Parameters Of BP Neural Network Research Articles

Research on Financial Risk Crisis Prediction of Listed Companies Based on IWOA-BP Neural Network

<p>To avoid the risk brought by the financial crisis, the Improved Whale Optimization Algorithm-Back Propagation neural network (IWOA-BP) financial crisis early warning model is proposed. This paper selects the data from financial statements of some of the listed Chinese manufacturing companies from 2015-2019 as the research sample. First, the financial data of enterprises are screened by principal component analysis, and the early warning model is constructed from the financial and nonfinancial factors of six indicators: solvency, operating capacity, profitability, development capacity, cash flow and risk level factors. Second, the Whale Optimization Algorithm is optimized by the chaos strategy, as well as by the dynamic weight and sine cosine algorithm. Finally, the improved Whale Algorithm is optimized for BP neural network parameters. In the simulation experiments, the performance of the improved whale optimization algorithm is substantially improved. In addition, in the empirical analysis, compared to the prediction model with other algorithms, the prediction model of this paper has better results in terms of prediction accuracy.</p> <p> </p>

Research on Securities Portfolio Model Based on Genetic Optimization Neural Network

Portfolio is an investment management concept different from individual asset management. This consideration leads to an interesting result, that is, investors should buy a variety of securities at the same time instead of one kind of securities for diversified investment. Aiming at the limitations of BPNN (BP neural network) in traditional artificial neural network and its shortcomings such as many iterations, low convergence accuracy, and poor generalization, a portfolio method based on GA_BPNN (Genetic Optimization Neural Network) was proposed. The setting of GA (genetic algorithm) parameters and BPNN parameters are discussed in detail, and the implementation steps of genetic BP algorithm are described. The results show that the evaluation indexes of GA_BPNN prediction model are obviously better than those of the comparison prediction model, with the coincidence rate of 77.96% and the average absolute error of 12.451. The combination of GA and BPNN can effectively solve this problem. The simulation results of optimizing securities portfolio show that its optimization scheme is better than quadratic programming method, and this method is more correct, efficient, and practical.

Application of hybrid neural network in discharge coefficient prediction of triangular labyrinth weir

Discharge coefficient (Cd) is an important parameter of triangular labyrinth weir. It is of great significance to predict the discharge coefficient accurately. In this research, in order to more accurately predict the Cd, in view of the traditional BP neural network is easy to fall into the local minimum in the training process, genetic algorithm (GA) and particle swarm optimization (PSO) are employed to optimize the traditional BP neural network's initial weights and thresholds. Nonlinear regression analysis (NLR) is also added to compare with these intelligent methods and four discharge coefficient prediction models are built, namely the NLR, the BPNN, the GA-BPNN and the PSO-BPNN. After the completion of the model construction, in order to objectively evaluate the performance of these models, the prediction results of these models are compared with the experiment results, and the determination coefficient (R2), the mean absolute error (MAE) and the root mean square error (RMSE) are introduced as the performance indicators to quantify the model performance. The results show that the accuracy and stability of the NLR are much worse than that of the intelligent models. The prediction results of the GA-BPNN and the PSO-BPNN are quite accurate with a higher decision coefficient than the BPNN. Moreover, the MAEs and the RMSEs of the GA-BPNN and the PSO-BPNN were significantly reduced by 25 and 40% compared with BPNN, respectively, and the maximum prediction errors were 4.4% and 2.6%, severally. Meanwhile, the width of error uncertainty band of GA-BPNN and PSO-BPNN is narrower than BPNN. By comparing GA-BPNN and PSO-BPNN with the discharge coefficient prediction models of triangular labyrinth weir in previous literatures, it is found that the mean absolute percentage error (MAPE) values of GA-BPNN and PSO-BPNN are 1.504% and 1.225% respectively, which are lower than other existing models. At the same time, the other performance indexes are better than most existing models, indicating that the genetic algorithm and PSO algorithm are more effective than the traditional BP algorithm in adjusting BP neural network parameters, easier to find the global optimal value, and improve the prediction accuracy and applicability of the model.

