Pawalai KraipeerapunSathit NakkrasaeChun Che FungSomkid AmornsamankulRamkhamhaeng UniversityMurdoch UniversityMahidol UniversitySouth Carolina Commission on Higher Education2018-09-242018-09-242010-12-01Memetic Computing. Vol.2, No.4 (2010), 249-25718659292186592842-s2.0-78649335853https://repository.li.mahidol.ac.th/handle/20.500.14594/28994This 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.Mahidol UniversityComputer ScienceMathematicsArticleSCOPUS10.1007/s12293-010-0036-5