Artificial neural network intelligent method for prediction

Trifonov, Roumen; Yoshinov, Radoslav; Pavlova, Galya; Tsochev, Georgi

doi:10.1063/1.4996678

Cited by 15 publications

(3 citation statements)

References 2 publications

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“…ANNs were established to imitate the architecture of the biological brain of humankind [ 27 ]. They are able to discover the nonlinear relationship in the input data set [ 28 ].…”

Section: Methodsmentioning

confidence: 99%

ANFIS and ANN models to predict heliostat tracking errors

Sarr

Thiam

Dieng

2023

Heliyon

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“…ANNs were established to imitate the architecture of the biological brain of humankind [ 27 ]. They are able to discover the nonlinear relationship in the input data set [ 28 ].…”

Section: Methodsmentioning

confidence: 99%

ANFIS and ANN models to predict heliostat tracking errors

Sarr

Thiam

Dieng

2023

Heliyon

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“…al. [23] suggested that the optimum number of neurons in the hidden layer could be estimated by N/2, where N is the number of input variables or experimental data. The tangent sigmoid transfer function (tansig) and linear transfer function (purelin) were applied to the hidden layer and output layer, respectively.…”

Section: Neural Network Modelingmentioning

confidence: 99%

Statistical Modeling and Optimization of Process Parameters for 2,4-Dichlorophenoxyacetic Acid Removal by Using AC/PDMAEMA Hydrogel Adsorbent: Comparison of Different RSM Designs and ANN Training Methods

Dahlan

Azhar

Hassan

et al. 2022

Water

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In this study, the response surface methodology (RSM) and artificial neural network (ANN) were employed to study the adsorption process of 2,4-dichlorophenoxyacetic acid (2,4-D) by using modified hydrogel, i.e., activated carbon poly(dimethylaminoethyl methacrylate) (AC/PDMAEMA hydrogel). The effect of pH, the initial concentration of 2,4-D and the activated carbon content on the removal of 2,4-D and adsorption capacity were investigated through the face-centered composite design (FCCD), optimal design and two-level factorial design. The response surface plot suggested that higher removal of 2,4-D and adsorption capacity could be achieved at the higher initial concentration of 2,4-D and lower pH and activated carbon content. The modeling and optimization for the adsorption process of 2,4-D were also carried out by different design methods of RSM and different training methods of ANN. It was found that among the three design methods of RSM, the optimal design has the highest accuracy for the prediction of 2,4-D removal and adsorption capacity (R2 = 0.9958 and R2 = 0.9998, respectively). The numerical optimization of the optimal design found that the maximum removal of 2,4-D and adsorption capacity of 65.01% and 65.29 mg/g, respectively, were obtained at a pH of 3, initial concentration of 2,4-D of 94.52 mg/L and 2.5 wt% of activated carbon. Apart from the optimization of process parameters, the neural network architecture was also optimized by trial and error with different numbers of hidden neurons in the layers to obtain the best performance of the response. The optimization of the neural network was performed with different training methods. It was found that among the three training methods of the ANN model, the Bayesian Regularization method had the highest R2 and lowest mean square error (MSE) with the optimum network architecture of 3:9:2. The optimum condition obtained from RSM was also simulated with the optimized neural network architecture to validate the responses and adequacy of the RSM model.

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“…Trifonov conducted a study on the architecture of a neural network which used four different technical indicators [6]. For UTOMO, improving optimization in stock price predictions can be done using a gradientbased back propagation neural network approach.…”

Section: Literature Reviewmentioning

confidence: 99%

Utilizing Neural Networks for Stocks Prices Prediction in Stocks Markets

Mahedy¹,

Abdelsalam²,

Mohamed³

et al. 2020

IJITCS

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The neural networks, AI applications, are effective prediction methods. Therefore, in the current research a prediction system was proposed using these neural networks. It studied the technical share indices, viewing price not only as a function of time, but also as a function depending on several indices among which were the opening and closing, top and bottom trading session prices or trading volume. The above technical indices of a number of Egyptian stock market shares during the period from 2007 to 2017, which can be used for training the proposed system, were collected and used as follows: The data were divided into two sets. The first one contained 67% of the total data and was used for training neural networks and the second contained 33% and was used for testing the proposed system. The training set was segmented into subsets used for training a number of neural networks. The output of such networks was used for training another network hierarchically. The system was, then, tested using the rest of the data.

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Artificial neural network intelligent method for prediction

Cited by 15 publications

References 2 publications

ANFIS and ANN models to predict heliostat tracking errors

ANFIS and ANN models to predict heliostat tracking errors

Statistical Modeling and Optimization of Process Parameters for 2,4-Dichlorophenoxyacetic Acid Removal by Using AC/PDMAEMA Hydrogel Adsorbent: Comparison of Different RSM Designs and ANN Training Methods

Utilizing Neural Networks for Stocks Prices Prediction in Stocks Markets

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