Algorithm Optimized BP Neural Network Research Articles

Assessment of salmon sashimi processing conditions for Listeria monocytogenes cross-contamination and effectiveness of CLPSO-BP neural network model constructed for predicting microbial transfer

During preparation stages of ready-to-eat (RTE) foods, they can be cross-contaminated with Listeria monocytogenes which could result to outbreaks of foodborne listeriosis. In this study, the effects of gloves, cutting boards and cleaning methods on the transmission of L. monocytogenes during the processing of salmon sashimi were investigated. Subsequently, an improved particle swarm algorithm optimized BP neural network prediction model (CLPSO-BP) was developed based on Lévy flight and chaotic mapping. After processing contaminated salmons with gloves, we found that the surfaces of the gloves contained a significant amount of L. monocytogenes (5.24 log CFU/g), and 10.69% of the bacteria were subsequently transferred to uninfected salmon after being processed with the same gloves. Contamination of the processing tools with L. monocytogenes was reduced by up to 3 log CFU/g and the transfer rate was reduced by up to 50% after cleaning with room temperature water and detergent. In contrast, no bacteria were found after wiping the surfaces of the tools 70 °C hot water and detergent. The mean squared error (MSE) for the CLPSO-BP modeling tests was 0.1003, indicating accurate predictions. Overall, choosing an appropriate cleaning method and changing gloves regularly can help to avoid cross-contamination when processing RTE foods.

The situation and transformation of sports governance in national colleges and universities under the background of double reduction

Abstract This paper aims to improve the quality of sports development by using particle swarm optimization (PSO) based long and short-term memory network (LSTM) and support vector machine (SVM) based sports performance prediction model (SPPM) to realize the shift to information technology (IT)-based sports governance in the context of the “Double Reduction”. The effects of LSTM and PSO-LSTM are evaluated by testing individual exercise physiological data like heart rate and blood oxygen values. Test the effectiveness of particle swarm optimization neural network’s sports performance prediction model with sample data and conduct comparative experiments with multiple linear regression, genetic algorithm optimization BP neural network, and firefly optimization BP neural network, respectively. Analyzing the characteristics of the extracted samples and performing a paired-sample t-test on the sample size revealed the gap between service perceptions and expectations. The analysis shows that the overall expectation mean of parents is 4.482, and the actual perceived mean is 3.966. All the indexes fail to reach the ideal level of parents, which indicates that the delayed sports service proposed by the institutional sports needs to be further adjusted.

Research on downhole visible light modeling and localization based on point cloud technology

In this paper, we reconstruct the downhole visible light channel model based on point cloud technology. To make up for the defect that the reflective surface element is a virtual surface, the planar projection method and the idea of point-by-point insertion are combined to turn the point cloud data on the wall surface into a triangular mesh topology. In the meantime, the point cloud data of obstacles is utilized to determine the precise range of shadows cast by light. Subsequently, shadow coefficients are defined to derive new expressions for channel gain. In addition, the localization in a 6 m × 4 m × 3 m space is achieved through the utilization of the particle swarm algorithm optimized BP neural network (PSO-BP) localization algorithm. In scenarios where the walls exhibit unevenness and barriers are present, the root mean square positioning error is found to be 6.52 cm when taking primary reflection into consideration. Conversely, when solely relying on direct power, the positioning error increases to 9.92 cm. Consequently, it is important to consider the non-line-of-light (NLOS) link when conducting downhole visible light localization.

Prediction of Surface Residual Stresses after Laser Shock Processing on TC4 Titanium Alloy Using Different Neural Network Agent Models

