BP Neural Network Prediction Model Research Articles

A BP Neural Network Prediction Model Based on Dynamic Cuckoo Search Optimization Algorithm for Industrial Equipment Fault Prediction

The fault prediction problem for modern industrial equipment is a hot topic in current research. So, this paper first proposes a dynamic cuckoo search algorithm. The algorithm improves the step size and discovery probability. Then, it introduces the change trend of fitness function value into the step size update formula to balance the search speed and accuracy. At the same time, the algorithm initial global search step is larger, while the step size of the local search is smaller in the latter part of the algorithm. In the process of discovering the global optimal solution, the probability of preserving the offspring with good fitness is increased, and the uncertainty of preference random walk is improved. As the search progresses, the probability of discovery is reduced, which makes it easy to produce new individuals in the later stage of evolution. Based on this, a BP neural network prediction model based on dynamic cuckoo search algorithm optimization is established. And the experimental results show that the proposed prediction model has faster convergence and higher accuracy.

Prediction of Ship Traffic Volume in Jiujiang Port Based on Genetic Wavelet Neural Network

In recent years, the traffic volume of the Yangtze River has increased dramatically. In order to provide more favorable assistance to port planning and traffic management, the accuracy of port ship traffic volume prediction is very important. In this paper, genetic algorithm and wavelet analysis and neural network are used to construct the genetic wavelet neural network model prediction model, and BP neural network prediction model is established. The ship volume of Jiujiang Port is used as experimental data to simulate and analyze. The results show that the prediction accuracy of the genetic wavelet neural network prediction model is significantly higher than that of the BP neural network prediction model. It is proved that the genetic wavelet neural network has broad application prospects for ship traffic flow prediction in the Yangtze River port. This method has practical application significance.

BP neural network prediction model for suicide attempt among Chinese rural residents.

This study aimed to establish and assess the Back Propagation Neural Network (BPNN) prediction model for suicide attempt, so as to improve the individual prediction accuracy. Data was collected from a wide range case-control suicide attempt survey. 659 serious suicide attempters (case group) were randomly recruited through the hospital emergency and patient registration system from 13 rural counties in China. Each case was matched the control by same community, gender, and similar age (± 2 ages). Face to face interviews were conducted for each subject with structured questionnaire. Logistic regression was applied to preliminarily screen the factors and BPNN was used to establish the prediction model of suicide attempt. Multivariate logistic regression indicated that family history of suicide (OR = 4.146), mental problem (OR = 3.876) Low education level, poor health, aspiration strain, hopelessness, impulsivity, depression are the risk predictors and social support, coping skills, healthy community are the protect predictors for suicide attempt. Repetitious data simulation process of BPNN indicated that three-layer BPNN with 9 hidden layer neurons is the optimal prediction model. The sensitivity (67.6%), specificity (93.9%), positive predictive value (86.0%), negative predictive value (84.1%), total coincidence rate (84.6%) manifested that it is excellent to distinguish suicide attempt case. The BPNN method is applicative, feasible, credible and good discriminative effect for suicide attempt. The BPNN established has significant clinical meaning for the clinical psychiatrist and lay theoretical foundation for artificial intelligence expert assisted diagnosis system for suicide attempt in the future.

Research on Intelligent Manufacturing Problem in Process Industry

In this paper, the practical problems of blast furnace iron making are considered, taking into account the influence of sulfur content [S], coal injection volume PML, blast volume FL and molten iron silicon content [Si], and extracting parameters according to data and data analysis. Because the parameters of this problem are more parameters and the nonlinear relationship is stronger, the control of parameters in the BP neural network model has become the focus of this paper. This paper finds that the BP neural network prediction model has a relatively high success rate for both numerical and furnace temperature rise and fall directions, and has wide applicability. At the same time, in the context of intelligent manufacturing in the process industry, the blast furnace iron making process can be greatly optimized, which is reflected in the reduction of raw material usage, production increase, and emission reduction.

Optimizing the Borrowing Limit and Interest Rate in P2P System: From Borrowers’ Perspective

P2P (peer-to-peer) lending is an emerging online service that allows individuals to borrow money from unrelated person without the intervention of traditional financial intermediaries. In these platforms, borrowing limit and interest rate are two of the most notable elements for borrowers, which directly influence their borrowing benefits and costs, respectively. To that end, this paper introduces a BP neural network interval estimation (BPIE) algorithm to predict the borrowers’ borrowing limit and interest rate based on their characteristics and simultaneously develops a new parameter optimization algorithm (GBPO) based on the genetic algorithm and our BP neural network predictive model to optimize them. Using real-world data from http://ppdai.com, the experimental results show that our proposed model achieves a good performance. This research provides a new perspective from borrowers in exploring the P2P lending. The case base and proposed knowledge are the two contributions for FinTech research.

