Learning Logic Programming in Radial Basis Function Network via Genetic Algorithm

Hamadneh, Nawaf N.; Sathasivam, Saratha; Tilahun, Surafel Luleseged; Choon, Ong Hong

doi:10.3923/jas.2012.840.847

Cited by 38 publications

(36 citation statements)

References 15 publications

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“…Error management during the training phase of RBFNN creates two perspectives of optimizations. Firstly, error accumulation can be reduced by implementing a good optimization algorithm such as Metaheuristics [22] or clustering method [8]. The complexity of the algorithm increases with the number of hidden neurons.…”

Section: Introductionmentioning

confidence: 99%

Systematic Boolean Satisfiability Programming in Radial Basis Function Neural Network

et al. 2020

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Radial Basis Function Neural Network (RBFNN) is a class of Artificial Neural Network (ANN) that contains hidden layer processing units (neurons) with nonlinear, radially symmetric activation functions. Consequently, RBFNN has extensively suffered from significant computational error and difficulties in approximating the optimal hidden neuron, especially when dealing with Boolean Satisfiability logical rule. In this paper, we present a comprehensive investigation of the potential effect of systematic Satisfiability programming as a logical rule, namely 2 Satisfiability (2SAT) to optimize the output weights and parameters in RBFNN. The 2SAT logical rule has extensively applied in various disciplines, ranging from industrial automation to the complex management system. The core impetus of this study is to investigate the effectiveness of 2SAT logical rule in reducing the computational burden for RBFNN by obtaining the parameters in RBFNN. The comparison is made between RBFNN and the existing method, based on the Hopfield Neural Network (HNN) in searching for the optimal neuron state by utilizing different numbers of neurons. The comparison was made with the HNN as a benchmark to validate the final output of our proposed RBFNN with 2SAT logical rule. Note that the final output in HNN is represented in terms of the quality of the final states produced at the end of the simulation. The simulation dynamic was carried out by using the simulated data, randomly generated by the program. In terms of 2SAT logical rule, simulation revealed that RBFNN has two advantages over HNN model: RBFNN can obtain the correct final neuron state with the lowest error and does not require any approximation for the number of hidden layers. Furthermore, this study provides a new paradigm in the field feed-forward neural network by implementing a more systematic propositional logic rule.

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Section: Introductionmentioning

confidence: 99%

Systematic Boolean Satisfiability Programming in Radial Basis Function Neural Network

et al. 2020

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“…In this study, we used the PSO algorithm to improve the performance of RBFNNs. We also compared the results with the genetic algorithm (GA) [12,14], with commercial software MATLAB.…”

Section: Introductionmentioning

confidence: 99%

“…Many optimization problems have been solved by using metaheuristic algorithms, such as Particle swarm optimization (PSO) and genetic algorithm (GA) [11,12]. PSO algorithm is one of the most widely used algorithms to find the optimal values in order to optimize the expectation as a function [11,13].…”

Section: Introductionmentioning

confidence: 99%

“…RBFNN is different from the other networks, possessing several distinctive features. Owing to its universal approximation ability, more compact topology and faster learning speed, it has attracted much attention, and it has been widely applied in many science and engineering fields [3][4][5][6]. RBFNNs typically have three layers: an input layer, a hidden layer with a non-linear activation function and a linear output layer.…”

Section: Introductionmentioning

confidence: 99%

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Computing single step operators of logic programming in radial basis function neural networks

Hamadneh¹,

Sathasivam²,

Choon³

2014

AIP Conference Proceedings

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“…This stochastic approach has reduced the neuron oscillations during retrieval phase deployed by Hopfield Neural Network. Hamadneh et al (2012) presented the logic programming in Radial basis function neural network in single operator logic. Inevitably, the Radial basis function worked well with logic programming.…”

Section: Introductionmentioning

confidence: 99%

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

Kasihmuddin¹,

Mansor²,

Sathasivam³

2018

JSM

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Learning Logic Programming in Radial Basis Function Network via Genetic Algorithm

Cited by 38 publications

References 15 publications

Systematic Boolean Satisfiability Programming in Radial Basis Function Neural Network

Systematic Boolean Satisfiability Programming in Radial Basis Function Neural Network

Computing single step operators of logic programming in radial basis function neural networks

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

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