A hybrid simultaneous perturbation artificial bee colony and back-propagation algorithm for training a local linear radial basis neural network on ore grade estimation

Jafrasteh, Bahram; Fathianpour, Nader

doi:10.1016/j.neucom.2017.01.016

Cited by 57 publications

(22 citation statements)

References 40 publications

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“…In the second stage, the output weight between the hidden layer and the output layer will be obtained. Several approaches such as k-means clustering [8], decision tree [9] and metaheuristics algorithm [10] have been utilized to optimize the two stages. Despite the advantages of RBFNN in solving a various real-life problem, the learning structure of RBFNN, which produces the desired outcome, is poorly understood.…”

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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“…Yu and Duan [41] introduced the hybrid ABC combined with Fuzzy C means Clustering into RBFNN to improve the image fusion accuracy. There are many studies that used the hybrid ABC with RBFNN as a model in many applications [42,43], including solving well-known datasets [44]. Jiang et al [45] employed ABC to optimize parameters in RBFNN and projected the ecological pressure.…”

Section: Introductionmentioning

confidence: 99%

Artificial Immune System in Doing 2-Satisfiability Based Reverse Analysis Method via a Radial Basis Function Neural Network

Alzaeemi

Sathasivam

2020

Processes

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A radial basis function neural network-based 2-satisfiability reverse analysis (RBFNN-2SATRA) primarily depends on adequately obtaining the linear optimal output weights, alongside the lowest iteration error. This study aims to investigate the effectiveness, as well as the capability of the artificial immune system (AIS) algorithm in RBFNN-2SATRA. Moreover, it aims to improve the output linearity to obtain the optimal output weights. In this paper, the artificial immune system (AIS) algorithm will be introduced and implemented to enhance the effectiveness of the connection weights throughout the RBFNN-2SATRA training. To prove that the introduced method functions efficiently, five well-established datasets were solved. Moreover, the use of AIS for the RBFNN-2SATRA training is compared with the genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), and artificial bee colony (ABC) algorithms. In terms of measurements and accuracy, the simulation results showed that the proposed method outperformed in the terms of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Schwarz Bayesian Criterion (SBC), and Central Process Unit time (CPU time). The introduced method outperformed the existing four algorithms in the aspect of robustness, accuracy, and sensitivity throughout the simulation process. Therefore, it has been proven that the proposed AIS algorithm effectively conformed to the RBFNN-2SATRA in relation to (or in terms of) the average value of training of RMSE rose up to 97.5%, SBC rose up to 99.9%, and CPU time by 99.8%. Moreover, the average value of testing in MAE was rose up to 78.5%, MAPE—rose up to 71.4%, and was capable of classifying a higher percentage (81.6%) of the test samples compared with the results for the GA, DE, PSO, and ABC algorithms.

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“…Because of the good nonlinear prediction capabilities, back-propagation (BP) neural networks have been widely used [21]. A novel bee colony algorithm was used to optimize a BP neural network in [22]. A BP-based detection algorithm was proposed for unmanned aerial vehicle systems in [23].…”

Section: Introductionmentioning

confidence: 99%

GWO-BP Neural Network Based OP Performance Prediction for Mobile Multiuser Communication Networks

Wang

Lin

et al. 2019

IEEE Access

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The complexity and variability of wireless channels makes reliable mobile multiuser communications challenging. As a consequence, research on mobile multiuser communication networks has increased significantly in recent years. The outage probability (OP) is commonly employed to evaluate the performance of these networks. In this paper, exact closed-form OP expressions are derived and an OP prediction algorithm is presented. Monte-Carlo simulation is used to evaluate the OP performance and verify the analysis. Then, a grey wolf optimization back-propagation (GWO-BP) neural network based OP performance prediction algorithm is proposed. Theoretical results are used to generate training data. We also examine the extreme learning machine (ELM), locally weighted linear regression (LWLR), support vector machine (SVM), BP neural network, and wavelet neural network methods. Compared to the wavelet neural network, LWLR, SVM, BP, and ELM methods, the results obtained show that the GWO-BP method provides the best OP performance prediction. INDEX TERMS Mobile multiuser communication, outage probability, performance prediction, GWO-BP neural network.

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A hybrid simultaneous perturbation artificial bee colony and back-propagation algorithm for training a local linear radial basis neural network on ore grade estimation

Cited by 57 publications

References 40 publications

Systematic Boolean Satisfiability Programming in Radial Basis Function Neural Network

Systematic Boolean Satisfiability Programming in Radial Basis Function Neural Network

Artificial Immune System in Doing 2-Satisfiability Based Reverse Analysis Method via a Radial Basis Function Neural Network

GWO-BP Neural Network Based OP Performance Prediction for Mobile Multiuser Communication Networks

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