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Training Feedforward Neural Networks Using Symbiotic Organisms Search Algorithm
- Source :
- Computational Intelligence and Neuroscience, Vol 2016 (2016), Computational Intelligence and Neuroscience
- Publication Year :
- 2016
- Publisher :
- Hindawi Limited, 2016.
-
Abstract
- Symbiotic organisms search (SOS) is a new robust and powerful metaheuristic algorithm, which stimulates the symbiotic interaction strategies adopted by organisms to survive and propagate in the ecosystem. In the supervised learning area, it is a challenging task to present a satisfactory and efficient training algorithm for feedforward neural networks (FNNs). In this paper, SOS is employed as a new method for training FNNs. To investigate the performance of the aforementioned method, eight different datasets selected from the UCI machine learning repository are employed for experiment and the results are compared among seven metaheuristic algorithms. The results show that SOS performs better than other algorithms for training FNNs in terms of converging speed. It is also proven that an FNN trained by the method of SOS has better accuracy than most algorithms compared.
- Subjects :
- Symbolism
0209 industrial biotechnology
General Computer Science
Article Subject
Computer science
General Mathematics
02 engineering and technology
Machine learning
computer.software_genre
lcsh:Computer applications to medicine. Medical informatics
Feedback
lcsh:RC321-571
020901 industrial engineering & automation
Search algorithm
0202 electrical engineering, electronic engineering, information engineering
Data Mining
Humans
Learning
Computer Simulation
Metaheuristic
lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry
Analysis of Variance
business.industry
General Neuroscience
Supervised learning
Training (meteorology)
General Medicine
Metaheuristic algorithms
Symbiotic interaction
Feedforward neural network
lcsh:R858-859.7
020201 artificial intelligence & image processing
Neural Networks, Computer
Artificial intelligence
business
computer
Algorithms
Research Article
Subjects
Details
- Language :
- English
- ISSN :
- 16875273 and 16875265
- Volume :
- 2016
- Database :
- OpenAIRE
- Journal :
- Computational Intelligence and Neuroscience
- Accession number :
- edsair.doi.dedup.....02735d3137e4e0dedaca3c3499688e0f