Model Predictive Control of Nonlinear System Based on GA‐RBP Neural Network and Improved Gradient Descent Method

Wang, Youming; Qing, Didi

doi:10.1155/2021/6622149

Cited by 13 publications

(10 citation statements)

References 51 publications

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“…Literature [ 16 ] points out that when the data noise has too much influence, it will become more difficult to train neural network, and the network will easily fall into local minimum. Literature [ 17 ] pointed out that because gradient descent algorithm iterates weights and thresholds, the solution of BPNN will be forced to separate into local minimum. To solve this problem, many scholars have suggested some methods to optimize the primary relationship weights and thresholds of BPNN.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Research on Digital Economy of Intelligent Emergency Risk Avoidance in Sudden Financial Disasters Based on PSO‐BPNN Algorithm

Liu

2021

Computational Intelligence and Neuroscience

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In recent years, disasters have seriously affected the normal development of financial business in some regions. At the time of disaster, how to effectively integrate resources of all parties, deal with sudden financial disasters efficiently, and restore financial services in time has become an important task. Therefore, this paper adopts Particle Swarm Optimization (PSO) to improve the traditional BP Neural Network (BPNN) and finally constructs a Particle Swarm Optimization powered BP Neural Network (PSO-BPNN) model for the intelligent emergency risk avoidance of sudden financial disasters in digital economy. At the same time, the proposed algorithm is also compared to GA-BPNN and BPNN algorithms, which are also intelligent algorithms. Experimental results show that the hybrid PSO-BPNN algorithm is superior to GA-BPNN algorithm and BPNN algorithm in simulation and prediction effect. It can accurately predict the sudden financial disaster in recent period, so the model has a good application prospect.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Research on Digital Economy of Intelligent Emergency Risk Avoidance in Sudden Financial Disasters Based on PSO‐BPNN Algorithm

Liu

2021

Computational Intelligence and Neuroscience

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“…Literature [20] innovatively studies the relationship between national culture and hotel management and demonstrates the role of national culture in hotel management. Literature [21] uses a large number of research methods to evaluate the efficiency of hotel management.…”

Section: Introductionmentioning

confidence: 99%

Research on Prediction Model of Hotels’ Development Scale Based on BP Artificial Neural Network Algorithm

Zhao

Tsai

2021

Mathematical Problems in Engineering

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Due to the lack of macro and systematic data, the target cost of high-star hotel project cannot meet the characteristics and needs of the hotel project itself. Therefore, the establishment of star hotel development scale prediction is urgent. In the scale development strategy, based on the previous studies, combined with the development characteristics of regional high-star hotels in a city, this paper constructs the index system of influencing factors of the development scale of high-star hotels and extracts the main influencing factors of hotel development scale by principal component analysis and partial relationship analysis, which are mainly urban development, economic development, tourism development, tourism development exhibition industry development, business development, and transportation development. The BP artificial neural network prediction method is used to establish a prediction model for the development scale of high-star hotels, by adopting the above key extraction factors as input of BP neural network. Through the input and output of the scale influence index data, the development scale of star hotels is accurately predicted. The simulation results verify the effectiveness and reliability of the star hotel development scale prediction strategy based on BP neural network, in terms of accuracy and model superiority.

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“…The neural network training problem can be reduced by finding the weight space to find the weight that can make the objective function reach the overall minimum. The main disadvantage of the gradient descent method is that it is easy to fall into a local minimum [8,9].…”

Section: Bp Neural Networkmentioning

confidence: 99%

The Prediction Model and System of Stock Rise and Fall Based on BP Neural Network

2022

AJBM

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The purpose of forecasting the rise and fall of the stock market is for investors to analyze the future stock price trends, trends and other information, and combine the characteristics of the stocks to choose appropriate methods to make trading decisions. The purpose of this paper to study the stock price prediction model is to improve the preventive measures against stock risks. This article mainly uses the experimental method and the comparative method to conduct an experimental research on the stock price fluctuation forecast model. Experimental results show that the accuracy of BP neural network algorithm in stock prediction can reach 91.5%. This means that the stock price prediction model based on BP neural network has certain feasibility, and its research value is also relatively large.

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Model Predictive Control of Nonlinear System Based on GA‐RBP Neural Network and Improved Gradient Descent Method

Cited by 13 publications

References 51 publications

[Retracted] Research on Digital Economy of Intelligent Emergency Risk Avoidance in Sudden Financial Disasters Based on PSO‐BPNN Algorithm

[Retracted] Research on Digital Economy of Intelligent Emergency Risk Avoidance in Sudden Financial Disasters Based on PSO‐BPNN Algorithm

Research on Prediction Model of Hotels’ Development Scale Based on BP Artificial Neural Network Algorithm

The Prediction Model and System of Stock Rise and Fall Based on BP Neural Network

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