Publication: Solving regression problem with complementary neural networks and an adjusted averaging technique
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Issued Date
2010-12-01
Resource Type
ISSN
18659292
18659284
18659284
Other identifier(s)
2-s2.0-78649335853
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Mahidol University
Rights Holder(s)
SCOPUS
Bibliographic Citation
Memetic Computing. Vol.2, No.4 (2010), 249-257
Suggested Citation
Pawalai Kraipeerapun, Sathit Nakkrasae, Chun Che Fung, Somkid Amornsamankul Solving regression problem with complementary neural networks and an adjusted averaging technique. Memetic Computing. Vol.2, No.4 (2010), 249-257. doi:10.1007/s12293-010-0036-5 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/28994
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Title
Solving regression problem with complementary neural networks and an adjusted averaging technique
Abstract
This research deals with complementary neural networks (CMTNN) for the regression problem. Complementary neural networks consist of a pair of neural networks called truth neural network and falsity neural network, which are trained to predict truth and falsity outputs, respectively. In this paper, a novel adjusted averaging technique is proposed in order to enhance the result obtained from the basic CMTNN. We test our proposed technique based on the classical benchmark problems including housing, concrete compressive strength, and computer hardware data sets from the UCI machine learning repository. We also realize our technique to the porosity prediction problem based on well log data set obtained from practical field data in the oil and gas industry. We found that our proposed technique provides better performance when compared to the traditional CMTNN, backpropagation neural network, and support vector regression with linear, polynomial, and radial basis function kernels. © 2010 Springer-Verlag.