An Ant-Lion Optimizer-Trained Artificial Neural Network System for Chaotic Electroencephalogram (EEG) Prediction

Köse, Utku

doi:10.3390/app8091613

Cited by 50 publications

(26 citation statements)

References 110 publications

(142 reference statements)

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“…A so-called function "roulette wheel selection (RWS)" is applied for this purpose. Further details of the ALO and the mathematical optimization process are detailed in other studies, such as [48][49][50].…”

Section: Ant Lion Optimizationmentioning

confidence: 99%

“…The SHO was similarly The applicability of the applied algorithms has been previously demonstrated for other optimization aims. In research by Kose [49] and Francis and Meganathan [60], for example, the ALO was used to improve the performance of the ANN and ANFIS, respectively. The SHO was similarly used by Li et al [61] to train a feed-forward neural network for a heart-related classification problem.…”

Section: Quality Assessment Of Predictive Modelsmentioning

confidence: 99%

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Spotted Hyena Optimizer and Ant Lion Optimization in Predicting the Shear Strength of Soil

et al. 2019

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Two novel hybrid predictors are suggested as the combination of artificial neural network (ANN), coupled with spotted hyena optimizer (SHO) and ant lion optimization (ALO) metaheuristic techniques, to simulate soil shear strength (SSS). These algorithms were applied to the ANN for counteracting the computational drawbacks of this model. As a function of ten key factors of the soil (including depth of the sample, percentage of sand, percentage of loam, percentage of clay, percentage of moisture content, wet density, liquid limit, plastic limit, plastic Index, and liquidity index), the SSS was considered as the response variable. Followed by development of the ALO–ANN and SHO–ANN ensembles, the best-fitted structures were determined by a trial and error process. The results demonstrated the efficiency of both applied algorithms, as the prediction error of the ANN was reduced by around 35% and 18% by the ALO and SHO, respectively. A comparison between the results revealed that the ALO–ANN (Error = 0.0619 and Correlation = 0.9348) performs more efficiently than the SHO–ANN (Error = 0.0874 and Correlation = 0.8866). Finally, an SSS predictive formula is presented for use as an alternative to the difficult traditional methods.

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Section: Ant Lion Optimizationmentioning

confidence: 99%

Section: Quality Assessment Of Predictive Modelsmentioning

confidence: 99%

Spotted Hyena Optimizer and Ant Lion Optimization in Predicting the Shear Strength of Soil

et al. 2019

View full text Add to dashboard Cite

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“…Finally, in the third stage, the output is predicted according to weights between the output units and hidden units. One of the most important actions and advantages of an ANN is the discovery of nonlinear, statistical data, complex relationships between input and output parameters, and its use in a variety of science and engineering applications, particularly recently [31,[42][43][44][45][46][47]. To achieve this goal, a sufficient number of dataset samples are required to train the ANN with a suitable algorithm.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Energy Evaluation of Triggering Soil Liquefaction Based on the Response Surface Method

Tang

Yang

2019

Applied Sciences

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Liquefaction is one of the most destructive phenomena caused by earthquakes, and it has been studied regarding the issues of risk assessment and hazard analysis. The strain energy approach is a common method to evaluate liquefaction triggering. In this study, the response surface method (RSM) is applied as a novel way to develop six new strain energy models in order to estimate the capacity energy required for triggering liquefaction (W), based on laboratory test results collected from the literature. Three well-known design of experiments (DOEs) are used to build these models and evaluate their influence on the developed equations. Furthermore, two groups of artificial neural network (ANN) and RSM models are derived to investigate the complicated influence of fine content (FC). The first group of models is based on a database without limitation on the range of input parameters, and the second group is based on a database with FC lower than the critical value of 28%. The capability and accuracy of the six presented models are compared with four existing models in the literature by using additional new laboratory test results (i.e., 20 samples). The results indicate the superior performance of the presented RSM models and particularly the second group of the models based on a limited value of FC.

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“…A literature review showed that various metaheuristic algorithms have been successfully employed for optimizing the neural computing models, i.e., genetic algorithm [42], particle swarm optimization [43], differential evolution [44], artificial bee colony (ABC) [45], cuckoo search [46], differential flower pollination [47], and ant-lion optimizer [48]. Herein, the weights of the neural computing models were searched and optimized.…”

Section: Introductionmentioning

confidence: 99%

A New Approach of Hybrid Bee Colony Optimized Neural Computing to Estimate the Soil Compression Coefficient for a Housing Construction Project

et al. 2019

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In the design phase of housing projects, predicting the settlement of soil layers beneath the buildings requires the estimation of the coefficient of soil compression. This study proposes a low-cost, fast, and reliable alternative for estimating this soil parameter utilizing a hybrid metaheuristic optimized neural network (NN). An integrated method of artificial bee colony (ABC) and the Levenberg–Marquardt (LM) algorithm is put forward to train the NN inference model. The model is capable of delivering the response variable of soil compression coefficient a set of physical properties of soil. A large-scale real-life urban project at Hai Phong city (Vietnam) was selected as a case study. Accordingly, a dataset of 441 samples with their corresponding testing values of the compression coefficient has been collected and prepared during the construction phase. Experimental outcomes confirm that the proposed NN model with the hybrid ABC-LM training algorithm has attained the highly accurate estimation of the soil compression coefficient with root mean square error (RMSE) = 0.008, mean absolute percentage error (MAPE) = 10.180%, and coefficient of determination (R2) = 0.864. Thus, the proposed machine learning method can be a promising tool for geotechnical engineers in the design phase of housing projects.

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An Ant-Lion Optimizer-Trained Artificial Neural Network System for Chaotic Electroencephalogram (EEG) Prediction

Cited by 50 publications

References 110 publications

Spotted Hyena Optimizer and Ant Lion Optimization in Predicting the Shear Strength of Soil

Spotted Hyena Optimizer and Ant Lion Optimization in Predicting the Shear Strength of Soil

Energy Evaluation of Triggering Soil Liquefaction Based on the Response Surface Method

A New Approach of Hybrid Bee Colony Optimized Neural Computing to Estimate the Soil Compression Coefficient for a Housing Construction Project

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