A New BP Neural Network Model for the Prediction Problem of Equally Spaced Time Sequences and Its Application

For the prediction of equally spaced time sequences, this paper proposes a new construction method for training datasets based on the method which is used to determine parameters of the ARIMA model and builds a new BP neural network predictive model. For the actual data of annual power consumption in China from 1980 to 2016, data from 1980 to 2013 are chosen for this paper to construct the training dataset, and then the model proposed in this paper and the standard BP model are used to predict the power consumption from 2014 to 2016. Finally, after comparing the results obtained from the model proposed in this paper and the standard BP model, the prediction accuracy obtained by the model used here is found to be no more than 2%.

Forming Quality Forecasting for Inner Threads Copper Tubes Based on Neural Network

Aims at the defects such as folding and gaps, this paper analyze the relationship between the main processing parameters and inner thread forming quality. Using the fitness function of genetic algorithm to calculate individual fitness value, then the initial weights and thresholds of the Neural Network are assigned, BP Neural Network prediction model based on genetic algorithm is established. The results show that the Neural Networks model has high convergence speed and forecast accuracy, can realize the inner thread forming prediction accurately and improve effectively the forming quality of inner thread.

A New Hybrid Model Based on Fruit Fly Optimization Algorithm and Wavelet Neural Network and Its Application to Underwater Acoustic Signal Prediction

The local predictability of underwater acoustic signal plays an important role in underwater acoustic signal processing, and it is the basis of nonstationary signal detection. Wavelet neural network model, with the advantages of both wavelet analysis and artificial neural network, makes full use of the time-frequency localization characteristics of wavelet analysis and the self-learning ability of artificial neural network; however, this model is prone to fall into local minima or creates convergence. To overcome these disadvantages, a new hybrid model based on fruit fly optimization algorithm (FOA) and wavelet neural network (WNN) is proposed in this paper. The FOA-WNN prediction model is constructed by optimizing the weights and thresholds of wavelet neural network, and the model is applied to underwater acoustic signal prediction. The experimental results show that the FOA-WNN prediction model has higher prediction accuracy and smaller prediction error, compared with wavelet neural network prediction model and BP neural network prediction model.

Reliability Prediction of Power Communication Network Based on BP Neural Network Optimized by Genetic Algorithm

The power communication network is a communication network which serves the power system. Its reliability has an important influence on the safe and stable operation of the power system. It is of great practical significance to study the reliability of the power communication network. However, the current research on the power communication network lag, the reliability of the quantitative measurement, in practice, mostly artificial evaluation of the business, subjective and arbitrariness. In order to achieve the transition from expert to data-driven, this paper is based on the business segment, with two sites and between a cable as a research object. Based on the construction of the index system, BP neural network and genetic algorithm optimization BP neural network prediction model are established respectively. Using the real data and simulation data of the national grid in Jiangxi Province, the empirical experiment is carried out to compare the effect of the model. The example shows the effectiveness of the model.

Cotton Yarn Quality Prediction Based on Mind Evolutionary Neural Network

In order to forecast the quality of cotton yarn based on the cotton fiber indexes more accurately, a yarn quality forecasting model based on Mind Evolutionary Algorithm (MEA) was used to optimize the BP neural network prediction of yarn strength and yarn evenness. The model adopts the Mind Evolutionary Algorithm to complete the BP network weights and threshold optimization, giving full play to the advantages of this method in the field of global optimization. The results show that the BP neural network prediction model based on Mind Evolutionary Algorithm to optimize is superior to the single BP neural network prediction model.

