PSO-BP Algorithm Research Articles

Research on the HHO improved BP neural network fault prediction algorithm for distribution networks based on weather factors

Abstract Weather factors have a substantial impact on the operation and maintenance of the distribution network, but the current system fault prediction platform mainly focuses on the operating characteristics of the system, that is, the internal parameters, and rarely considers the external parameters. Firstly, by studying the fault data of the distribution network in a city in northern China caused by weather factors, this paper obtains the main weather factors affecting the reliability of power supply in this area. Then, the Harris Hawks optimization (HHO) improved BP neural network algorithm is used to establish the main reliability evaluation models suitable for this region, and based on this, the fault probability of this region is predicted under the target weather. Finally, the prediction results are compared with the actual fault data caused by weather factors in this area. The results show that both the PSO-BP algorithm and HHO-BP neural network algorithm achieve better prediction accuracy than the BP neural network algorithm, and the HHO-BP algorithm has better convergence speed.

Cloud inversion analysis of surrounding rock parameters for underground powerhouse based on PSO-BP optimized neural network and web technology

Aiming at the shortcomings of the BP neural network in practical applications, such as easy to fall into local extremum and slow convergence speed, we optimized the initial weights and thresholds of the BP neural network using the particle swarm optimization (PSO). Additionally, cloud computing service, web technology, cloud database and numerical simulation were integrated to construct an intelligent feedback analysis cloud program for underground engineering safety monitoring based on the PSO-BP algorithm. The program could conveniently, quickly, and intelligently carry out numerical analysis of underground engineering and dynamic feedback analysis of surrounding rock parameters. The program was applied to the cloud inversion analysis of the surrounding rock parameters for the underground powerhouse of the Shuangjiangkou Hydropower Station. The calculated displacement simulated with the back-analyzed parameters matches the measured displacement very well. The posterior variance evaluation shows that the posterior error ratio is 0.045 and the small error probability is 0.999. The evaluation results indicate that the intelligent feedback analysis cloud program has high accuracy and can be applied to engineering practice.

Prediction and control of profile for silicon steel strip in the whole tandem cold rolling based on PSO-BP algorithm

In the rolling process of silicon steel strip of the five-stand UCMW tandem cold rolling line, the quality of finished profile is the result of cooperative control of five stands, the exit profiles of the first four stands jointly decide the exit profile of the fifth stand, so it is necessary to accurately control the strip profile in the rolling process. However, the shapemeter is only typically equipped at the exit of fifth stand, making it impossible to obtain the strip profile of the first four stands. A profile prediction model for the whole tandem cold rolling based on PSO-BP algorithm is established, compared with the finite element method, this model can satisfy the demand for fast prediction of strip profile and quickly analyzing the profile change characteristics for five stands according to the adjustment of profile control parameters in the industrial field. Firstly, a 3D elastic-plastic finite element model of rolls and strip for five stands is built, and a large number of profile calculation data are as the training sample sets to build the PSO-BP model. Through the transfer calculation of strip profile between adjacent stands, the PSO-BP model of five stands is established, and the validity is verified by comparing with the actual measured data. Secondly, to improve the quality of product profile, focusing on the unreasonable setting of production parameters, many improvement schemes of WRB and WRS for five stands are designed, and the PSO-BP model is used to quickly calculate the strip profile, then comparing the result of each scheme, the optimum improvement scheme is selected, which is furtherly carried out rolling experiments in the industrial field, and the quality of strip profile is well improved. At present, the new scheme has been successfully applied to industrial production.

BACK ANALYSIS AND STABILITY PREDICTION OF SURROUNDING ROCK DURING EXCAVATION OF THE SHUANGJIANGKOU UNDERGROUND POWERHOUSE

The underground powerhouse of the Shuangjiangkou hydropower station is one of the largest caverns under construction in China, and its stability during construction is crucial for safe construction. To study the stability of the surrounding rock during excavation, the displacement and stress of the surrounding rock were monitored by multi-point displacement meters and bolt stress meters. Based on the monitoring data, the elastic modulus, Poisson’s ratio, friction angle, and cohesion of surrounding rock were inversely analyzed by the PSO-BP algorithm. Then, the back-analyzed parameters were used to simulate the subsequent excavations and predict the stability of surrounding rock during the following construction. The analysis results show that the surrounding rocks were generally stable during the initial four stages of excavation, and the main factors affecting their stability were blasts and unfavorable geological structures, including the lamprophyre vein and the F1 fault. These unfavorable geological structures also significantly decrease the mechanical parameters of surrounding rock as demonstrated by back analysis, and the stability prediction results show that the omnibus bar cave and the tailrace tunnel were at the greatest risk of instability during the subsequent excavations. This study provides a practical analysis for engineering excavation of the underground caverns.