The improved grasshopper optimization algorithm and its applications

Grasshopper optimization algorithm (GOA) proposed in 2017 mimics the behavior of grasshopper swarms in nature for solving optimization problems. In the basic GOA, the influence of the gravity force on the updated position of every grasshopper is not considered, which possibly causes GOA to have the slower convergence speed. Based on this, the improved GOA (IGOA) is obtained by the two updated ways of the position of every grasshopper in this paper. One is that the gravity force is introduced into the updated position of every grasshopper in the basic GOA. And the other is that the velocity is introduced into the updated position of every grasshopper and the new position are obtained from the sum of the current position and the velocity. Then every grasshopper adopts its suitable way of the updated position on the basis of the probability. Finally, IGOA is firstly performed on the 23 classical benchmark functions and then is combined with BP neural network to establish the predicted model IGOA-BPNN by optimizing the parameters of BP neural network for predicting the closing prices of the Shanghai Stock Exchange Index and the air quality index (AQI) of Taiyuan, Shanxi Province. The experimental results show that IGOA is superior to the compared algorithms in term of the average values and the predicted model IGOA-BPNN has the minimal predicted errors. Therefore, the proposed IGOA is an effective and efficient algorithm for optimization.

Forecasting of solar radiation in photovoltaic power station based on ground‐based cloud images and BP neural network

The solar radiation near the surface is the main reason that affects photovoltaic power generation. Accurate ultra-short-term solar radiation prediction is the premise of photovoltaic power generation prediction. Here the cloud movement prediction method based on the ground-based cloud images is presented. The cloud recognition, cloud matching, cloud area correction and cloud movement prediction are performed to predict the drift trajectory of the clouds that will block the sun. Then, using digital image technology, 13 kinds of feature information are extracted from the ground-based cloud images. Then, these feature information are input into BP neural network, and the parameters of BP neural network are optimized by genetic algorithm. Through a large number of data training, a new ultra-short-term prediction model of solar radiation is established. Finally, through experimental comparison, the results show that the prediction accuracy of the model with the feature information of ground-based cloud images can reach 96%, compared with the model without the feature information of ground-based cloud images, the accuracy is improved by 5%. The proposed ultra-short-term solar radiation prediction model can effectively predict the radiation jumping process caused by cloud occlusion, and greatly improve the prediction accuracy, especially in cloudy weather.

Research on emotion recognition algorithm based on improved BP neural network

The application of small robots in the production of freeze frame animation can speed up the production progress and improve the quality of production. Small robots will play the role of intelligent agents. It is necessary to recognize their emotions for the promotion of animation films. With the development of intelligent technology, BP neural network has been applied to the field of emotion recognition because of its advantages of self-learning, self-adaptive and self-organization. In this paper, the parameters of BP neural network are adjusted and improved to avoid the defects of BP neural network in emotion recognition. Then, the parameters and structure of the network are designed according to the emotion category. Then a three-layer BP neural network with 4 input nodes, 13 hidden layer nodes and 4 output nodes is constructed. Finally, it is applied to emotion recognition, and 10 groups of data are selected from the training samples for detection, and the diagnostic accuracy is 80%.

Fault diagnosis method for scintillation detector based on BP neural network

This article gives a scintillation detector fault diagnosis method based on BP neural network. From the aspect of output signals of scintillation detectors, the wavelet packet transform is used to extract the energy characteristic vectors which are treated as the input of BP neural network, and a training database is established as well as BP neural network parameters are optimized. Then the method is employed to establish a fault recognition model and fault types can be concluded. Finally, the simulation data are compared with those of two other methods (the statistical diagnosis method and an method based on multi-classification support vector machine). The experimental results illustrate that the application of proposed method can improve the fault diagnosis accuracy of scintillation detectors effectively.

Rapid Modeling of the Sound Speed Field in the South China Sea Based on a Comprehensive Optimal LM-BP Artificial Neural Network

Ocean sound speed is an essential foundation for marine scientific research and marine engineering applications. In this article, a model based on a comprehensive optimal back propagation artificial neural network model is developed. The Levenberg–Marquardt algorithm is used to optimize the model, and the momentum term, normalization, and early termination method were used to predict the high precision marine sound speed profile. The sound speed profile was described by five indicators: date, time, latitude, longitude, and depth. The model used data from the CTD observation dataset of scientific investigation over the South China Sea (2009–2012) (108°–120° E, 6°–8° N), which includes comprehensive scientific investigation data from four voyages. The feasibility of modeling the sound speed field in the South China Sea is investigated. The proposed model uses the momentum term, normalization, and early termination in a traditional BP artificial neural network structure and mitigates issues with overtraining and difficulty when determining the BP neural network parameters. With the LM algorithm, a fast-modeling method for the sound field effectively achieves the precision requirement for sound speed prediction. Through the prediction and verification of the data from 2009 to 2012, the newly proposed optimized BP network model is shown to dramatically reduce the training time and improve precision compared to the traditional network model. Results showed that the root mean squared error decreased from 1.7903 m/s to 0.95732 m/s, and the training time decreased from 612.43 s to 4.231 s. Finally, the sound ray tracing simulations confirm that the model meets the accuracy requirements of acoustic sounding and verify the model’s feasibility for the real-time prediction of the vertical sound speed in saltwater bodies.

