scholarly journals Agent-based Modeling in doing Logic Programming in Fuzzy Hopfield Neural Network

2021 ◽  
Vol 13 (2) ◽  
pp. 23-32
Author(s):  
Shehab Abdulhabib Saeed Alzaeemi ◽  
◽  
Saratha Sathasivam ◽  
Muraly Velavan
Keyword(s):  
Neural Network ◽  
Logic Programming ◽  
Hopfield Neural Network ◽  
Agent Based Modeling ◽  
Agent Based

scholarly journals Agent-based Modeling for Activation Function in Enhancement Logic Programming in Hopfield Neural Network

2020 ◽  
Vol 9 (4) ◽  
pp. 1872-1879
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Logic Programming ◽  
Hamming Distance ◽  
Hopfield Neural Network ◽  
Activation Function ◽  
Agent Based Modeling ◽  
Activation Functions ◽  
Agent Based ◽  
Artificial Neural

Artificial Neural Network (ANN) uses many activation functions to update the state on neuron. The research and engineering have been used activation functions in the artificial neural network as the transfer functions. The most common reasons for using this transfer function were its unit interval boundaries, the functions and quick computability of its derivative, and several useful mathematical properties in the approximation of theory realm. Aim of this study is to figure out the best robust activation functions to accelerate HornSAT logic in the Hopfield Neural Network's context. In this paper we had developed Agent-based Modelling (ABM) assessed the performance of the Zeng Martinez Activation Function (ZMAF) and the Hyperbolic Tangent Activation Function (HTAF) beside the Wan Abdullah method to do Logic Programming (LP) in Hopfield Neural Network (HNN). These assessments are carried out on the basis of hamming distance (HD), the global minima ratio (zM), and CPU time. NETLOGO 5.3.1 software has been used for developing Agent-based Modeling (ABM) to test the proposed comparison of the efficaecy of these two activation functions HTAF and ZMAF.

scholarly journals Hybrid Imperialistic Competitive Algorithm Incorporated with Hopfield Neural Network for Robust 3 Satisfiability Logic Programming

2019 ◽  
Vol 8 (2) ◽  
pp. 144 ◽  
Author(s):  
Vigneshwer Kathirvel ◽  
Mohd. Asyraf Mansor ◽  
Mohd Shareduwan Mohd Kasihmuddin ◽  
Saratha Sathasivam
Keyword(s):  
Neural Network ◽  
Logic Programming ◽  
Computation Time ◽  
Imperialist Competitive Algorithm ◽  
Hopfield Neural Network ◽  
Absolute Error ◽  
Training Algorithm ◽  
Competitive Algorithm ◽  
Bayesian Criterion ◽  
Expected Outcome

Imperialist Competitive algorithm (ICA) is a robust training algorithm inspired by the socio-politically motivated strategy. This paper focuses on utilizing a hybridized ICA with Hopfield Neural Network on a 3-Satisfiability (3-SAT) logic programming. Eventually the performance of the proposed algorithm will be compared to other 2 algorithms, which are HNN-3SATES (ES) and HNN-3SATGA (GA). The performance shall be evaluated with the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Sum of Squares Error (SSE), Schwarz Bayesian Criterion (SBC), Global Minima Ratio and Computation Time (CPU time). The expected outcome will portray that the IC algorithm will outperform the other two algorithms in doing 3-SAT logic programming.

Comparing the logic programming between Hopfield neural network and radial basis function neural network

2019 ◽  
Author(s):  
Shehab Abdulhabib Alzaeemi ◽  
Mohd. Asyraf Mansor ◽  
Mohd Shareduwan Mohd Kasihmuddin ◽  
Saratha Sathasivam
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Logic Programming ◽  
Basis Function ◽  
Hopfield Neural Network ◽  
Radial Basis

scholarly journals Quality Solution of Logic Programming in Hopfield Neural Network

2019 ◽  
Vol 1366 ◽  
pp. 012094
Author(s):  
M S M Kasihmuddin ◽  
M A Mansor ◽  
S Alzaeemi ◽  
M F M Basir ◽  
S Sathasivam
Keyword(s):  
Neural Network ◽  
Logic Programming ◽  
Hopfield Neural Network

scholarly journals Hybrid Genetic Algorithm Model in Neuro Symbolic Integration

2020 ◽  
Vol 9 (4) ◽  
pp. 2144-2149
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Hamming Distance ◽  
Hybrid Genetic Algorithm ◽  
Hopfield Neural Network ◽  
Computational Time ◽  
Agent Based ◽  
Proposed Model ◽  
Network Studies ◽  
Minimum Ratio

The development of artificial neural network and logic programming plays an important part in neural network studies. Genetic Algorithm (GA) is one of the escorted randomly searching technicality that uses evolutional concepts of the natural election as a stimulus to solve the computational problems. The essential purposes behind the studies of the evolutional system is for developing adaptive search techniques which are robust. In this paper, GA is merged with agent based modeling (ABM) by using specified proceedings to optimise the states of neurons and energy function in the Hopfield neural network (HNN). Hence, it is observed that the GA provides the best solutions in affirming optimal states of neurons and thus, enhancing the performance of Horn Satisfiability logical program (HornSAT) in Hopfield neural network. This is due to the fact that the GA lesser susceptive to be restricted in the local optimal or in any suboptimal solutions. NETLOGO version 6.0 will be used as a dynamic platform to test our proposed model. Hence, the computer simulations will be carried out to substantiate and authenticate the efficiency of the proposed model. The results are then tabulated by evaluating the global minimum ratio, computational time and hamming distance

scholarly journals Discrete Hopfield Neural Network in Restricted Maximum k-Satisfiability Logic Programming

2018 ◽  
Vol 47 (6) ◽  
pp. 1327-1335 ◽  
Author(s):  
Mohd Shareduwan Mohd Kasihmuddin ◽  
Mohd Asyraf Mansor ◽  
Saratha Sathasivam
Keyword(s):  
Neural Network ◽  
Logic Programming ◽  
Hopfield Neural Network
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