Model and Algorithm of BP Neural Network Based on Expanded Multichain Quantum Optimization

Xu, Baoyu; Zhang, Hongjun; Zhi-teng, Wang; Wang, Huaixiao; Zhang, Youliang

doi:10.1155/2015/362150

Cited by 23 publications

(12 citation statements)

References 17 publications

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“…Backpropagation is an algorithm used to train a neural network in machine learning. Figure 5 shows the structure of the backpropagation neural network (BP neural network) [26]. In the figure, X1, X2, …, Xn are the input values for the input layer.…”

Section: Neural Network Modelmentioning

confidence: 99%

Effects of Design Factors on Drag Forces and Deformations on Marine Aquaculture Cages: A Parametric Study Based on Numerical Simulations

Dong

You

2020

JMSE

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In Japan, the marine aquaculture net cage has an important role in farming pacific bluefin tuna farming in oceans, and the design of the net cage needs to ensure robustness against hostile oceanic conditions. Accordingly, this study focuses on the drag forces and the cage volume of the net cage, and on their variations induced by different design parameters (netting solidity ratio, netting height, and bottom weight). A series of parametric studies on drag force and deformation of the net cage was conducted using a numerical simulation model. Accordingly, the contribution of each parameter to the drag and volume was analyzed using a generalized additive model. The results indicate that the bottom weight had the highest contribution to the holding ratio of the cage volume, whereas the netting height had the highest contribution to the drag coefficient of the net cage. Finally, a fast prediction model was created by a backpropagation (BP) neural network model and was examined for the accurate prediction of the objective variables.

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Section: Neural Network Modelmentioning

confidence: 99%

Effects of Design Factors on Drag Forces and Deformations on Marine Aquaculture Cages: A Parametric Study Based on Numerical Simulations

Dong

You

2020

JMSE

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“…Modeling. Suppose that the network has R nodes [33] and the transfer function of each layer is of sigmoid type [34]. e following notations are used throughout this section: α 1i denotes the output of the i-th hidden-layer node; α 2k denotes the output of the k-th output-layer node; ω 1ij and ω 2ki represent the weight between node i and node j and the weight between node k and node i, respectively; b 1i denotes the threshold for hiddenlayer node i; and b 2k denotes the threshold for output-layer node k [35].…”

Section: Bp Neural Networkmentioning

confidence: 99%

Forecasting Primary Energy Requirements of Territories by Autoregressive Integrated Moving Average and Backpropagation Neural Network Models

Pan

2019

Mathematical Problems in Engineering

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Forecasting energy data, especially the primary energy requirement, is the key part of policy-making. For those territories of different developing types, seeking a knowledge-based and dependable forecasting model is an essential prerequisite for the prosperous development of policy-making. In this paper, both autoregressive integrated moving average and backpropagation neural network models which have been proved to be very efficient in forecasting are applied to the forecasts of the primary energy consumption of three different developing types of territories. It is shown that the average relative errors between the actual data and simulated value are from 4.5% to 5.9% by the autoregressive integrated moving average and from 0.04% to 0.47% by the backpropagation neural network. Specially, this research shows that the backpropagation neural network model presents a better prediction of primary energy requirement when considering gross domestic product, population, and the particular values as predictors. Furthermore, we indicate that the single-input backpropagation neural network model can still work when the particular values have contributed most to the energy consumption.

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“…η is the learning rate of the BP neural network. e sigmoid function [55] used as an excitation function in this study is presented as follows:…”

Section: Training For the Bpnnmentioning

confidence: 99%

“…A BPNN comprises an input layer, hidden layer, and output layer [55]. e BPNN model built by the study consists of three layers of neurons, including a hidden layer, as shown in Figure 3.…”

Section: Training For the Bpnnmentioning

confidence: 99%

A Grey-BPNN Model for Evaluating the Competitiveness of Chinese Contractors in the High-Speed Rail Market in Europe

Lin

Shi

Chen

et al. 2019

Advances in Civil Engineering

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With the planning and progress of the construction of the trans-Eurasian high-speed rail (HSR) network, it becomes an important issue for Chinese contractors to enter the European HSR market. Facing the world's most competitive contractors and their high technology levels, Chinese contractors will need to know their advantages and disadvantages, so as to make necessary improvements. In this study, contractors for HSR are divided into two groups: construction contractors and rail equipment suppliers. In order to evaluate the competitiveness of HSR contractors, a Grey-BPNN model that combines the grey relational analysis and backpropagation neural network (BPNN) is proposed. e Grey-BPNN model is expected to analyze the overall competitiveness of Chinese contractors in the European HSR market and provide informative decision support for them. e study results show the following: (1) in the field of HSR construction, the competitiveness gap between the top-tier Chinese contractors and the most competitive international contractors is small. Chinese contractors' competitive advantages lie in medium-and low-technology-level projects, with a strong development potential. However, they highly depend on Chinese domestic market and lack in intangible resources, like management ability and market development ability; (2) for rail equipment suppliers, China Railway Rolling Stock Corporation (CRRC) ranks among the top-tier leaders of the international market. CRRC's greatest competitor in the European HSR market is Siemens, and CRRC is much more competitive than others in the sustainable development capability. However, CRRC needs to increase the quantity of patents and Research and Development (R&D) expenditures in transportation. As a weak transportation patent holder, CRRC has a potential risk of getting intellectual property litigations in the European HSR market.

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Model and Algorithm of BP Neural Network Based on Expanded Multichain Quantum Optimization

Cited by 23 publications

References 17 publications

Effects of Design Factors on Drag Forces and Deformations on Marine Aquaculture Cages: A Parametric Study Based on Numerical Simulations

Effects of Design Factors on Drag Forces and Deformations on Marine Aquaculture Cages: A Parametric Study Based on Numerical Simulations

Forecasting Primary Energy Requirements of Territories by Autoregressive Integrated Moving Average and Backpropagation Neural Network Models

A Grey-BPNN Model for Evaluating the Competitiveness of Chinese Contractors in the High-Speed Rail Market in Europe

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