Retraction Note: Combinatorial optimization analysis of the production process of C4 olefins from ethanol based on the PSO–BP algorithm

Retraction Note: Combinatorial optimization analysis of the production process of C4 olefins from ethanol based on the PSO–BP algorithm

KOA massage robot: A Study on The Reduction of TCM Manipulation Based on PSO-BP Algorithm

KOA massage robot: A Study on The Reduction of TCM Manipulation Based on PSO-BP Algorithm

Information technology system to promote drug production command supervision

Abstract Drug quality problems caused by problems in drug production processes generally cannot be effectively controlled by current drug quality standards, leading to potential safety risks for drugs. This paper applies the information system to promote the command and control of drug production to the quality control of the drug production process. By using five key quality technology points affecting drug quality as the input of the improved PSO-BP algorithm for network training based on the GMP (Good Manufacturing Practice) standard, the output of the classification prediction network is used to determine whether the drug is qualified or not to achieve the purpose of drug quality control. The parameters of the BP algorithm are optimized to minimize the output error after the PSO algorithm is improved by invoking the linear variational operator. The results show that the average absolute error and the average relative error of the improved PSOBP algorithm are 0.129 and 1.86%, respectively, and the average absolute error and the average relative error of the PSOBP algorithm are 0.694 and 8.28%, respectively, compared with the PSO-BP algorithm, the error of the improved PSOBP algorithm is effectively reduced. The improved PSO-BP algorithm proposed in this paper can be used for drug production command supervision, and it effectively reduces the inferiority rate of drug production and fundamentally eliminates the circulation market of inferior drugs.

Voltage Management of Distributed Photovoltaic Cluster Connected to a Distribution Network

The proportion of distributed photovoltaic grid connection in the distribution network is gradually increasing, and its characteristics of high volatility and poor stability have brought new challenges to the overvoltage problem faced by the distribution network structure and safe and stable optimization operation. This paper mainly studies clustering and reactive power optimization control of distributed Photovoltaic. The system architecture of the cloud edge is constructed, and the voltage autonomy strategy within the region after partition is proposed. The model is established with the total voltage offset and the minimum line loss as targets. Adopting adaptive PSO-BP algorithm model for optimization solution. Finally, conduct simulation analysis on the MATLAB platform. We can see that the proposed method can not only reduce the uneven voltage distribution, network loss, but also reduce the calculation burden of the controller has a positive effect.

RETRACTED ARTICLE: Combinatorial optimization analysis of the production process of C4 olefins from ethanol based on the PSO–BP algorithm

RETRACTED ARTICLE: Combinatorial optimization analysis of the production process of C4 olefins from ethanol based on the PSO–BP algorithm

Influence Analysis of Rock Mass Mechanical Properties on Tunnel Rock Stability Based on Sensor Data Integration

