Systematic boolean satisfiability programming in radial basis function neural network

• Main Authors: Mansor, M. A., Jamaludin, S. Z. M., Kasihmuddin, M. S. M., Alzaeemi, S. A., Basir, M. F. M., Sathasivam, S.
• Format: Article
• Language: English
• Published: MDPI AG 2020
• Subjects: QA Mathematics
• Online Access: http://eprints.utm.my/id/eprint/87853/1/MohdAsyrafMansor2020_SystematicBooleanSatisfiabilityProgramming.pdf; http://eprints.utm.my/id/eprint/87853/; http://www.dx.doi.org/10.3390/pr8020214
• Institution: Universiti Teknologi Malaysia

Radial Basis Function Neural Network (RBFNN) is a class of Artificial Neural Network (ANN) that contains hidden layer processing units (neurons) with nonlinear, radially symmetric activation functions. Consequently, RBFNN has extensively suffered from significant computational error and difficulties...