Incremental Gain Analysis of Chaotic Recurrent Neural Network and Applications in Pattern Association

Chaotic neural networks have been successfully applied in pattern association problems in many research. However there are few in-depth theoretical analysis for such networks, such as stability issues. In this paper, we propose a new type of chaotic recurrent neural network (CRNN) which is more powerful in pattern association comparing to previous work. Furthermore robustness analysis is also presented based on circle theorem, which contributes to provide a theoretical guideline on how to choose the CRNN parameter in different cases. Simulations are also given to verify the results.