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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. [ABSTRACT FROM AUTHOR]

The training of a radial basis function neural network (RBFNN) involves finding the optimal number of hidden neurons in the hidden layer and finding the RBFNN parameters such as center, width, and the output weight. 2 Satisfiability logic programming will be embedded in RBFNN during the training phase. Two training techniques, notraining, and half-training are proposed in this paper. The experiment of both techniques has been examined by using Microsoft Visual Studio 2008 C# Express software. The detailed comparison of the performance of two different techniques in performing 2SAT is discussed in term of root mean square error (RMSE), the number of the hidden neurons and CPU time. The results obtained from the computer simulation have shown that RBFNN-2SAT in halftraining technique outperforms than RBFNN-2SAT in no-training technique due to the terms of RMSE, the number of hidden neurons and CPU time are typically much less than the number of data points, and the centers are not restricted to be data points. [ABSTRACT FROM AUTHOR]

Logic programming is a superior language because it operates on a higher level of mathematical or logical reasoning. Logic programming is well-suited in building the artificial intelligence systems. In this paper, we reviewed the performance of the logic programming in Hopfield Neural Network (HNN) and Radial Basis Function Neural Network (RBFNN). Logic programming by using the Embedding method will improve the performance of RBFNN. In HNN, the logic programming can be implemented by finding the optimal synaptic weight via Wan Abdullah method. RBFNN is expected to do logic programming optimally compared to HNN. This study gives an overview of HNN and RBFNN regarding architectures, learning processing, and their application in 2 Satisfiability (2SAT) logic programming. Both networks will be assessed based on accuracy, sensitivity, and robustness. Pursuing that, RBFNN is expected to outperform HNN in doing 2 Satisfiability logic programming. [ABSTRACT FROM AUTHOR]