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| Title: | Engine data classification with simultaneous recurrent network using a hybrid PSO-EA algorithm | |
| Author (s): | Xindi Cai Wunsch, Donald C. | |
| Department/Lab Affiliations: | Applied Computational Intelligence Laboratory Electrical and Computer Engineering | |
| Keywords: | 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 | |
| Issue Date: | 2005 | |
| Publisher: | Institute of Electrical and Electronics Engineers | |
| Citation: | Xindi Cai; Wunsch, D.C., II, "Engine data classification with simultaneous recurrent network using a hybrid PSO-EA algorithm" IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on Neural Networks, pp. 2319- 2323 vol. 4, 31 July-4 Aug. 2005 | |
| Abstract: | 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. | |
| Type: | Article - Conference proceedings text | |
| Copyright Notice: | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. FULL COPYRIGHT INFORMATION: | |
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| title | Engine data classification with simultaneous recurrent network using a hybrid PSO-EA algorithm | |
| contributor.author | Xindi Cai | |
| contributor.author | Wunsch, Donald C. | |
| contributor.deptlab | Applied Computational Intelligence Laboratory | |
| contributor.deptlab | Electrical and Computer Engineering | |
| subject | automobiles | |
| subject | classification | |
| subject | engine data classification | |
| subject | engines | |
| subject | evolutionary algorithm | |
| subject | evolutionary computation | |
| subject | evolutionary learning algorithm | |
| subject | nonlinear car engine data | |
| subject | particle swarm optimisation | |
| subject | particle swarm optimization | |
| subject | recurrent neural nets | |
| subject | simultaneous recurrent network | |
| date.issued | 2005 | |
| date.submitted | 2007 | |
| publisher | Institute of Electrical and Electronics Engineers | |
| identifier.citation | Xindi Cai; Wunsch, D.C., II, "Engine data classification with simultaneous recurrent network using a hybrid PSO-EA algorithm" IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on Neural Networks, pp. 2319- 2323 vol. 4, 31 July-4 Aug. 2005 | |
| identifier.pub.URI | ||
| description.abstract | 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. | |
| type | Article - Conference proceedings | |
| type.DCMIType | text | |
| type.status | Final version | |
| rights | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. | |
| rights.URI | ||
| date.accessioned | 2007-04-05T14:25:23Z | |
| date.available | 2007-04-05T14:25:23Z | |
| identifier.persist.URI | ||
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