Nowadays, it has become a trend to use finite element simulation instead of experimental processes, and this is widely used in the fields of structural mechanics, fluid mechanics, fracture mechanics, and so on. By replacing the experimental process with finite element simulation, we can reduce time and costs; however, when using finite element simulation, we need to define a series of settings, such as modeling, material assignment, environment settings, and many other operations. For laser shock processing intensification, the simulation experiment process is cumbersome and time-consuming. It involves performing neural network agent modeling, replacing finite element simulation with the learning and prediction capabilities of neural networks, learning by using some of the simulation results as a training sets for the neural network, and then learning by using the remaining simulation results as testing sets to test the predictive ability of the neural network agent model. TC4 titanium alloy was selected as the experimental material. Three kinds of neural network agent models, a genetic algorithm-optimized BP network, a strong classifier design based on BP_Adaboost, and an extreme learning machine, instead of finite element simulation experiments, were used to predict the residual stresses generated on the surfaces of the material under different laser shock parameters. Comparing the prediction performances of different neural network agent models, the genetic algorithm-optimized BP network shows the best prediction performance, and its prediction value matches well with the experimental value. The R2, RMSE, and MAE of the testing sets of the BP network optimized using the genetic algorithm were 0.9985, 44.4518, and 30.6285, respectively. The BP network agent model optimized using the genetic algorithm for laser shock parameters other than the 208 sets of data also had good prediction performance, and the predicted values were similar to the actual experimental results. The prediction results show that the BP network optimized using the genetic algorithm can predict the residual stresses on the surface of TC4 titanium alloy material under strengthening via laser shock processing; the genetic algorithm-optimized BP neural network agent model is more convenient and quicker compared to the finite element simulation, and the predicted value is also similar to the actual value. It can thus be used to replace finite element simulation by establishing a more convenient and quicker neural network agent model.

Hybrid mechanistic and neural network modeling of nuclear reactors

An accurate and efficient model is the foundation for dynamic characteristics analysis and control design of nuclear reactors. Nevertheless, the widely-used mechanistic models of nuclear reactors inevitably have some deviations from the actual reactors due to inaccurate model parameters and model assumptions. This paper proposes a hybrid mechanistic and neural network modeling method for nuclear reactors to eliminate or minimizing these deviations. The high-efficiency point reactor kinetics model was adopted as the mechanistic reactor core model, and the simulation results obtained using the commercial program RELAP5 were taken as the reference operational data, based on which the reactivity feedback in the mechanistic model that is difficult to be accurately determined during reactor operations was calibrated using a genetic algorithm optimized BP neural network. Dynamic simulation results of a Generation III large pressured water reactor under three typical reactivity change transients show that the core power and outlet coolant temperature obtained with the hybrid model match much better with the reference results than those of the mechanistic model, demonstrating that the hybrid modeling method can improve the accuracy of the mechanistic reactor model effectively. This study can provide a valuable reference for accurate and efficient modeling of complex systems.

Study on prediction model of liquid holdup based on back propagation neural network optimized by tuna swarm algorithm

ABSTRACT It is unavoidable that there will be liquid accumulation in the low-lying areas of the pipelines during the operation of wet gas pipelines. The existence of liquid accumulation can generate a variety of safety issues and, in extreme circumstances, accidents. The accurate calculation of liquid holdup in gas-liquid two-phase flow is of great significance for the study of flow pattern identification, pressure drop calculation, pigging cycle determination, hydrate prediction, wax deposition prediction, pipeline corrosion evaluation and prediction, and transportation efficiency calculation of gas pipelines. Therefore, it is crucial to predict the liquid holdup of wet gas pipelines. 2141 independent experimental data samples were collected and screened out from literatures. Based on the gray theory, gray relation analysis was carried out on the influencing factors of liquid holdup, and the factors with greater influence were selected as the influencing variables; the liquid holdup prediction model based on tuna swarm algorithm optimized BP neural network was established, with pipe diameter, inclination angle, apparent gas velocity, apparent liquid velocity, average temperature, average pressure, and liquid viscosity as input parameters, and liquid holdup as output parameter. Liquid holdup was predicted for upward inclined, downward inclined, and horizontal pipelines respectively. The results show that the prediction model of liquid holdup established in this paper has high accuracy, with the MAPE value of 5.3223%, RMSE value of 0.0213, and R 2 value of 0.9924 for upward inclined pipelines; the MAPE value of 10.1859%, RMSE value of 0.0174, and R 2 value of 0.9922 for downward inclined pipelines; the MAPE value of 4.8037%, RMSE value of 0.0113, and R 2 value of 0.9974 for horizontal pipelines. The predicted results are generally stable and have a wider scope of application, providing a new idea and approach for predicting the liquid holdup of wet gas pipelines.

