An improved Aihara chaotic neural network and its dynamic characteristics
Yu Chuan Wu, Yandong Wen, Li Wang · 2012
Emergence describes the macroscopic dynamic phenomena of complex systems with mutual effects of local members on each other. At present, emergent mechanism needs to be further studied, and types of researched emergence computation model are limited. The study method of well-known Swarm model also lacks of generality. A different emergent model which is improved from Aihara chaotic neural network is proposed in this paper to give the diversity of the current emergent model. Firstly, considering the features of emergent model and based on characteristics of Aihara chaotic neural network, the connection mechanism of cellular automata is introduced to the chaotic neural networks to improve it. By comparing with existing network model, there is an obvious emergency for the interaction rules and forms in our new model. Then, by calculating dynamic index of the model emergency of the model is verified. Finally, the emergence and chaos characteristics of improved model are proved via emergence analysis methods.