Complex Systems Modeling Using Scale-Free Highly-Clustered Echo State Network
Zhidong Deng, Yi Zhang · The 2006 IEEE International Joint Conference on Neural Network Proceedings · 2006
Inspired by the universal laws governing different kinds of complex networks, we propose a scale-free highly-clustered echo state network (SHESN). Different from echo state network (ESN), the state reservoir of the SHESN is generated by natural growth rules and eventually forms a complex network with small-world, scale-free properties, and hierarchically distributed structure. We implemented a large-scale SHESN with 3,000 internal neurons and applied it to modeling the pH-neutralization process. Simulation results showed the superior performance of SHESN. Furthermore, we analyzed the natural characteristics of the SHESN and discussed our growth rules and the new state reservoir from a brain functional network perspective.