We applied an architecture which automates the design of simultaneous recurrent network (SRN) using a new evolutionary learning algorithm. This new evolutionary learning algorithm is based on a hybrid of particle swarm optimization (PSO) and evolutionary algorithm (EA). By combining the searching abilities of these two global optimization methods, the evolution of individuals is no longer restricted to be in the same generation, and better performed individuals may produce offspring to replace those with poor performance. The novel algorithm is then applied to the simultaneous recurrent network for the engine data classification. The experimental results show that our approach gives solid performance in categorizing the nonlinear car engine data.
X. Cai and D. C. Wunsch, "Engine Data Classification with Simultaneous Recurrent Network using a Hybrid PSO-EA Algorithm," Proceedings of the IEEE International Joint Conference on Neural Networks, 2005, Institute of Electrical and Electronics Engineers (IEEE), Jan 2005.
The definitive version is available at http://dx.doi.org/10.1109/IJCNN.2005.1556263
IEEE International Joint Conference on Neural Networks, 2005
Electrical and Computer Engineering
Keywords and Phrases
Automobiles; Classification; Engine Data Classification; Engines; Evolutionary Algorithm; Evolutionary Computation; Evolutionary Learning Algorithm; Nonlinear Car Engine Data; Particle Swarm Optimisation; Particle Swarm Optimization; Recurrent Neural Nets; Simultaneous Recurrent Network
Article - Conference proceedings
© 2005 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.