scholarly journals Research on Digital Economy of Intelligent Emergency Risk Avoidance in Sudden Financial Disasters Based on PSO-BPNN Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Lulu Liu
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Bp Neural Network ◽  
Financial Services ◽  
Normal Development ◽  
Particle Swarm ◽  
Digital Economy ◽  
Recent Period ◽  
Risk Avoidance ◽  
Swarm Optimization

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.

scholarly journals An Improved Method for NURBS Surface Based on Particle Swarm Optimization BP Neural Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 184656-184663
Author(s):  
Xiaoqiang Tian ◽  
Lingfu Kong ◽  
Deming Kong ◽  
Li Yuan ◽  
Dehan Kong
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Bp Neural Network ◽  
Particle Swarm ◽  
Nurbs Surface ◽  
Improved Method ◽  
Swarm Optimization

Application of BP Neural Network Based on Immune Particle Swarm Optimization for Fault Diagnosis of Power Transformer

2013 ◽  
Vol 448-453 ◽  
pp. 3605-3609
Author(s):  
Yu Xin Zhang ◽  
Yu Liu
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Power Transformer ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Network Training ◽  
Gradient Descent Algorithm ◽  
Ipso Algorithm

Cloing and hypermutation of immune theory were used in optimization on particle swarm optimization (PSO), an immune particle swarm optimization (IPSO) algorithm was proposed , which overcome the problem of premature convergence on PSO. IPSO was used in BP Neural Network training to overcome slow convergence speed and easily getting into local dinky value of gradient descent algorithm. BP Neural Network trained by IPSO was used to fault diagnosis of power transformer, it has high accuracy after experimental verification and to meet the power transformer diagnosis engineering requirements.

Study on Chaotic Time Series Prediction Algorithm for Kent Mapping Based on Particle Swarm Optimization

2014 ◽  
Vol 511-512 ◽  
pp. 941-944 ◽  
Author(s):  
Hong Li Bian
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Particle Swarm Optimization ◽  
Bp Neural Network ◽  
Time Series Prediction ◽  
Particle Swarm ◽  
Chaotic Time Series ◽  
Learning Ability ◽  
Swarm Optimization ◽  
Chaotic Time Series Prediction

Based on the particle swarm optimization (PSO) and BP neural network (BPNN), an algorithm for BP neural network optimized particle swarm optimization (PSOBPNN) is proposed. In the algorithm, PSO is used to obtain better network initial threshold and weight to compensate the defect of connection weight and thresholds of BPNN, thus it can make BPNN have faster convergence and greater learning ability. The efficiency of the proposed prediction method is tested by the simulation of the chaotic time series for Kent mapping. The simulations results show that the proposed method has higher forecasting accuracy compared with the BPNN, so it is proved that the algorithm is feasible and effective in the chaotic time series prediction.

Application BP Neural Network in the Speaker Recognition Based on Chaos Particle Swarm Optimization Algorithm

2013 ◽  
Vol 765-767 ◽  
pp. 2805-2808
Author(s):  
Guo Wen Wang ◽  
Shi Xin Luo ◽  
Li He ◽  
Gang Yin
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Bp Neural Network ◽  
Speaker Recognition ◽  
Particle Swarm Optimization Algorithm ◽  
Recognition Rate ◽  
Particle Swarm ◽  
Bp Network ◽  
Swarm Optimization

According to the question that BP Neural Network has slow velocity of convergence and is apt to fall into the minimum value, chaos thought is adopted in the particle swarm optimization (PSO). For this, chaos particle swarm optimization algorithm, which improve the ability of getting rid of fractional extreme point in the PSO, is presented and applied to the BP network exercise so that the calculation accuracy and velocity of convergence of BP network are increased. The method of training the BP network for speaker recognition, the recognition rate and speed of training have been greatly improved, making the speaker recognition based on BP neural network to get better results.

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