Machine Learning-Based Quantitative Structure-Activity Relationship and ADMET Prediction Models for ERα Activity of Anti-Breast Cancer Drug Candidates

Breast cancer is presently one of the most common malignancies worldwide, with a higher fatality rate. In this study, a quantitative structure-activity relationship (QSAR) model of compound biological activity and ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) properties prediction model were performed using estrogen receptor alpha (ERα) antagonist information collected from compound samples. We first utilized grey relation analysis (GRA) in conjunction with the random forest (RF) algorithm to identify the top 20 molecular descriptor variables that have the greatest influence on biological activity, and then we used Spearman correlation analysis to identify 16 independent variables. Second, a QSAR model of the compound were developed based on BP neural network (BPNN), genetic algorithm optimized BP neural network (GA-BPNN), and support vector regression (SVR). The BPNN, the SVR, and the logistic regression (LR) models were then used to identify and predict the ADMET properties of substances, with the prediction impacts of each model compared and assessed. The results reveal that a SVR model was used in QSAR quantitative prediction, and in the classification prediction of ADMET properties: the SVR model predicts the Caco-2 and hERG(human Ether-a-go-go Related Gene) properties, the LR model predicts the cytochrome P450 enzyme 3A4 subtype (CYP3A4) and Micronucleus (MN) properties, and the BPNN model predicts the Human Oral Bioavailability (HOB) properties. Finally, information entropy theory is used to validate the rationality of variable screening, and sensitivity analysis of the model demonstrates that the constructed model has high accuracy and stability, which can be used as a reference for screening probable active compounds and drug discovery.

Research on soil moisture content combination prediction model based on ARIMA and BP neural networks

AbstractPredicting soil moisture accurately is the precondition of realizing accurate irrigation and improving the utilization rate of water resource and the necessary step of developing water‐saving agriculture, which can alleviate the water shortage in our agricultural effectively. In order to further improve the accuracy of soil water content prediction, a combined soil water content prediction model based on Autoregressive moving average model (ARIMA model) and back propagation neural network (BP neural network) neural network is proposed. The model considers the linear and nonlinear characteristics of soil water content data, combines them according to the characteristics of the model itself, gives full play to the advantages of ARIMA model and BP neural network. At the same time, two data smoothing methods were used to establish the ARIMA model, and the adaptive moment estimation algorithm (Adam algorithm) and mind evolutionary algorithm (MEA) optimization BP neural network model were used to propose an improved combined prediction model to predict soil water content data. The experimental results show that the average relative error of the improved combinatorial prediction model is 1.51%, which is 4.18%, 0.95% and 3.1% lower than the combinatorial prediction model, BP neural network model and ARIMA model, respectively, and the overall prediction effect is better, which can be used to save agricultural water and provide a strong basis for the development of water‐saving agriculture in China. At the same time, it can also ensure that crop production is increased and the purpose of national food security is guaranteed.

Simulation of the Ozone Concentration in Three Regions of Xinjiang, China, Using a Genetic Algorithm-Optimized BP Neural Network Model

Accurate ozone concentration simulation can provide a health reference for people’s daily lives. Simulating ozone concentrations is a complex task because near-surface ozone production is determined by a combination of volatile organic compounds (VOCs) and NOx emissions, atmospheric photochemical reactions, and meteorological factors. In this study, we applied a genetic algorithm-optimized back propagation (GA-BP) neural network, multiple linear regression (MLR), BP neural network, random forest (RF) algorithm, and long short-term memory network (LSTM) to model ozone concentrations in three regions of Xinjiang, China (Urumqi, Hotan, and Dushanzi districts) for the first time by inputting wind speed, humidity, visibility, temperature, and wind direction. The results showed that the average relative errors of the model simulations in the Urumqi, Hotan, and Dushanzi districts were BP (61%, 14%, and 16%), MLR (97%, 14%, and 23%), RF (39%, 11%, and 14%), LSTM (50%, 12%, and 16%), and GA-BP (16%, 4%, and 6%) and that the significance coefficients R2 were BP (0.73, 0.65, and 0.83), MLR (0.68, 0.62, and 0.74), RF (0.85, 0.80, and 0.88), LSTM (0.78, 0.74, and 0.85), and GA-BP (0.92, 0.93, and 0.94), respectively, with the simulated values of GA-BP being the closest to the true values. The GA-BP model results showed that among the 100 samples with the same wind speed, humidity, visibility, temperature, and wind direction data, the highest simulated ozone concentrations in the Urumqi, Hotan, and Dushanzi districts were 173.5 μg/m3, 114.3 μg/m3, and 228.4 μg/m3, respectively. The results provide a theoretical basis for the effective control of regional ozone pollution in urban areas (Urumqi), dusty areas (Hotan), and industrial areas (Dushanzi) in Xinjiang.

