Radial basis function neural network for 2 satisfiability programming

Shehab Alzaeemi (Universiti Sains Malaysia)
Mohd. Asyraf Mansor (Universiti Sains Malaysia)
Mohd Shareduwan Mohd Kasihmuddin (Universiti Sains Malaysia)
Saratha Sathasivam (Universiti Sains Malaysia,)
Mustafa Mamat (Universiti Sultan Zainal Abidin)

Indonesian Journal of Electrical Engineering and Computer Science
Vol 18, No 1: April 2020

Radial Basis Function Neural Network (RBFNN) is very prominent in data processing. However, improving this technique is vital for the NN training process. This paper presents an integrated 2 Satisfiability in radial basis function neural network (RBFNN-2SAT). There are two different types of training in RBFNN, namely no-training technique and half-training technique. The performance of the solutions via Genetic Algorithm (GA) training was investigated by comparing the Radial Basis Function Neural Network No-Training Technique (RBFNN- 2SATNT) and Radial Basis Function Neural Network Half-Training Technique (RBFNN- 2SATHT). The comparison of both techniques was examined on 2 Satisfiability problem by using a C# software that was developed for this experiment. The performance of the RBFNN-2SATNT and RBFNN-2SATHT in performing 2SAT is discussed in terms of root mean squared error (RMSE), sum squared error (SSE), mean absolute percentage error (MAPE), mean absolute error (MAE), number of the hidden neurons and CPU time. Results obtained from a computer simulation showed that RBFNN-2SATHT outperformed RBFNN-2SATNT.