This study takes the multisensor monitoring data of tunnel support structure and surrounding rock mechanical properties during TBM construction as the research object, integrates the monitoring data of multisensor through an intelligent optimization algorithm, and explores the self‐consistent fusion characteristics between the multisensor structural mechanical properties and the field monitoring data. At the same time, the support structure parameters and construction progress in the tunnel construction process are dynamically adjusted and scientifically integrated through the information feedback of monitoring data. The research results are as follows: (1) The tunnel excavation rate is different under different rock strengths and surrounding rock levels. The overall performance is that the harder the surrounding rock, the lower the surrounding rock level, and the faster the tunnel excavation rate. The relationship between the FPI parameters of surrounding rock and the deformation of tunnel surrounding rock is closer, and the correlation coefficient reaches 98%. The mechanical properties of the surrounding rock have the greatest influence on its deformation. (2) The rotation speed, thrust, and torque of TBM machinery as well as the surrounding rock workability index and the tunnel surrounding rock deformation maintain a good fitting relationship, with a fitting rate of more than 85%. After the improved PSO‐BP algorithm is used to calculate and fit the four parameters of torque, rotational speed, thrust, and surrounding rock drivable coefficient during TBM mechanical construction, the fitting accuracy is higher than 94%. (3) At the same time, according to the analysis of the fitting results, the fitting effect of TBM mechanical thrust is the best among the four‐parameter algorithm analysis fitting degrees, which is 99.3%. (4) The measured points are evenly distributed near the surrounding rock deformation surface calculated by the improved PSO‐BP algorithm, and the fitting variance is 0.972. With the tunnel excavation, the settlement deformation of surrounding rock first increases rapidly, and then, the settlement deformation enters a slow growth stage and finally reaches a relatively stable state. (5) After the jam occurs, the tunnel is expanded from the telescopic shield or tail shield to the outer side of the shield to reduce the contact area between the shield and the surrounding rock to reduce the extrusion of the surrounding rock pressure on the shield.

Evaluation of Political Classroom Teaching Quality in Universities Based on DA-BP Algorithm

In order to improve the evaluation accuracy of political classroom teaching quality in universities, a DA-BP-based evaluation method of political classroom teaching quality in universities is proposed. First of all, in constructing the teaching quality evaluation system of university political courses using the framework of behavior theory, the system consists of 4 primary indicators and 15 secondary indicators. Second, the university’s 2008–2013 annual teaching quality assessment scores for the teaching quality of political courses were used as the input data of the DA-BP network, and the 2013–2016 teaching evaluation scores were used as the algorithm’s test data. In order to test the prediction accuracy of the DA-BP algorithm, it is compared with the GA-BP algorithm, PSO-BP algorithm, and BP algorithm. The research results show that DA-BP has higher prediction accuracy than other algorithms and can better evaluate the teaching quality of political courses.

Dynamic recrystallization of Ti-6Al-4V titanium alloy based on cellular automata

Ti-6Al-4V (TC4) alloy is a (α + β) two-phase titanium alloy and has good process plasticity, superplasticity, and corrosion resistance. It is widely used in the aerospace industry. During the hot working process, the microstructure and deformation behavior affect its mechanical properties. However, most of the current research focuses on the use of metallographic methods and physical modeling methods to characterize and estimate the evolution of microstructures and lacks accuracy. Therefore, in this study, the thermal deformation behavior of TC4 titanium alloy is predicted and the dynamic recrystallization process is visualized through thermal simulation test, metallographic test, particle swarm optimization–backpropagation (PSO–BP) algorithm, and cellular automata (CA) method. The conclusions are as follows: the grain growth of TC4 titanium alloy is relatively uneven during hot compression. The increase in temperature and the decrease in strain rate are favorable to dynamic recrystallization. After hot compression, the grain texture appeared in the microstructure of TC4 titanium alloy, and the type and strength of texture changed with the change in temperature. Based on the PSO–BP algorithm, the rheological behavior of TC4 titanium alloy during hot compression is predicted. Compared with the experimental value, the correlation of the PSO–BP model is 0.9972, and the error is within 10%. Finally, using the PSO–BP algorithm as the input, combined with the CA method and the related theory of dynamic recrystallization, the dynamic recrystallization behavior of TC4 titanium alloy during hot compression is simulated. The electron backscatter diffraction test is used for comparison, and the simulated microstructure is similar to the experimental structure after removing the influence of some defects. The average grain size error is within 10%. It shows that this model can well predict the dynamic recrystallization behavior of TC4 titanium alloy during hot compression.