Intelligent Diagnosis Model of Alzheimer's Disease Based on PSO Algorithm Optimized BP Neural Network

In this paper, an intelligent diagnosis model of Alzheimer's disease is established based on the PSO algorithm optimized BP neural network. After descriptive analysis and normalization of patient-specific information characteristic data, the model was established by parameter selection of BP neural network, optimization of BP neural network by PSO algorithm and sensitivity analysis of neural network connection weights, and the intelligent diagnostic model of Alzheimer's disease was tested. The results showed that the model has strong applicability in terms of the degree of Alzheimer's disease in patients, and provides a certain reference basis for accurate diagnosis of the developmental stage of Alzheimer's disease.

Research on neural network wind speed prediction model based on improved sparrow algorithm optimization

The wind speed signal measured by the mechanical anemometer lags behind the wind speed at the wind wheel surface of the wind turbine in time. The wind speed reconstruction algorithm adopted by the ordinary lidar anemometer is insufficient. For the sake of enhancing the accuracy of wind speed forecast on the surface of wind turbines in wind farms for wind turbine pitch control strategies, an modified sparrow search algorithm (SSA) based on sinusoidal chaotic mapping is proposed to improve the BP neural network wind speed prediction model. According to the characteristics that lidar measure wind speed at multiple points, a multiple regression forecast model is build, and then the wind speed is predicted. Simulation tests were conducted using lidar wind measurement data from a wind farm in Xinjiang and analyzed and in contrast to a BP neural network wind speed prediction model. The experimental results indicated that the four error evaluation indicators of the Sine-SSA-BP algorithm are all smaller than the BP neural network. In contrast to the single BP neural network wind speed forecast model, sine chaotic mapping modified sparrow search algorithm optimized BP neural network has significantly improved prediction accuracy.

Machine Learning-Assisted Competency Modeling for Human Resource Management Jobs

The competency model has an important role in human resource management (HRM) as a scientific and objective talent assessment theory. With the widespread use of machine learning methods, machine learning assistance for HRM job competency assessment and determination provides a new way of thinking for talent assessment and management. The establishment of HR competency models based on machine learning methods can quickly and accurately help companies gain a competitive advantage. Based on this paper, we take Company C as the research object and establish the HR management job, competency model, based on a genetic algorithm optimized BP neural network (GA-BP). The results show that the model can be better applied to the analysis of the HR management job competency model of Company C. The prediction accuracy reaches 91.16% classification accuracy, which has a certain reference significance, and it has a certain reference and value for the HRM job industry.

Study on Prediction Model of Soil Nutrient Content Based on Optimized BP Neural Network Model

ABSTRACT Soil nutrient content is an important index to determine the amount of fertilizer. Traditional soil nutrient content is obtained by manual sampling, which greatly increases the cost of agricultural production input. In order to solve this problem, this paper studies the relationship between soil nutrients, fertilizer amount, yield and the next year’s soil nutrients, and establishes an optimized BP neural network model to realize the prediction of soil nutrient content. Aiming at the problem that the traditional BP neural network model will affect the prediction accuracy due to the different weights and thresholds, the Grey Wolf algorithm with reverse learning mechanism is introduced to obtain the optimal solution of BP neural network weights and thresholds. The soil nutrient content data collected in chenjiadian village, Nongan County, Jilin Province for five consecutive years were used to test the model. The data of the first four years were used as the training set and the data of the fifth year as the test set. The prediction results of BP neural network model, Grey Wolf algorithm optimized BP neural network model and Grey Wolf algorithm optimized BP neural network model with reverse learning mechanism were compared The prediction accuracy of BP neural network model optimized by Grey Wolf algorithm with reverse learning mechanism reached 88.7%, which was better than the first two models. It can provide basis for the fertilization decision of variable rate fertilization in the next year, so as to reduce the input of labor cost.