A Data Driven Fuel Cell Life-Prediction Model for a Fuel Cell Electric City Bus

<div class="section abstract"><div class="htmlview paragraph">Life prediction is a major focus for a commercial fuel cell stack, especially applied in fuel cell electric vehicles (FCEV). This paper proposes a data driven fuel cell lifetime prediction model using particle swarm optimized back-propagation neural network (PSO-BPNN). For the prediction model PSO-BP, PSO algorithm is used to determine the optimal hyper parameters of BP neural network. In this paper, total voltage of fuel cell stack is employed to represent the health index of fuel cell. Then the proposed prediction model is validated by the aging data from PEMFC stack in FCEV at the actual road condition. The experimental results indicate that PSO-BP model can predict the voltage degradation of PEMFC stack at actual road condition precisely and has a higher prediction accuracy than BP model.</div></div>

The Research on Improving the Precision of The Polymer IFHI By BP Neural Network of The Method Data Normalization

BP neural network has a good effect on data prediction. Using BP neural network to predict polymer fire risk index (IFHI) is a cross research method based on computer intelligent learning model and traditional fire risk assessment. In order to solve the defect of low precision of BP neural network in IFHI prediction, a solution to standardize the initial data processing is proposed to optimize the BP neural network simulation model. In the construction of BP neural network model, the range, threshold, and weight of the initial data need to be processed globally, and then the BP neural network is simulated and trained. Finally, the data is simulated and predicted by using MATLAB. The output results of the neural network with different data standardization processing methods are compared to verify the simulation accuracy. The results show that the input node parameters of BP neural network are processed by Z-score normalization method, which improves the learning ability of the model, improves the accuracy of the prediction effect, and reduces the IFHI error of prediction.

English Feature Recognition Based on GA-BP Neural Network Algorithm and Data Mining.

With the development of society and the promotion of science and technology, English, as the largest universal language in the world, is used by more and more people. In the life around us, there is information in English all the time. However, because the process of manual recognition of English letters is very labor-intensive and inefficient, the demand for computer recognition of English letters is increasing. This paper studies the influence of the parameters of BP neural network and genetic algorithm on the whole network, including the input, output, and number of hidden layer nodes. Finally, it improves and determines the settings and values of the relevant parameters. On this basis, it shows the rationality of the selected parameters through experiments. The results show that only GA-BP neural network and feature data mining algorithm can complete feature extraction and become the main function of feature classification at the same time. After enough initial data sample analysis training, the GA-BP neural network was found to have good data fault tolerance and feature recognition. The experimental results show that the genetic algorithm can find the best weights and thresholds and the weights and thresholds are given to the BP neural network. After training, the recognition of handwritten letters can be realized. Finally, the convergence of the two algorithms is compared through experiments, which shows that the overall performance of the BP neural network algorithm is improved after genetic algorithm optimization. It can be seen that the genetic algorithm has a good effect in improving the BP neural network and this method has a broad prospect in English feature recognition.

Research on Global Higher Education Quality Based on BP Neural Network and Analytic Hierarchy Process

Having a universal, fair, democratic and practical higher education system plays a particularly important role in the future development of the country. However, the higher education system in various countries is uneven. It is of great significance to establish a general evaluation system for the development of global education. In this paper, 23 indicators are preliminarily selected from the education data of Universitas 21 and Global Statistical Yearbook. After the gray correlation analysis, 12 indicators were selected. On the one hand, principal component analysis is used to reduce the dimension of these 12 indicators in 50 countries, and the first four principal components with cumulative contribution rate of 99% are finally selected as the input parameters of BP neural network. On the other hand, 12 indicators are divided into four aspects as the standard of scheme decision-making. Finally, a higher education quality evaluation and decision-making model based on BP neural network and analytic hierarchy process are established. Then eight countries are selected to use the model to evaluate their current higher education quality. Based on the input and evaluation results of the four aspects of higher education in various countries, the analytic hierarchy process is used to make program decision, and several improvement suggestions are put forward for the current education policies of various countries.