Simulation Path of Network Microvideo Personalized Recommendation Based on Improved Ant Colony Algorithm

With the rapid development of Internet technology, network information not only brings many conveniences to the majority of users but also brings about the problem of information overload. It is increasingly difficult for users to accurately obtain the information they need from the vast ocean of information. This problem has led to the research of recommendation technology, especially the simulation path of online microvideo personalized recommendations. The video recommendation model in this study adopts the idea of an ant colony algorithm. The evaluation data and browsing record data generated by users are the basis of video recommendation, and the microvideo feature model and user preference model are abstracted from the original data by improving the ant colony algorithm. The recommendation algorithm is then used to recommend online microvideos that meet users’ preferences. There are two recommendation methods, one is to find similar students through the user preference model of each student, and find the microvideo content that the students have not learned; the other is to use the user preference model and the content feature model to match the content that conforms to the user’s preference. Compared with the traditional BP neural network algorithm, GA-BP algorithm, and PSO-BP algorithm, the mean square error MSE value of the ACO algorithm for microvideo scoring classification is reduced by 11.41%, 5.93%, and 2.41%, respectively. It can be seen that the classification of video ratings by the improved ACO algorithm has lower average errors than other algorithms. The network microvideo personalized recommendation scheme designed in this study has very practical significance.

Roughness Prediction and Performance Analysis of Data-Driven Superalloy Belt Grinding

Abrasive belt grinding has unique advantages in avoiding machining defects and improving surface integrity while grinding hard materials such as superalloys. However, the random distribution of abrasive particles on the abrasive belt surface is uncontrollable, and chatter and machining errors accompany the machining process, leading to unclear mapping relationship between process parameters and surface roughness, which brings great challenges to the prediction of surface roughness of superalloy. Traditional empirical equations are highly dependent on empirical knowledge and the development of scientific theories and can only solve problems with relatively simple and clear mechanisms, but cannot effectively solve complex and mutually coupled problems. The method based on data-driven patterns has a better idea for mining the implicit mapping relationship and eliminating the uncertainty of complex problems. This study presents a data-driven roughness prediction method for GH4169 superalloy. First, a superalloy grinding platform is built. According to the grinding empirical equation, the mapping relationship between process parameters and surface roughness is analyzed, and a prediction model is established based on the error back propagation (BP) algorithm. Second, genetic algorithm (GA) and particle swarm optimization (PSO) algorithm are used to optimize the weights and thresholds of the neural network, and the global optimal solution is obtained. Finally, the prediction performance of different algorithms is compared. The results show that the non-uniform absolute errors of the BP algorithm, GA-BP algorithm, and PSO-BP algorithm are 0.12, 0.085, and 0.078, respectively. The results show that the roughness prediction algorithm based on PSO-BP is more suitable for GH4169 superalloy.

Machine Vision and Intelligent Algorithm Based on Neural Network.

Neural network algorithms and intelligent algorithms are hot topics in the field of deep learning. In this study, the neural network algorithm and intelligence are optimized, and it is used in simulation experiments to improve the target image recognition ability of the algorithm in the machine vision environment. First, this paper introduces the application of neural networks in the field of machine vision. Second, in the experiment, the improved VGG-16 convolutional neural network (CNN) model is applied to metal block defect detection. Experimental results show that the optimized network can classify metal block defects with the maximum accuracy of 99.28%. Then, the intelligent algorithm based on neural network is studied, and the CIFAR-10 data set is taken as the experimental target for training test and verification test. Using BP algorithm, particle swarm optimization algorithm (PSO-BP), and improved neural network algorithm, respectively, the convergence speed of ICS algorithm based on BP neural network is compared. In contrast, ICS-BP algorithm has the fastest convergence speed and converges when the number of iterations is 32, followed by PSO-BP algorithm.

Research on Forest Carbon Sequestration Based on FSC Model and DeepAR Algorithm

Forestry resources play an irreplaceable role in solving the problem of climate change. Forest managers to find a balance between carbon sinks and forest products, decisions must therefore take into account many aspects of forest value. This paper clarifies that forest carbon sequestration is mainly influenced by three aspects: human, climate, and nonclimatic physical factors, constructs a model for estimating the carbon sequestration rate of forest ecosystems based on forest age and the logistic growth equation, and combines human behaviour and climate influencing factors to make comprehensive corrections to the carbon sequestration rate of forest ecosystems per unit area. A model for calculating the carbon sequestration rate over time in a forest system was developed, which combined with the DeepAR Algorithm to obtain carbon sequestration. Then, this paper then assesses the various values of the forest, dividing the 25 factors affecting forest value assessment into five indices to establish a model of the forest ecosystem value assessment system for application to forest value assessment under different conditions. After determining the index system, we use the EWM‐CVM to integrate the indexes into the value index based on the forest ecosystem to propose a management plan. And the PSO‐BP algorithm is used to determine the transition point for forest management in order to optimise the ecological value of the forest.

