks; Fuzzy neural networks; Fuzzy reasoning; Fuzzy sets; Neural networks; Neurofeedback; Problem-solving; Recurrent neural networks;
Conference_Titel :
Fuzzy Systems, 1997., Proceedings of the Sixth IEEE International Conference on
Conference_Location :
Barcelona
Print_ISBN :
0-7803-3796-4
DOI :
10.1109/FUZZY.1997.619743
