The Research on BP Neural Network Model Based on Guaranteed Convergence Particle Swarm Optimization

In order to achieve effective prediction and control the size of outlet concentration of Carbon Dioxide (CO 2), but also in view of PSO compared to other neural network, which has a fast convergence characteristics such as particle swarm, neural network model proposed is to predict the outlet concentration of CO 2, and this model is studied. obtained PSO – BP (Particle Swarm Optimization- Back Propagation) algorithm proposed to the square and - death e2pso BP 1.6892, by using 8.0 (r2009a) matlab7 for data analysis and simulation study, which shows their very strong fitting ability, proved further by contrasting PSO-BP algorithm with GA – BP (General Anesthesia- Back Propagation) algorithm and BP algorithm. In this paper, experimental data have been used to establish GA-BP model and BP model, on the other hand Theses models have been used to analyze these data and make simulation studies separately, furthermore, these three models will be compared, the results from which further proved the PSO-BP model stronger in fitting ability.

GCPSO in cooperation with graph theory to distribution network reconfiguration for energy saving

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.

PurposeMechanical products usually require deterministic finite element analysis in the design phase to determine whether their structures meet the requirements. However, deterministic design ignores the influence of uncertainties in the design and manufacturing process of mechanical products, leading to the problem of a lack of design safety or excessive redundancy in the design. In order to improve the accuracy and rationality of the design results, a robust design method for structural reliability based on an active-learning marine predator algorithm (MPA)–backpropagation (BP) neural network is proposed.Design/methodology/approachThe MPA was used to obtain the optimal weights and thresholds of a BP neural network, and an active-learning function applicable to neural networks was proposed to efficiently improve the prediction performance of the BP neural network. On this basis, a robust optimization design method for mechanical product reliability based on the active-learning MPA-BP model was proposed. Random moving quadrilateral sampling was used to obtain the sample points required for training and testing of the neural network, and the reliability sensitivity corresponding to each sample point was calculated by subset simulated significant sampling (SSIS). The total mass of the mechanical product and the structural reliability sensitivity of the trained active-learning MPA-BP model output were taken as the optimization objectives, and a multi-objective reliability-robust optimization design model was constructed, which was solved by the second-generation non-dominated ranking genetic algorithm (NSGA-II). Then, the dominance function was used in the obtained Pareto solution set to make a dominance-seeking decision to obtain the final reliability-robust optimization design solution. The feasibility of the proposed method was verified by a reliability-robust optimization design example of the bogie frame.FindingsThe prediction error of the active-learning MPA-BP neural network was smaller than those of the particle swarm optimization (PSO)-BP, marine predator algorithm (MPA)-BP and genetic algorithm (GA)-BP neural networks under the same basic parameter settings of the algorithm, which indicated that the improvement strategy proposed in this paper improved the prediction accuracy of the BP neural network. To ensure the reliability of the bogie frame, the reliability sensitivity and total mass of the bogie frame were reduced, which not only realized the lightweight design of the bogie frame, but also improved the reliability and robustness of the bogie.Originality/valueThe MPA algorithm with a higher optimization efficiency was introduced to find the weights and thresholds of the BP neural network. A new active-learning function was proposed to improve the prediction accuracy of the MPA-BP neural network.

To resolve the problem of short-term power load forecasting, we propose a self-adapting particle swarm optimization (PSO) algorithm to optimize the error back propagation (BP) neural network model. The proposed model is called PSO-BP model which employs PSO to adjust control parameters of BP neural network. In order to verify the performance of PSO-BP, the practical datum of a city in China are selected in the experiments. Simulation results show that our approach outperforms simple BP neural network.

A sales forecast model was designed based on BP (Back Propagation) neural network. The selection of network types, the determination of structural parameters, the processing of the network input and output data and other issues are also discussed. Experimental results show that the BP algorithm is applied to predict the contents of unknowing rules; it has higher accuracy and better predictive ability than the traditional regression analysis approach.

In this paper, we present an approach to recognizing characters in surface mount technology (SMT) product. The recognition rate of SMT product character is not very good, because of unexpected noise in SMT the product character image. An improved SMT product character recognition method is proposed which can improve the recognition rate. At first, the character image is changed into a gray image. Some appropriate image processing algorithms are used to eliminate the noise. Then, single character image is obtained after character segmentation and character normalization. Finally, a three-layer back propagation (BP) neural network module is constructed. In order to improve the convergence rate of the network and avoid oscillation and divergence, the BP algorithm with momentum item is used. As a result, the SMT product character recognition system is developed, and its implementation method and steps are introduced with practical examples. Experimental results indicate that the proposed character recognition can obtain satisfactory character-recognition rate and the recognition rate reached over by 98.6%.