Regional carbon emission evolution mechanism and its prediction approach driven by carbon trading – A case study of Beijing

Resources and environmental issues have become the main obstacles to the global sustainable development. For example, the global warming and paroxysmal environmental problems induced by fossil energy consumption are highlighted in recent years. As a big energy consumption and carbon emission country, China has tried to establish and implement the carbon emission trading mechanism in order to adjust the economic development patterns, optimize the energy structure and fulfill the emission goals. This mechanism has played a certain role in guiding and supporting the energy saving and carbon emission reduction. With the wide popularization and acceptance of low-carbon and green development, the advantages and the benefits of regional carbon emission trading mechanism will gradually show up with more trading activities and enterprise participation. Therefore, it’s imperative to explore the carbon emission trading mechanism and provide relative suggestions for government and enterprises. For analyzing the carbon emission trading mechanism in China, the development situations of economy, energy and policy were reviewed firstly. Then, based on the direct and indirect emissions, the carbon emission measurement method was used to study the emission trends of Beijing and pilot areas. With the system dynamics analysis model, the key factors and evolution circuits influential to the carbon emission mechanism were identified from the aspects of society, energy, economy and environment. The factors were further selected by extended STIRPAT model and ridge regression model in order to construct the BP Neural Network prediction model of carbon emissions. Meanwhile, take Beijing as an example, seven different development scenarios were set to test the rational levels of carbon emissions in the next five years. At last, with the prediction and scenario analysis results, some policy advices were discussed and provided theoretical and practical references for reasonable and efficient carbon emission trading.

Application of clustering analysis in the prediction of photovoltaic power generation based on neural network

In order to select effective samples in the large number of data of PV power generation years and improve the accuracy of PV power generation forecasting model, this paper studies the application of clustering analysis in this field and establishes forecasting model based on neural network. Based on three different types of weather on sunny, cloudy and rainy days, this research screens samples of historical data by the clustering analysis method. After screening, it establishes BP neural network prediction models using screened data as training data. Then, compare the six types of photovoltaic power generation prediction models before and after the data screening. Results show that the prediction model combining with clustering analysis and BP neural networks is an effective method to improve the precision of photovoltaic power generation.

The research of emergency managers’ risk attitude based on genetic neural network

Since the 21st century, the sudden emergencies occurred around the world. The decision which emergency managers made in the emergency environment is actually a risk decision-making. And decision-makers will be affected by its risk attitude in the risk decision-making. The paper puts forward a kind of forecasting method of risk attitudes of emergency managers, which are influenced by various factors such as their own gender, age, training experiences, treatment experiences, actual average accuracy and self-confidence and so on. Based on the questionnaire results from Kunming and Wuhan, the above six factors were chosen as the input variables of the BP neural network model and the risk attitude values as the output variable. Genetic algorithm was used to optimize the weights and thresholds of the neural network and establish BP Neural network prediction model of emergency managers. The results of risk attitude value prediction show that the GA-BP is a more effective and accurate method to predict emergency managers’ risk attitude, which can provide the reference for the risk managers’ risk attitude prediction and more efficient management.

English

Near infrared spectroscopy (NIRS) technology is widely used on agricultural products for quality detection, classification and variety identification due to its rapid speed and high-efficiency. NIRS experiments were conducted to identify varieties of DUN millet, JIN 21 millet and 5 other types of millet. The NIRS characteristic curves and data of millet samples were collected. The spectroscopic data on different types of millet were analyzed by discriminant analysis, principal component analysis and neural network technology. The calibration set correct classification was 98.9%. A BP neural network prediction model for millet was also built. It was found that the forecast results of original wave spectrum prediction model were best, with its correlation coefficient of validation (Rv) at 0.9999, the standard error of prediction (SEP) was 0.0191 and the root mean square error of prediction (RMSEP) was 0.0189. Moreover, the Rv of first derivative spectra was 0.9976, the SEP and RMSEP were 0.1043 and 0.1437, respectively, and the Rv, SEP and RMSEP of second derivative spectra were 0.9835, 0.28735 and 0.2720 respectively. This study laid the foundation for identification of millet varieties by NIRS. Key words: Millet, near infrared spectroscopy (NIRS), principal component analysis, neural network prediction, variety identification.

Supercritical water heat transfer coefficient prediction analysis based on BP neural network

14 groups of experimental data have been collected for determining the heat transfer coefficient within the scope of supercritical water pressure. On the basis of experimental data, the BP neural network predictive model has been built for the determination of supercritical water heat transfer coefficient. Using the BP neural network prediction model, a study has been conducted to determine effect of changing certain parameters such as heat flux, mass flux, pipe diameter and pressure on the heat transfer coefficient of supercritical water. The prediction results show that the mean error, standard deviation and the root mean square error are 0.179032187%, 4.128897482% and 0.179032187% respectively, the regression coefficient R is 0.97165 and the maximum error is 16.06332%. The trained BP neural network prediction model can applied for better prediction and understanding of the heat transfer coefficient of supercritical water. The prediction range is as follow: specific enthalpy is 451.30–3135.87kJ/kg, mass flux is 400–3000kg/m2s, heat flux is 200–2960kW/m2, pressure is 22.6–31MPa and diameter is 0.7–38.1mm.

