An evolved recurrent neural network and its application

Authors:
Chunkai Zhang;Hong Hu
Affiliations:
Member, IEEE Department of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen Graduate School, Shenzhen, China;Member, IEEE Department of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen Graduate School, Shenzhen, China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Year:
2005

Citing 5
Cited 0

Learning internal representations by error propagation

Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Learning state space trajectories in recurrent neural networks

Neural Computation
A learning algorithm for continually running fully recurrent neural networks

Neural Computation
A hybrid of genetic algorithm and particle swarm optimization for recurrent network design

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Pruning algorithms-a survey

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

An evolved recurrent neural network is proposed which automates the design of the network architecture and the connection weights using a new evolutionary learning algorithm. This new algorithm is based on a cooperative system of evolutionary algorithm (EA) and particle swarm optimisation (PSO), and is thus called REAPSO. In REAPSO, the network architecture is adaptively adjusted by PSO, and then EA is employed to evolve the connection weights with this network architecture, and this process is alternated until the best neural network is accepted or the maximum number of generations has been reached. In addition, the strategy of EAC and ET are proposed to maintain the behavioral link between a parent and its offspring, which improves the efficiency of evolving recurrent neural networks. A recurrent neural network is evolved by REAPSO and applied to the state estimation of the CSTR System. The performance of REAPSO is compared to TDRB, GA, PSO and HGAPSO in these recurrent networks design problems, demonstrating its superiority.