Improving multilayer perceptron neural network using chaotic grasshopper optimization algorithm to forecast iron ore price volatility

Ewees, Ahmed A.; Elaziz, Mohamed Abd; Alameer, Zakaria; Ye, Haiwang; Zhang, Jianhua

doi:10.1016/j.resourpol.2019.101555

Cited by 69 publications

(41 citation statements)

References 69 publications

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“…A supervised artificial neural network learning model, which typically consists of three main parts, namely an input layer, hidden layers and an output layer, was used as the MLP model in this study [ 31 ]. The input layer receives input vectors and then passes each input data point to the neurons in the hidden layer.…”

Section: Methodsmentioning

confidence: 99%

Quality Prediction for Injection Molding by Using a Multilayer Perceptron Neural Network

Ke¹,

Huang²

2020

Polymers

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Injection molding has been widely used in the mass production of high-precision products. The finished products obtained through injection molding must have a high quality. Machine parameters do not accurately reflect the molding conditions of the polymer melt; thus, the use of machine parameters leads to erroneous quality judgments. Moreover, the cost of mass inspections of finished products has led to strict restrictions on comprehensive quality testing. Therefore, an automatic quality inspection that provides effective and accurate quality judgment for each injection-molded part is required. This study proposes a multilayer perceptron (MLP) neural network model combined with quality indices for performing fast and automatic prediction of the geometry of finished products. The pressure curves detected by the in-mold pressure sensor, which reflect the flow state of the melt, changes in various indicators and molding quality, were considered in this study. Furthermore, the quality indices extracted from pressure curves with a strong correlation with the part quality were input into the MLP model for learning and prediction. The results indicate that the training and testing of the first-stage holding pressure index, pressure integral index, residual pressure drop index and peak pressure index with respect to the geometric widths were accurate (accuracy rate exceeded 92%), which demonstrates the feasibility of the proposed method.

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Section: Methodsmentioning

confidence: 99%

Quality Prediction for Injection Molding by Using a Multilayer Perceptron Neural Network

Ke¹,

Huang²

2020

Polymers

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“…The design of another effective parameter (neuron number) on the GMDH model needs another parametric study. To do this, considering the previous studies (e.g., [21]), values of 2,4,6,8,10,12,14,16,18, and 20 were selected to be used as number of neurons in the parametric study and results of GMDH models based on R 2 are shown in Table 4. Among the obtained results, as it can be seen in Table 4, GMDH model number 8 with 16 neurons shows the best prediction performance and due to that neuron number of 16 was selected in the rest of modelling process.…”

Section: Group Methods Of Data Handlingmentioning

confidence: 99%

“…In fact, they studied the power of these hybrid models in predicting gold price fluctuations and finally, they introduced WOA-ANN as a new and applicable model in this field. Ewees et al [2] proposed a hybrid intelligent model i.e., chaotic grasshopper optimization algorithm-ANN for estimation of iron ore prices and concluded that their proposed model is a promising technique for forecasting commodity prices with high accuracy. In another study of price prediction, the prediction of coal price fluctuations was considered as the objective by Alameer et al [83].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Iron ore is the basic raw material used in steel production. About 10 to 20 percent of total cost of steel is attributed to iron ore so that identification of factors affecting price of iron ore can be beneficial in controlling steel price [2]. Iron ore has previously confirmed itself to be a substantial resource in the economy and society [3].…”

Section: Introductionmentioning

confidence: 99%

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Development of a Group Method of Data Handling Technique to Forecast Iron Ore Price

Moghaddam

Monjezi

et al. 2020

Applied Sciences

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Iron is one of the most applicable metals in the world. The global price of iron ore is determined based on demand and supply. There are numerous parameters (e.g., price of steel, steel production, oil price, gold price, interest rate, inflation rate, iron production, and aluminum price) affecting the global iron ore price. Considering the high number of effective parameters and existence of complex relationship among them, artificial intelligence-based approaches can be employed to predict iron ore price. In this paper, a new intelligence system namely group method of data handling (GMDH) was developed and introduced to predict the price of iron ore. For comparison purposes, four other techniques i.e., autoregressive integrated moving average (ARIMA), support vector regression (SVR), artificial neural network (ANN), and classification and regression tree (CART) were developed for prediction of monthly iron ore price. Then, using testing datasets, the developed models were validated and their performance capacities were compared. The results showed that performance prediction of the GMDH model is significantly better than other predictive models based on four performance indices i.e., root mean square error, variance account for (VAF), mean absolute error, and mean absolute percentage error. Results of VAF (97.89%, 90.81%, 80.95%, 55.02%, and 23.87% for GMDH, SVR, ANN, CART, and ARIMA models, respectively) revealed that the GMDH technique is able to predict iron ore price with higher degree of accuracy compared to the other techniques.

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“…But, GOA has some limitations such as: 1) unbalancing between the processes of exploitation and exploration; 2) convergence speed is unstable; and 3) may be fall into the local optimum. So, there are several hybrid algorithms between GOA and other PBAs have been proposed in the literature [52][53][54][55][56][57][58][59]. In [52], the authors proposed a dynamic population quantum binary GOA based on shared knowledge and a rough set theory for the selection of features; where GOA was improved by the quantum method.…”

Section: Introductionmentioning

confidence: 99%

Hybridization of Grasshopper Optimization Algorithm With Genetic Algorithm for Solving System of Non-Linear Equations

El-Shorbagy

El-Refaey

2020

IEEE Access

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Improving multilayer perceptron neural network using chaotic grasshopper optimization algorithm to forecast iron ore price volatility

Cited by 69 publications

References 69 publications

Quality Prediction for Injection Molding by Using a Multilayer Perceptron Neural Network

Quality Prediction for Injection Molding by Using a Multilayer Perceptron Neural Network

Development of a Group Method of Data Handling Technique to Forecast Iron Ore Price

Hybridization of Grasshopper Optimization Algorithm With Genetic Algorithm for Solving System of Non-Linear Equations

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