Title :
TSK interval type-2 fuzzy neural networks for chaotic time series prediction
Author_Institution :
Inst. of Electr. Eng., Henan Univ. of Technol., Zhengzhou, China
Abstract :
This paper presents TSK interval type-2 fuzzy neural network (TSK IT2FNN)and its learning algorithm for chaotic time series prediction. First, The structure of TSK IT2FNN is decided using the hierarchical fuzzy clustering algorithm. Then its parameters of the precondition membership function and consequence weight are optimized using the gradient descent algorithm. Finally the effectiveness of IT2FNN and its learning algorithm are evaluated by using the Mackey-Glass chaotic time series. The simulation result shows that this proposed method is effective in the paper.
Keywords :
chaos; fuzzy neural nets; gradient methods; learning (artificial intelligence); pattern clustering; time series; Mackey Glass chaotic time series; TSK; chaotic time series prediction; fuzzy clustering algorithm; gradient descent algorithm; interval type-2 fuzzy neural networks; learning algorithm; precondition membership function; Clustering algorithms; Electrical engineering; Fuzzy control; Fuzzy neural networks; Prediction algorithms; Simulation; Time series analysis; TSK interval type-2 fuzzy neural network; chaotic time series; gradient descent algorithm; hierarchical fuzzy clustering algorithm;
Conference_Titel :
Intelligent Control and Automation (WCICA), 2010 8th World Congress on
Conference_Location :
Jinan
Print_ISBN :
978-1-4244-6712-9
DOI :
10.1109/WCICA.2010.5553888
