- Title
- Optimization of back-propagation neural networks architecture and parameters with a hybrid PSO/SA approach
- Creator
- Zarei, Mahdi; Dzalilov, Zari
- Date
- 2009
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/67552
- Identifier
- vital:3673
- Abstract
- Determining the architecture and parameters of neural networks is an important scientific challenge. This paper reports a new hybrid optimization method for optimization of back-propagation neural networks architecture and parameters with a high accuracy. We use particle swarm optimization that has proven to be very effective and fast and has shown to increase the efficiency of simulated annealing when applied to a diverse set of optimization problems. To evaluate the proposed method, we employ the PIMA dataset from the University of California machine learning database. Compared with previous work, we show superior classification accuracy rates of the developed approach.
- Publisher
- Famagusta, North Cyprus :
- Relation
- Paper presented at Fifth International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control, ICSCCW 2009, Famagusta, North Cyprus : 2nd-4th September 2009
- Rights
- Open Access
- Rights
- This metadata is freely available under a CCO license
- Subject
- Neural networks; Neural network architecture; Optimization
- Full Text
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