Title :
Cloud Neural Network Algorithm Based on Cloud Transformation
Author :
Han Liwei ; Li Zongkun
Author_Institution :
Sch. of Water Conservancy & Environ. Eng., Zheng Zhou Univ., Zheng Zhou
Abstract :
For the aim of improving the simulation ability of neural network and being able to reflect the randomness, fuzziness and the relevance between the two existed in the real world, a new algorithm-cloud neural network (CNN) based on cloud transformation is presented in this paper. And the parameter adjustment method of CNN is given. The CNN based on cloud transformation could be used in the nonlinear system simulation successfully. Simulation example shows that CNN has a faster convergence rate and higher convergence accuracy in calculation. At the same time, the CNN also has better generalization ability, which means it has a broad prospect on application.
Keywords :
generalisation (artificial intelligence); neural nets; nonlinear systems; simulation; cloud neural network algorithm convergence; cloud transformation theory; generalization ability; nonlinear system simulation; parameter adjustment method; Cellular neural networks; Clouds; Distribution functions; Entropy; Frequency; Fuzzy neural networks; Fuzzy reasoning; Helium; Neural networks; Uncertainty; Cloud neural network(CNN); Cloud transformation; Parameter adjustment; Uncertainty;
Conference_Titel :
Modelling, Simulation and Optimization, 2008. WMSO '08. International Workshop on
Conference_Location :
Hong Kong
Print_ISBN :
978-0-7695-3484-8
DOI :
10.1109/WMSO.2008.103