Prediction of TBM penetration rate based on Monte Carlo-BP neural network

Based on the BP neural network model of machine learning method, the corresponding random input parameters are generated by Monte Carlo method, and the prediction of TBM driving speed is studied. In this study, the machine learning method is applied to the prediction of TBM penetration rate, and the established empirical model has higher accuracy and practicability. After selecting the predictive control type of BP neural network, according to the control requirements of TBM, system composition and the characteristics of different geological tunneling, the appropriate data are selected to train the neural network, and the predictive control model of neural network for TBM with high convergence and real-time performance is established. Monte Carlo method has strong optimization and control functions in the realistic planning of many complex problems. In the process of TBM velocity prediction, the random input of parameters is realized by Monte Carlo method, which makes the prediction more accurate. BP neural network is used to predict the penetration rate of TBM. Its accuracy mainly depends on the accuracy of input parameters. The actual measured and predicted values of TBM driving speed are basically near the straight line x = y as the horizontal and vertical coordinates, and the correlation coefficient R = 0.9789. Therefore, the BP neural network combined with genetic algorithm has a high reference value for the prediction of TBM driving speed. When the TBM type is the same and the system equipment is the same, four factors, namely uniaxial compressive strength, Brazilian tensile strength, peak slope index, and distance between planes of weakness, are taken as input parameters of BP network by calculating the weight of influencing factors. In the specific operation, the genetic algorithm is used to iterate continuously to find the optimal solution of the initial weight parameters of BP neural network. In this study, this prediction method is applied to practical prediction. The feasibility of this method is verified by comparing with the final actual measurement result, which is of great practical significance to the evaluation of engineering, design scheme and cost control.

Prediction of Explosion Power of Fuel Air Mixture in Coal Mine by Improved BP Neural Network

In order to improve the feasibility and accuracy of predicting the explosion power of fuel air mixture, a method of BP neural network prediction is proposed by combining factor analysis method with BP neural network. Using factor analysis method, the original data of 9 fuel air mixture explosion power factors were processed by dimensionality reduction data, 2 common factors were obtained, and 2 common factors were substituted for 9 fuel air mixtures as input layer parameters of BP neural network. A prediction model of coal and gas outburst with the combination of factor analysis method and BP Neural network is established to predict the explosion power of fuel air mixture. The prediction model of the explosion power of the fuel air mixture is selected to verify the improved BP neural network prediction models, and the final results are as follows: The relative error range of four prediction samples is 0.16%-7.58%, all less than 10%. The improved BP neural network prediction method is used to solve the problem of the traditional BP neural network because of the excessive number of input layer parameters, low data processing efficiency, slow iteration rate and low precision, which provides a new research way for predicting the explosion power of fuel air mixture.

Analysis and Prediction of Regional Electricity Consumption Based on BP Neural Network

Electricity consumption forecasting is one of the most important tasks for power system workers, and plays an important role in regional power systems. Due to the difference in the trend of power load and the past in the new normal, the influencing factors are more diversified, which makes it more difficult to predict the current electricity consumption. In this paper, the grey system theory and BP neural network are combined to predict the annual electricity consumption in Jiangsu. According to the historical data of annual electricity consumption and the six factors affecting electricity consumption, the gray correlation analysis method is used to screen the important factors, and three factors with large correlation degree are selected as the input parameters of BP neural network. The power forecasting model uses nearly 18 years of data to train and validate the model. The results show that the gray correlation analysis and BP neural network method have higher accuracy in power consumption prediction, and the calculation is more convenient than traditional methods.

Inversion of permanent deformation parameters of neural network based on bee colony optimization algorithm

In this research, a method of artificial bee colony algorithm was proposed to optimize BP neural network parameters and then introduced to parameter inversion of geotechnical engineering. The method is based on the research and analysis of permanent deformation of geotechnical earthquake engineering and aimed at addressing the shortages of BP neural network which is slow to converge and may easily get minimum value. This method not only strengthens the global optimization ability of bee colony optimization algorithm, but also reflects the high accuracy of predicting nonlinear problems of BP neural network and the strong generalization ability. The proposed method also improves the prediction accuracy This new proposed method was validated by using the earthquake data of Zipingpu concrete faced rockfill dam in WenChuan earthquack. The results showed that this method can better solve the problem of permanent deformation parameters and can be extended to inversion predict of the same kind.