Thunderstorm disaster prediction based on enhanced GWO optimized BP neural network

Aiming at the problem that traditional BP algorithm is prone to fall into local optimum when carrying out thunderstorm disaster prediction, a thunderstorm disaster prediction method based on BP neural network optimized by enhanced GWO algorithm is proposed in this paper. Firstly, the global search capability of GWO is enhanced by introducing clone mutation operation in genetic algorithm and position update idea in particle swarm optimization, and the convergence speed of the algorithm is improved. Then, the BP neural network weight and threshold optimized by the enhanced GWO algorithm are used to build the network model and predict the occurrence of thunderstorm disasters. Simulation results show that compared with the original BP algorithm, GWO-BP algorithm and PSO-BP algorithm, the improved IPSGWO-BP algorithm improves the accuracy of thunderstorm disaster prediction by 13.33%, 12.50% and 8.33%, respectively. Meanwhile, the null alarm rate is lower, the optimization ability is stronger, and the convergence speed is faster.

Analysis of the Relationship between Dance Action and Health Psychology in the Process of Dance Performance Teaching Environment.

One of the public fundamental disciplines that is typically put up among the professional teaching units in universities is dance. In order for this sports project with fitness, mental health, and aesthetic functions to be widely developed in universities, the use of reasonable, scientific, and targeted teaching methods can effectively improve the instructional effect. At the same time, it has further promoted the quality of education in universities and implemented the guiding ideology of “health first.” In order to avoid the classifier's performance-degrading effects brought on by the high dimension, this research suggests combining the classifier's psychological stress identification algorithm with a particle swarm optimization (PSO) approach. The experimental findings reveal that the PSO-SVM algorithm, PSO-BP algorithm, improved PSO-SVM algorithm, and improved PSO-BP algorithm, respectively, have recognition rates for psychological stress of 82.50%, 84.50%, 90.17%, and 94.83%. Additionally, the recognition rates of the improved PSO classifier are significantly higher than those of the basic PSO algorithm, demonstrating the improved PSO algorithm's strong generalization ability in optimization.

Application Of Particle Swarm Optimization and Improved PSO-BP Algorithm in Computer Forecasting Model

Given the shortcomings of the traditional BP (Back Propagation) neural network in dealing with medium and long-term stock forecasting, this paper presents a combination forecasting model of BP network optimized by PSO (Particle Swarm Optimization). Firstly, the ARIMA (Autoregressive Integrated Moving Average) model was used to predict the stock's closing price, which was used as part of the BP neural network input. On this basis, the BP network was optimized by particle swarm optimization algorithm, and the optimal threshold and weight were given to the network for further prediction, which improved the generalization ability and prediction accuracy of the whole network. The results show that, compared with BP neural network, LSTM (Long Short-Term Memory) and ARIMA model, the PSO-BP network model has a significant improvement in forecasting accuracy, which verifies the effectiveness of the combination forecasting method in dealing with the problem of medium and long-term stock price forecasting.

A bionic piezoelectric tactile sensor for features recognition of object surface based on machine learning.

Based on the tactile mechanism of human fingertips, a bionic tactile sensor fabricated from polyvinylidene fluoride piezoelectric film is proposed, which can identify the surface softness, viscoelasticity, thermal conductivity, and texture roughness of the object. The tactile sensor is mounted on the fingertip of the bionic manipulator, which obtains the surface features by touching and sliding the object. The time-domain features of the output signal are used for preliminarily discriminating the softness, viscoelasticity, and heat conduction of the object. Finally, based on the Back Propagation and the Particle Swarm Optimization-Back Propagation neural network algorithm, the recognition experiment of texture roughness is carried out using the PSO algorithm to improve the BP neural network so that the optimized BP algorithm has a higher convergence accuracy. The results show that the PSO-BP algorithm achieved the highest accuracy of 98% for identifying samples with different roughnesses and the average recognition achieved an accuracy of 94%. The bionic piezoelectric tactile sensor proposed in this paper has a good application development prospect in recognizing the surface features of objects and intelligent robots.