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G Artificial Intelligence (AI) can be encompassed in some functional graphical and mathematical models that act as a symbolic system [1]. Greater impact can be achieved when symbolic operations have been integrated inside the Artificial Neural Network (ANN). ANN known as the conductive system has received careful attention due to its ability to evaluate the complex nonlinear dataset [1]. ANN has been successfully used in solving non-limited applications such as classification and optimization of approximation functions. However, the functionality of ANN can be measured by embedding the correct symbolic rule to govern the whole neural system. Logic programming has been a language of ANN for decades. Wan Abdullah [2] successfully explored the neural network that has been governed by logic programming. In this work, logical rule embodied ANN and the characteristic of the network will be examined by using Lyapunov energy analysis. The minimization of energy as a solution to the combinatorial representation motivates the integration of logical rules in a neural network [3]-[6]. The question remains on how one can choose the best ANN model in order to embed logic programming. The reliable ANN model typically has the least prediction and classification error analysis. In that regard, Radial Basis Function Neural Network (RBFNN) fascinated the researchers from sciences and engineering field because of simpler networks structure, faster learning speed and better approximation capabilities. As stated by Hamadneh et al. [7] in their paper, RBFNN can be used to develop separate models for the shear stress and heat transfer rate due to simpler networks structure. RBFNN is a feedforward neural network that contains 3 neuron layers (input, hidden and output layers). The input layer (containing input neurons) receives information being transferred to the hidden layer for data synthesis and training. The synthesized data will be used in the output layer (containing output neuron). The foundation of having 3 layers is to minimize the classification and prediction error in RBFNN [8]. Hamadneh et al. [4] initially implemented logic programming in RBFNN. Their proposed network explored the capability of HornSAT as a logical rule in RBFNN. In this case, logical structure of RBFNN is solely dependent on 3 parameters: the center of all input neurons, its widths, and its Gaussian activation function. Despite the fact that RBFNN can be applied effectively, the number of neurons in a hidden layer for RBFNN will determine the complexity of the network [9]. If the number of neurons in the hidden layers is not enough, the learning in RBFNN fails to achieve optimal convergence. However, if the number of neurons in the hidden layers is very high, the network will experience overlearning [10]. Since the complexity of RBFNN increases as the number of clauses increase, an optimization algorithm becomes crucial. Yang and Ma [11] have successfully applied the Sparse Neural Network (SNN) algorithm for optimizing the number of hidden neurons. The core mechanism of SNN is in reducing the error via trial and error approach for determining the number of hidden neurons explicitly from the set of neurons. The limitation of SNN paradigm can be seen in extensive computational time during the number of hidden Satisfiability Logic Analysis Via Radial Basis Function Neural Network with Artificial Bee Colony Algorithm