In order to improve the efficiency and accuracy of thermal and moisture comfort prediction of underwear, a new prediction model is designed by using principal component analysis method to reduce the dimension of related variables and eliminate the multi-collinearity relationship between variables, and then inputting the converted variables into genetic algorithm (GA) and BP neural network. In order to avoid the problems of slow convergence speed and easy falling into local minimum of Back Propagation (BP) neural network, this paper adopted GA to optimize the weights and thresholds of BP neural network, and utilized MATLAB software to program, and established the prediction models of BP neural network and GA–BP neural network. To verify the superiority of the model, the predicted result of GA–BP, PCA–BP and BP are compared with GA–BP neural network. The results show that PCA could improve the accuracy and adaptability of GA–BP neural network for thermal and moisture comfort prediction. PCA–GA–BP model is obviously superior to GA–BP, PCA–BP, BP, SVM and K-means prediction models, which could accurately predict thermal and moisture comfort of underwear. The model has better accuracy prediction and simpler structure.

This paper uses a back propagation (BP) neural network to predict short-term wind power. Since the initial weights and thresholds of BP neural networks significantly impact their performance, we use the optimized particle swarm optimization (PSO) to obtain the critical parameters of BP neural networks. Specifically, we optimize the PSO to make it easier to get better parameters. The experimental results show that the BP neural network's mean relative error (MRE) is 11.91%, 15.18%, and 8.56%, respectively. In comparison, the MRE of the optimized BP neural network is 5.09%, 7.21%, and 4.44%.

To address the insufficient expressive capabilities of traditional methods in assessing the development level of rural tourism, this study explores the fusion application of the Bidirectional Encoder Representations from Transformers (BERT) and the Back Propagation (BP) algorithm to enhance the accuracy and comprehensiveness of rural tourism development assessment. Firstly, this study introduces the BERT deep learning model and its applications in natural language processing, alongside the role of the BP algorithm in pattern recognition and predictive analysis. Subsequently, a framework for assessing rural tourism development levels, integrating BERT and the BP algorithm, is proposed. This framework collects multidimensional rural tourism-related data and utilizes the BERT model for sentiment analysis and topic extraction from textual data. Empirical analysis of rural tourism development in a specific region validates the effectiveness of the proposed approach. Experimental results demonstrate: (1) The model achieves an accuracy of 84.33% and an F1 score of 85.33% on the publicly available Laptop dataset, with a processing time of 20 s, significantly outperforming other methods. Compared to traditional approaches, the proposed method accurately captures correlations between textual information and numerical data, thereby enhancing the credibility and accuracy of assessment results. (2) From the ablation study results, it is evident that removing any component from the model leads to performance degradation. Specifically, removing the Bidirectional Gated Recurrent Unit (BiGRU) reduces accuracy and F1 scores to 78.21% and 76.33% on the Laptop dataset, and to 85.10% and 70.45% on the Tourist_F dataset. Removing Text Convolutional Neural Network (CNN) reduces accuracy and F1 scores to 79.34% and 77.56% on the Laptop dataset, and to 86.25% and 72.11% on the Tourist_F dataset. The most significant performance decline occurs upon removing BERT, with accuracy and F1 scores decreasing to 70.14% and 66.43% on the Laptop dataset, and to 78.45% and 60.33% on the Tourist_F dataset. These results indicate that BiGcRU, TextCNN, and BERT each contribute significantly to the model’s performance. This study provides substantial support for advancing sustainable development in rural tourism, offering significant practical and innovative value.