The Improved Antlion Optimizer and Artificial Neural Network for Chinese Influenza Prediction

The antlion optimizer (ALO) is a new swarm‐based metaheuristic algorithm for optimization, which mimics the hunting mechanism of antlions in nature. Aiming at the shortcoming that ALO has unbalanced exploration and development capability for some complex optimization problems, inspired by the particle swarm optimization (PSO), the updated position of antlions in elitism operator of ALO is improved, and thus the improved ALO (IALO) is obtained. The proposed IALO is compared against sine cosine algorithm (SCA), PSO, Moth‐flame optimization algorithm (MFO), multi‐verse optimizer (MVO), and ALO by performing on 23 classic benchmark functions. The experimental results show that the proposed IALO outperforms SCA, PSO, MFO, MVO, and ALO according to the average values and the convergence speeds. And the proposed IALO is tested to optimize the parameters of BP neural network for predicting the Chinese influenza and the predicted model is built, written as IALO‐BPNN, which is against the models: BPNN, SCA‐BPNN, PSO‐BPNN, MFO‐BPNN, MVO‐BPNN, and ALO‐BPNN. It is shown that the predicted model IALO‐BPNN has smaller errors than other six predicted models, which illustrates that the IALO has potentiality to optimize the weights and basis of BP neural network for predicting the Chinese influenza effectively. Therefore, the proposed IALO is an effective and efficient algorithm suitable for optimization problems.

Vessel traffic flow analysis and prediction by an improved PSO-BP mechanism based on AIS data

Vessel traffic flow forecasting is indispensable for the development of national shipping industry and the coordinated development of regional economy. In this paper, an improved PSO-BP (particle swarm optimization-back propagation) prediction model is established for the prediction of the total vessel traffic flow in a designated port area. The presented prediction model is referred to as SAPSO-BP neural network which utilizes the SAPSO (self-adaptive particle swarm optimization) algorithm to adjust the structure parameters of BP neural network. Facilitated by the establishment of foundation networks and satellite communication of Automatic Identification System (AIS) receivers, the detailed information of vessel is becoming increasingly obtainable. Therefore, a large number of real-observed vessel traffic flow data based on AIS records of Port area of Los Angeles (LA) has been chosen as the testing database to validate the effectiveness of the SAPSO-BP prediction model in vessel traffic flow forecasting. The grey correlation analysis (GCA) is employed to confirm the input dimension of the prediction model. Finally, simulation results demonstrate that the presented prediction approach can achieve vessel traffic flow trend predictions with reasonable, satisfactory convergence and stability.

Emotional Semantic Recognition of Visual Scene in Flash Animation

Based on the organization structure of the Flash animation files, we first use the edge density method to segment the Flash animation to obtain the visual scenes, then extract the visual features such as color and texture as the input parameters of BP neural network, and set up the sample database. Secondly, we choose a suitable model for emotion classification, use eight kinds of emotional adjectives to describe the emotion of Flash animation, such as warm, delightful, exaggerated, funny, desolate, dreary, complex, and illusory, and mark the emotion value of the visual scene in the sample database and so use it as the output parameter of the BP neural network. Finally, we use BP neural network with appropriate transfer function and learning function for training to obtain the rules for mapping from visual features of the visual scene to semantic space and, at last, complete the automatic classification work of emotional semantic of the visual scene. We used the algorithm to carry on the emotional semantics recognition to 5012 visual scenes, and the experiment effect is good. The results of our study can be used in the classification, retrieval, and other fields of Flash animation based on emotional semantics.

Optimized BP neural network for Dissolved Oxygen prediction

To solve the low accuracy, slow convergence and poor robustness problem of traditional neural network method for water quality forecasting, a new model of dissolved oxygen content prediction is proposed based on sliding window, particle swarm optimization (PSO) and BP neural network. dissolved oxygen content prediction model in water quality is established by handling dissolved oxygen content data through sliding window, and using particle swarm optimization algorithm to obtain BP neural network parameters. This model is applied to prediction analysis of dissolved oxygen with online monitoring of regional groundwater in Xilin Gol League on July 25, 2017 to December 5, 2017. Experimental results show that the model has better prediction effect, and mean square error (MSE), root mean square error(RMSE), mean absolute error(MAE) value of PSO algorithm to optimize the BP neural network based on sliding window are 0.437% and 6.611%, 0. 251% respectively, which are better than single forecasting method by using sliding window, PSO, and BP neural network individually. The Optimized BP neural network not only has fast convergence speed and high prediction accuracy, but also provides decision-making basis for water pollution control and water management.