Prediction of annual water consumption in Guangdong Province based on Bayesian neural network

In the context of the implementation of the most stringent water resources management system, the role of water demand forecasting for regional water resources management is becoming increasingly significant. Based on the analysis of the influencing factors of water consumption in Guangdong Province, we made the forecast index system of annual water consumption, and constructed the forecast model of annual water consumption of BP neural network, then optimized the regularization BP neural network in utilization rate of water. The results showed that the average absolute percentage error of Bayesian neural network prediction model and BP neural network prediction model is 0.70% and 0.46% respectively. BP neural network model by Bayesian regularization is more ability to improve the accuracy of about 0.24%, more in line with the regional annual water demand forecast high precision requirements. Take the planning index value of Guangdong Province’s thirteen five plan into Bayesian neural network forecasting model, and its forecast value is 45.432 billion cubic meters, which will reach 456.04 billion cubic meters of red water in Guangdong Province in 2020.

Sunspots Time-Series Prediction Based on Complementary Ensemble Empirical Mode Decomposition and Wavelet Neural Network

The sunspot numbers are the major target which describes the solar activity level. Long-term prediction of sunspot activity is of great importance for aerospace, communication, disaster prevention, and so on. To improve the prediction accuracy of sunspot time series, the prediction model based on complementary ensemble empirical mode decomposition (CEEMD) and wavelet neural network (WNN) is proposed. First, the sunspot time series are decomposed by CEEMD to obtain a set of intrinsic modal functions (IMFs). Then, the IMFs and residuals are reconstructed to obtain the training samples and the prediction samples, and these samples are trained and predicted by WNN. Finally, the reconstructed IMFs and residuals are the final prediction results. Five kinds of prediction models are compared, which are BP neural network prediction model, WNN prediction model, empirical mode decomposition and WNN hybrid prediction model, ensemble empirical mode decomposition and WNN hybrid prediction model, and the proposed method in this paper. The same sunspot time series are predicted with five kinds of prediction models. The experimental results show that the proposed model has better prediction accuracy and smaller error.

Water Absorption Rate Prediction of PMMA and Its Composites Using BP Neural Network

Referring to water absorption rate of poly (methyl methacrylate) (PMMA) and its composites is hard to obtain under some working conditions, BP neural network prediction model was constructed. Regarding water absorption rate predictions of exfoliated PMMA/MMT nanocomposites in 0.1 mol/L H2SO4 solution, 0.1 mol/L NaOH solution and deionized water respectively as examples, the applicability of model established in water absorption rate prediction of PMMA and its composites was researched. The results show that the relative errors between prediction value obtained from model established and actual value of water absorption rate of composites soaking 63min in three kinds of mediums are 1.50%, 0.47% and 1.04% respectively, prediction accuracy is higher than that (relative errors are 3.89%, 3.40% and 4.43% respectively) obtained from GM (1, 1) model obviously. BP neural network can be used to predict water absorption rate of PMMA and its composites.

Research on the Prediction of Solar Energy Generation based on Measured Environmental Data

As a kind of renewable energy, solar power becomes more and more widely used as the power supply for large-scale datacenters to save the brown energy consumption and to reduce the overall cost. The prediction accuracy of solar energy generation becomes a fundamental issue in the research of how to efficiently manage the renewable energy resources. This paper explores the possible ways to predict the solar radiation intensity based on the assumption that it impacts the solar power generation proportionally. Through the analysis and research of photovoltaic power generation system, we explore the influence factors for solar radiation intensity, establish a relationship of solar radiation intensity and ambient temperature, time, humidity, wind speed in the forecasting model, and finally established the multivariate linear regression model and artificial neural network model. According to the two models, the environmental monitoring data measured at the Qinghai University are employed as the basis of the prediction of solar radiation intensity, and compared with the actual measurement data monitoring system. Experimental results show that, by using the BP neural network prediction model, the achieved accuracy is higher than other empirical model. The prediction method and good results provide a necessary foundation for future related research based on solar radiation values forecasts.

The Prediction of Food Safety Composite Index based on BP Neural Network and GA Algorithm

The study established a BP neural network prediction model to test the effect of the application to predict the food safety Index. The GA was used to optimize the weights and thresholds of BP neural network. The theoretical analysis and experimental results prove that the BP neural network prediction is feasible for the food safety Index. The index prediction has some value in the field of food index forecast.