For the purpose of analyzing the application of back propagation (BP) neural network model in retinal vascular segmentation of color fundus images, in this study, the traditional BP neural network was first improved by adopting the additional momentum method. Second, adaptive histogram, morphological background subtraction, Gauss preprocessing matching filter and Heisen matrix were used to enhance the image and extract the features. Third, the retinal vascular segmentation algorithm for color fundus images was constructed based on optimized BP neural network. Finally, the DRIVE and MESSIDOR data sets of color fundus images were introduced to compare the proposed algorithm with the convolutional neural network (CNN) and pulse-coupled neural network (PCNN) algorithms in terms of performance. In addition, the three algorithms were also compared in terms of the sensitivity (Se), specificity (Sp), accuracy (Ac), average operation time for each image and F1 value. The results show that the BP neural network algorithm proposed in this study shows obvious advantage over the other two algorithms in the segmentation of color fundus images. In the DRIVE data set, the Se (81.37%), Sp (90.55%) and Ac (95.82%) of BP algorithm are the highest among the three; in the MESSIDOR data set, the Se (85.22%), Sp (91.08%) and Ac (96.16%) of BP algorithm are also highest; in the DRIVE and MESSIDOR data sets, the operation time of BP algorithm is (28.46 ± 3.19 ms; 24.73 ± 4.81 ms, respectively), which are significantly less than the other two algorithms. Besides, the F1 value of the proposed algorithm is obviously higher than that the other two algorithms. As a result, it is concluded that compared with the CNN algorithm and the PCNN algorithm, the proposed algorithm is more effective in the retinal vascular segmentation of color fundus images; the capillaries have better connectivity, and the proposed algorithm can improve the segmentation Ac while reducing the operation time.

The Quran is a guideline for all Muslims. In the Quran, many things are talked about. In Quranic studies, the Quran is first classified into several topics according to the discussions of the Quranic verses. In this research, a classification model using a Back Propagation Neural Network was built based on the verses of Al-Quran and its multi-labelled topics. This allows the Back Propagation algorithm architecture to issue labels for each class in the form of `yes' or `no' for each output neuron. When using the Back Propagation algorithm, a sentence input that has become a vector is taken. In this way, TF-IDF will be used for feature extraction. Then, the model was evaluated via calculation of Hamming Loss. To ensure an optimal Back Propagation process, a comparison was made between the Stochastic Gradient Descent (SGD) and Adam optimizers. Based on some experiments, the proposed scheme yielded the best performance with a Hamming Loss value of 0.129.

Training a back propagation neural network is an optimization problem to find optimal weight set in training process. The back propagation neural network can fall into a local minimum point during learning of training patterns. Therefore, evolutionary algorithms can be used to train this neural network to obtain suitable initial weight set. In this paper, a novel approach is proposed to train the back propagation neural network based on asexual reproduction optimization (ARO) algorithm. The basic idea of the proposed method is to apply ARO algorithm at the first step to search for the global initial connection weights. Then, the back propagation algorithm is used to thoroughly search for the optimal weight set. The performance of the proposed method is evaluated using a number of problems. Experimental results show that the proposed method is better than the genetic and back propagation algorithms in convergent speed and convergent accuracy.

Human resource management risks are due to the failure of employer organization to use relevant human resources reasonably and can result in tangible or intangible waste of human resources and even risks; therefore, constructing a practical early warning model of human resource management risk is extremely important for early risk prediction. The back propagation (BP) neural network is an information analysis and processing system formed by using the error back propagation algorithm to simulate the neural function and structure of the human brain, which can handle complex and changeable things that do not have an obvious linear relationship between output results and input factors, so as to find the objective connection between the two. Based on the summary and analysis of previous research works, this article expounded the research status and significance of early warning for human resource management risks, elaborated the development background, current status, and future challenges of the BP neural network, introduced the method and principle of the BP neural network’s connection weight calculation and learning training, performed the risk inducement analysis, index system establishment, and network node selection of human resource management, constructed an early warning model of human resource management risk based on the BP neural network, conducted the risk warning model training and detection based on the BP neural network, and finally carried out a simulation and its result analysis. The study results show that the early warning model of human resource management risk based on the BP network is effective, and this trained and tested BP network risk warning model can be used to conduct early warning empirical research on human resource risks to prevent human resource risks, ensure enterprise’s benign operation, and at the same time play a role in supervision and promotion of market order rectification.

The back propagation (BP) algorithm is the most popular supervised learning method for multi-layered feed forward Neural Network. It has been successfully deployed in numerous practical problems and disciplines. Regardless of its popularity, BP is still known for some major drawbacks such as easily getting stuck in local minima and slow convergence; since, it uses Gradient Descent (GD) method to learn the network. Over the years, many improved modifications of the BP learning algorithm have been made by researchers but the local minima problem remains unresolved. Therefore, to resolve the inherent problems of BP algorithm, this paper proposed BPGD-A3T algorithm where the approach introduces three adaptive parameters which are gain, momentum and learning rate in BP. The performance of the proposed BPGD-A3T algorithm is then compared with BPGD two term parameters (BPGD-2T), BP with adaptive gain (BPGD-AG) and conventional BP algorithm (BPGD) by means of simulations on classification datasets. The simulation results show that the proposed BPGD-A3T shows better performance and performed highest accuracy for all dataset as compared to other.