A New Artificial Immune System Algorithm for training the 2 Satisfiability Radial Basis Function Neural Network

2 Satisfiability (2SAT) logic programming has been a prominent logical rule that defines the structure of Radial Basis Function Neural Network. Training Radial Basis Function Neural Network with logic 2 Satisfiability is an optimization task since it is desired to find the optimal output weights during the training process. In this paper, artificial immune system (AIS) algorithm will be introduced to facilitate the training of RBFNN-2SAT. AIS is used for updating the output weights during training RBFNN-2SAT. In this study, the effectiveness of our hybrid computing paradigm, namely RBFNN-2SATAIS can be estimated by evaluating its testing data result using the root mean square error (RMSE) and computation time (CT). The obtained findings show that the proposed method was effective for achieving acceptable results for 2SAT logic rule.