Using Genetic Algorithm and Particle Swarm Optimization BP Neural Network Algorithm to Improve Marine Oil Spill Prediction

Using Genetic Algorithm and Particle Swarm Optimization BP Neural Network Algorithm to Improve Marine Oil Spill Prediction

Photovoltaic Fuzzy Logical Control MPPT Based on Adaptive Genetic Simulated Annealing Algorithm-Optimized BP Neural Network

The P–U characteristic curve of the photovoltaic (PV) cell is a single peak curve with only one maximum power point (MPP). However, the fluctuation of the irradiance level and ambient temperature will cause the drift of MPP. In the maximum power point tracking (MPPT) algorithm of PV systems, BP neural network (BPNN) has an unstable learning rate and poor performance, while the genetic algorithm (GA) tends to fall into local optimum. Therefore, a novel PV fuzzy MPPT algorithm based on an adaptive genetic simulated annealing-optimized BP neural network (AGSA-BPNN-FLC) is proposed in this paper. First, the adaptive GA is adopted to generate the corresponding population and increase the population diversity. Second, the simulated annealing (SA) algorithm is applied to the parent and offspring with a higher fitness value to improve the convergence rate of GA, and the optimal weight threshold of BPNN are updated by GA and SA algorithm. Third, the optimized BPNN is employed to predict the MPP voltage of PV cells. Finally, the fuzzy logical control (FLC) is used to eliminate local power oscillation and improve the robustness of the PV system. The proposed algorithm is applied and compared with GA-BPNN, simulated annealing-genetic (SA-GA), particle swarm optimization (PSO), grey wolf optimization (GWO) and FLC algorithm under the condition that both the irradiance and temperature change. Simulation results indicate that the proposed MPPT algorithm is superior to the above-mentioned algorithms with efficiency, steady-state oscillation rate, tracking time and stability accuracy, and they have a good universality and robustness.

Rapid and accurate detection of starch content in mixed sorghum by hyperspectral imaging combined with data fusion technology

AbstractSorghum is a type of brewing material. The starch content of different kinds of mixed sorghum affect the quality and yield of liquor. Therefore, an accurate and efficient detection of its starch content is of great significance to obtain high‐quality and high‐yield of liquor. Based on the data of single visible light (Vis), near‐infrared (NIR), and Vis and NIR fusion, this study develops a genetic algorithm optimization BP neural network (GA‐BPNN) model and a particle swarm optimization support vector machine model. It is deduced that the model based on the data from the Vis and NIR fusion has the highest performance in predicting the starch content of mixed sorghum. For the prediction of amylose, the GA‐BPNN model developed after using the Pearson correlation coefficient to select the characteristic wavelength for fusion, has the highest performance (RMSEP = .0298 and = .9948). For the prediction of amylopectin, the GA‐BPNN model developed after fusion of spectral features extracted by principal component analysis, has the highest performance (RMSEP = .0213 and = .9985). In conclusion, the hyperspectral imaging combined with data fusion can rapidly and accurately detect the starch content of mixed sorghum.Practical ApplicationsAs the single raw material of Maotai‐flavor liquor and the main raw material of Luzhou‐flavor liquor, the amylopectin content of sorghum directly affects the quality of liquor. Its amylose content also affects the yield of liquor. Because the different varieties of sorghum have different amylose and amylopectin content, liquor manufacturers often use several mixed varieties of sorghum as a brewing material. In order to ensure the quality and yield of liquor, it is particularly crucial to detect the amylose and amylopectin content variables of blended sorghum. The traditional sorghum starch content determination methods, such as the chemical detection methods and near infrared spectroscopy, have the disadvantages of destructiveness, low efficiency, and low detection accuracy. In the process of liquor brewing, the traditional detection methods cannot guide the timely adjustment of brewing process parameters. The hyperspectral imaging technology is widely used in food material detection, due to its fast and nondestructive advantages. The data fusion technology can fuse data from different sources of the same object to be detected, in order to obtain more comprehensive data for the development of accurate prediction models. This study uses the hyperspectral imaging combined with data fusion in order to quickly and accurately predicts the starch content (amylose, amylopectin) of mixed sorghum, which has a guiding significance for the timely adjustment of process parameters in the brewing process of liquor. In addition, it provides an accurate method for the component detection of other grains.

