Prediction of Natural Rubber Customs Declaration Price Based on Wavelet Decomposition and GA-BP Neural Network Group

Yi, Hongjie; Zhang, Ke; Ma, Kai-Kuang; Zhou, Lei; Tang, Futong

doi:10.3390/math10224264

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…Fuzzy logic can manage fuzzy or uncertain inputs, allowing neural networks to better handle uncertainty and ambiguity in the real world [42][43][44]. Investigating the use of neural networks to learn and establish a mapping relationship between inputs and outputs, and then employing this mapping relationship in a fuzzy control system to achieve control of fuzzy variables, would be an interesting avenue to explore [45][46][47][48][49].…”

Section: Discussionmentioning

confidence: 99%

A Hybrid Algorithm Based on Social Engineering and Artificial Neural Network for Fault Warning Detection in Hydraulic Turbines

Tan

Zhan

et al. 2023

Mathematics

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Hydraulic turbines constitute an essential component within the hydroelectric power generation industry, contributing to renewable energy production with minimal environmental pollution. Maintaining stable turbine operation presents a considerable challenge, which necessitates effective fault diagnosis and warning systems. Timely and efficient fault w arnings are particularly vital, as they enable personnel to address emerging issues promptly. Although backpropagation (BP) networks are frequently employed in fault warning systems, they exhibit several limitations, such as susceptibility to local optima. To mitigate this issue, this paper introduces an improved social engineering optimizer (ISEO) method aimed at optimizing BP networks for developing a hydraulic turbine warning system. Experimental results reveal that the ISEO-BP-based approach offers a highly effective fault warning system, as evidenced by superior performance metrics when compared to alternative methods.

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Section: Discussionmentioning

confidence: 99%

A Hybrid Algorithm Based on Social Engineering and Artificial Neural Network for Fault Warning Detection in Hydraulic Turbines

Tan

Zhan

et al. 2023

Mathematics

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“…A DBN has the ability to extract high-dimensional abstract features of traffic flows through a hidden multilayer structure, which recognizes the pattern of large-scale data efficiently. Therefore, the BP, GA-BP, SAGA-BP and DBN algorithms are used to recognize the traffic flow patterns [ 41 , 42 , 43 , 44 ]. The flow chart of each algorithm is shown in Figure 7 .…”

Section: Traffic Flow Pattern Recognition Methodsmentioning

confidence: 99%

A Novel Environment Estimation Method of Whole Sample Traffic Flows and Emissions Based on Multifactor MFD

Zang

Jiao

Song

et al. 2022

IJERPH

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Vehicle emissions seriously affect the air environment and public health. The dynamic estimation method of vehicle emissions changing over time on the road network has always been the bottleneck of air quality simulation. The dynamic traffic volume is one of the important parameters to estimate vehicle emission, which is difficult to obtain effectively. A novel estimation method of whole sample traffic volumes and emissions on the entire road network based on multifactor Macroscopic Fundamental Diagram (MFD) is proposed in this paper. First, the intelligent clustering and recognition methods of traffic flow patterns are constructed based on neural network and deep-learning algorithms. Then, multifactor MFD models are developed considering different road types, traffic flow patterns and weekday peak hours. Finally, the high spatiotemporal resolution estimation method of whole sample traffic volumes and emissions are constructed based on MFD models. The results show that traffic flow patterns are clustered efficiently by the Self-Organizing Maps (SOM) algorithm combined with the direct time-varying speed index, which describe 91.7% traffic flow states of urban roads. The Deep Belief Network (DBN) algorithm precisely recognizes 92.1% of the traffic patterns based on the speeds of peak hours. Multifactor MFD models estimate the whole sample traffic volumes with a high accuracy of 91.6%. The case study shows that the vehicle emissions are evaluated dynamically based on the novel estimation method proposed in this paper, which is conducive to the coordinated treatment of air pollution.

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“…During the training process, the neural network continuously adjusts the weights and thresholds between the input layer and the hidden layer, and between the hidden layer and the output layer. Training stops when the neural network output value is consistent with the target value or when the iteration limit is reached [5]. In addition, this algorithm also uses the steepest descent method to continuously adjust the weights and thresholds of the network through backpropagation, so as to minimize the sum of squared errors of the network.…”

Section: Bp Neural Networkmentioning

confidence: 99%

Research on Price Forecast Based on Genetic Algorithm Optimizing Neural Network

Zou,

Zheng

2023

BCPBM

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This paper chooses a number of indicators such as per capita GDP and economic level. First of all, the box diagram method is used to remove the outliers in the data, and then the Box-Cox transform is used to normalize the data. Then calculate the correlation coefficient between features, and finally determine 15 features for the construction of the model. On the basis of BP neural network, using GA algorithm to optimize the weight setting, a GA-BP model is established to predict the prices of different types of forecasting objects. Combined with the search ability of genetic algorithm optimization algorithm and the learning ability of BP neural network, the advantages of both are fully utilized to get better regression prediction results. The result of GA-BP is better than that of BP neural network, which shows that the prediction accuracy of the model is very high, in order to provide some reference for the prediction of other fields.

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Prediction of Natural Rubber Customs Declaration Price Based on Wavelet Decomposition and GA-BP Neural Network Group

Cited by 5 publications

References 15 publications

A Hybrid Algorithm Based on Social Engineering and Artificial Neural Network for Fault Warning Detection in Hydraulic Turbines

A Hybrid Algorithm Based on Social Engineering and Artificial Neural Network for Fault Warning Detection in Hydraulic Turbines

A Novel Environment Estimation Method of Whole Sample Traffic Flows and Emissions Based on Multifactor MFD

Research on Price Forecast Based on Genetic Algorithm Optimizing Neural Network

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