Service provider portfolio selection for project management using a BP neural network

Bai, Libiao; Zheng, Kanyin; Wang, Zhiguo; Liu, Jiale

doi:10.1007/s10479-020-03878-0

Cited by 20 publications

(18 citation statements)

References 56 publications

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“…d represents the network sample data [29]. Firstly, the designed RS-BP model is trained, and the model's performance is evaluated.…”

Section: Multisensor Controllable Technology Incorporatingmentioning

confidence: 99%

The Influence of Multisensor Fusion Machine Learning on the Controllable Fabrication of MOF (UIO-66)/ZrAl Ceramic Composite Membranes

Shao

et al. 2022

Journal of Sensors

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This study is aimed at improving the utilization efficiency of resources and enhancing the experiments’ effect of various composite membrane research. Firstly, the meaning and preparation process of Metal-Organic Frameworks (MOFs) are discussed. Then, the theoretical knowledge of fusing machine learning and multisensor technology is outlined. Finally, based on the controllable fabrication concept of MOF [UIO- (Universitetet I Oslo-) 66]/ZrAl ceramic composite membranes, a multisensor model incorporating machine learning is designed. The results show that the designed radial sensor backpropagation (RS-BP) fusion multisensor model has the highest error rate of about 0.87. When the number of training is about 100 times, the model’s error rate tends to be stable, and the minimum error rate is about 0.01. Secondly, the maximum adsorption capacity of the composite membrane under the controllable preparation of the model is 800 cm3/g Spanning Tree Protocol (STP). Additionally, the adsorption capacity decreases slowly, and the overall adsorption energy is higher than that of the traditional preparation method. Finally, the catalytic efficiency of membranes prepared by fusing multiple sensors is 90%-97%. The research achieves innovation in technology and improves the feasibility of rational application of MOF (UIO-66)/ZrAl ceramic composite membranes. This study not only provides technical support for the development of machine learning fusion multisensing technology but also contributes to the comprehensive improvement of the resource utilization effect.

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“…d represents the network sample data [29]. Firstly, the designed RS-BP model is trained, and the model's performance is evaluated.…”

Section: Multisensor Controllable Technology Incorporatingmentioning

confidence: 99%

The Influence of Multisensor Fusion Machine Learning on the Controllable Fabrication of MOF (UIO-66)/ZrAl Ceramic Composite Membranes

Shao

et al. 2022

Journal of Sensors

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show abstract

“…If the number is too small, the hidden layer cannot fully learn all the data features, which will affect the prediction results of the output layer. On the contrary, if there are too many neurons in the hidden layer, it may reduce the training speed and lead to overfitting [15]. Therefore, it is very important to determine the appropriate number of hidden layer neurons.…”

Section: Genetic Algorithm To Optimize Bp Neural Network 31 Bp Neural...mentioning

confidence: 99%

“…The number of neurons is represented by A, B, and N in the input, output, and hidden layers, respectively. Constant C∈ [1,10].According to the established model, there are 20 neurons in the input layer, that is, A=20, and 1 neuron in the output layer, that is, B=1.Then, N∈ [6,15].…”

Section: Genetic Algorithm To Optimize Bp Neural Network 31 Bp Neural...mentioning

confidence: 99%

Safety risk assessment of green building construction based on BP neural network optimized by genetic algorithm

zhang,

wang

2023

Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023)

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This study examines the assessment of construction safety risks in green building risk management. By comprehensively considering all factors that affect the safety of green building construction, an evaluation index system is established. The AHP method is used to weigh each index, expert scoring determines the risk level of each project index, and the weighted summation results in the expected output value. In addition, the genetic algorithm (GA) is introduced to optimize the BP neural network, and an evaluation model based on AHP and GA-BP neural network is established that can be used to predict and evaluate construction safety risks. The results show that this method can effectively predict the safety risk of green building construction and provide valuable guidance for the safety management of green building construction.

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“…Meanwhile, as the link between knowledge utilization and creation, the academic field is flooded with a large amount of knowledge and recommendation systems need to be introduced to solve the information overload problem. Increased platforms are providing services for human beings through the Internet, almost all over the clothing, food, and housing of people's lives [ 1 ]. Since the birth of the Internet, the development of related technologies has been advancing all the time, and people can access more information by going online to fully enjoy the convenience of the Internet.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Quantitative and Biological Neural Network Optimization Model of Sports Industry Structure Based on Knowledge Mapping

Wang

Gao

Li³

2022

Computational Intelligence and Neuroscience

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In this paper, a comprehensive quantitative and biological neural network optimization model of sports industry structure is thoroughly studied and analyzed using knowledge graphs. To address the problems of poor performance interpretability deficiency of knowledge graph-based recommendation methods in the face of relational sparse graphs, a pretraining-based implicit characterization algorithm strategy is proposed for the recall stage, which can solve the problems of difficulty in going online and high delay in the recall stage of the recommendation system while improving the accuracy, and not only this can be applied in the recall stage, but also the sorting and postsorting modules can be used as features. To study the relationship between signaling activity and energy metabolism of pyramidal neurons, an empirical model of the synaptic vesicle cycle is proposed to simulate the synaptic transmission process, the role played by energy metabolism in synaptic transmission is studied from the perspective of feedback control, and the quantitative relationship between neuronal pulse discharge frequency, energy consumption, and information quantity in dendritic integration is analyzed using the cable theory and atrial chamber model. It was found that, when 0 ≤ ε ≤ 0.6 , the chaotic region shrinks and eventually disappears with the increase of the memory factor ε ; however, when 0.6 ≤ ε ≤ 1 is used, chaos is recreated and the chaotic area gradually increases with the increase of the memory factor ε . This paper conducts comparative experiments on data sets in the recommendation domain and verifies that the proposed model and the feature intersection module can effectively perform feature interaction between items and entities, thus enhancing the recommendation effect.

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Service provider portfolio selection for project management using a BP neural network

Cited by 20 publications

References 56 publications

The Influence of Multisensor Fusion Machine Learning on the Controllable Fabrication of MOF (UIO-66)/ZrAl Ceramic Composite Membranes

The Influence of Multisensor Fusion Machine Learning on the Controllable Fabrication of MOF (UIO-66)/ZrAl Ceramic Composite Membranes

Safety risk assessment of green building construction based on BP neural network optimized by genetic algorithm

A Comprehensive Quantitative and Biological Neural Network Optimization Model of Sports Industry Structure Based on Knowledge Mapping

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