Prediction model of low cycle fatigue life of 304 stainless steel based on genetic algorithm optimized BP neural network

The low cycle fatigue life of 304 stainless steel is an essential basis for safety assessment. Usually, there is a complex nonlinear relationship between fatigue life and influencing factors, which is difficult to be predicted by traditional fatigue life models. Based on this, the BP algorithm and genetic optimization algorithm (GA) for the fatigue life prediction problem of 304 stainless steel is proposed. Based on the existing large amount of test data, the fatigue life of 304 stainless steel material is predicted by using BP and GA-BP learning models. The results show that the GA-BP prediction model is more flexible, the correlation coefficient R reaches 0.98158, the prediction data are within the 2 times error limit and closer to the ideal line, and the model prediction is better.

Harmonic detection technology based on ant colony optimization BP neural network

At present, ip-iq algorithm based on instantaneous reactive power theory is the most widely used in the field of harmonic detection.However, the ip-iq algorithm based on instantaneous reactive power has coordinate transformation and inverse transformation, which leads to cumbersome calculation.the existence of low-pass filter leads to delay and poor dynamic performance of the algorithm.The harmonic detection based on BP neural network has the advantages of simple calculation, fast detection speed, strong adaptive ability and good dynamic performance.However, the initial weights and thresholds are random, so it is easy for the network to fall into local optimal solution during training, resulting in low detection accuracy. This paper presents a harmonic detection method based on ant colony algorithm optimized BP neural network. ACO-BP, BP and ip-iq algorithms are compared by Simulink simulation. The results show that ACO-BP harmonic detection algorithm is feasible and superior

Fault Diagnosis of Automobile Engine Based on Improved BP Neutral Network

With the continuous enrichment of automobile functions and the complexity of automobile structure, the difficulty of automobile fault diagnosis is constantly increasing. The study of fault diagnosis methods with high real-time performance, accuracy, and predictability is of great significance to improve automobile safety performance and ensure driving safety. Since the convergence speed of the traditional BP neural network algorithm is slow and the accuracy is insufficient in the process of automobile engine fault diagnosis, this paper improves convergence speed of the algorithm by introducing the momentum term, and the weights and thresholds of the neural network are optimized by using GA selection, crossover, and genetic characteristics, to propose a genetic algorithm (GA) optimization BP neural network fault diagnosis method. The average absolute error of the traditional BP neural network algorithm is 0.5976, while the average absolute error of the improved BP neural network algorithm in this paper is 0.1027. The comparative simulation results show that the proposed improved algorithm is better than the traditional BP neural network algorithm in diagnosis precision, accuracy, and other key indicators.

Quality prediction of internal thread cold extrusion based on genetic algorithm optimized BP neural network

The extrusion process parameters have a great impact on the quality of the extrusion thread. In order to ensure the extrusion quality and the smooth progress of the extrusion process, a prediction model based on BP-GA neural network is established. The model takes the diameter of the bottom hole, extrusion speed, and friction coefficient as the input layer, and the extrusion torque, extrusion temperature, and tooth height as the output layer. After training the model with sample data, the extrusion internal thread quality prediction is carried out. The results show that the BP-GA neural network prediction model has a high accuracy in predicting the extrusion torque, extrusion temperature and tooth height rate during the cold extrusion of internal threads. The error between the experimental value and the predicted value of the extrusion torque is between 10% and 15%, the experimental value of the extrusion temperature is consistent with the predicted value, and the error between the experimental value and the predicted value of the tooth height rate is less than 5%. The BP-GA neural network prediction model can accurately predict the extrusion temperature, extrusion torque, and tooth height rate, providing a new way for the re-search of internal thread cold extrusion technology.