Construction and verification of color fundus image retinal vessels segmentation algorithm under BP neural network

The Back Propagation (BP) neural network genetic algorithm was used to identify alphabet, and the new algorithm combine the advantages of both genetic algorithm and the BP neural network. Genetic learning algorithm was used for the global optimization and BP training algorithm to accurately optimize the neural network weights and training the neural network to learn letter recognition algorithm. Add-noise alphabet of MATLAB simulation results show that the new network error recognition rate reduced by 10% compared to BP neural network and the recognition speed is also faster than the traditional BP neural network with accuracy and fast convergence.

In order to improve the accuracy of port container throughput prediction, an improved ABC-BP prediction model based on Artificial Bee Colony (ABC) was proposed. With the advantage of global search ability and difficulty in local optimization, the weights and thresholds of Back Propagation (BP) neural network are optimized, and the optimal weights and thresholds in the network are finally determined. Referring to the existing research results, Qingdao 2014-2019 (24 quarters) GDP, port throughput, total foreign trade import and export volume were selected as network input, and Qingdao port 2014-2019 port container throughput statistics were used as network output, a total of 24 sets of data were constructed, so as to build a BP neural network prediction model. The first 20 groups of data were used as the training set, and the last 4 groups of data were used as the test set for instance verification. The results show that, compared with the traditional BP neural network and the BP neural network prediction model optimized by Genetic Algorithm (GA), the ABC-BP model can significantly improve the prediction accuracy of port container throughput.

Internal leakage rate prediction and failure diagnosis of buried pipeline ball valve based on valve cavity pressure detection

Software aging is a common phenomenon that exists in systems that require long periods of operation, especially in Internet-of-Things environments. The back propagation (BP) neural network has been adopted widely to predict the trend of software aging. However, the weight and threshold of the BP neural network are randomly initialized, so it is easy to get the unsatisfactory local optimal solutions and the convergence speed of computing is slow. In this paper, we propose a novel software aging prediction method using the artificial bee colony algorithm to optimize the BP neural network model for achieving better software aging prediction accuracy. The experiment results show that our method fits the prediction trend of software aging more accurately than the traditional BP neural network, and our method also has faster convergence speed and more stable prediction results.

Stock market crashes bring about huge economic loss, so predicting the stock market crashes is extremely important for both the investors and practitioners. Back propagation (BP) neural network performed greatly in data adaptability and fitting, it has been widely used for forecasting study in various fields. However, given the low frequency and lack of periodicity of stock market crashes, the BP neural network can hardly predict the intensively fluctuated stock prices accurately during the crashes. To resolve this issue, we introduced BP neural network into Log-periodic power law model, proposed the Log-periodic power law hybrid model based on the BP neural network. This combined approach could further capture the fluctuation trend and predict the volatility during the stock market crashes better. The experimental results showed that this hybrid model could predict the stock prices in different stock markets with a slightly increased accuracy. Moreover, its performance was more stable compared with BP neural network.

The NVH (Noise, Vibration, and Harshness) characteristics of new energy vehicles are the key indexes to measure interior comfort. The multi-stage gear reducer in the transmission system is the primary source of vibration and noise. The parameterized 3D model of the multi-stage gear transmission system of the new energy vehicle was established through Romax software, and the comprehensive gear modification method of the tooth direction combined with the tooth profile was built. Then a complete simulation analysis process is established to solve the maximum vibration acceleration of the multi-stage gear transmission system under constant speed condition, to obtain the simulation data of two-stage gear set under different modification parameters. The traditional BP (Back Propagation) neural network is optimized and improved through the optimal selection of network parameters combined with Bayesian regularization. Based on the optimized BP neural network, a modified parameter-vibration noise prediction model is constructed. Finally, the GA (Genetic Algorithm) optimization algorithm is used to solve the prediction model to obtain the optimal combination of modification parameters aiming at the minimum vibration acceleration, the effectiveness and reliability of the modified design are verified through actual simulation. It provides ideas and a basis for the research on vibration and noise reduction of multi-stage gears.

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 proposes a new method to detect and identify foreign matter mixed in a plastic bottle filled with transfusion solution. A spin-stop mechanism and mixed illumination style are applied to obtain high contrast images between moving foreign matter and a static transfusion background. The Gaussian mixture model is used to model the complex background of the transfusion image and to extract moving objects. A set of features of moving objects are extracted and selected by the ReliefF algorithm, and optimal feature vectors are fed into the back propagation (BP) neural network to distinguish between foreign matter and bubbles. The mind evolutionary algorithm (MEA) is applied to optimize the connection weights and thresholds of the BP neural network to obtain a higher classification accuracy and faster convergence rate. Experimental results show that the proposed method can effectively detect visible foreign matter in 250-mL transfusion bottles. The misdetection rate and false alarm rate are low, and the detection accuracy and detection speed are satisfactory.

Aiming at the output current of grid-connected inverter of LCL filter, this paper put forward a PR (Proportion Resonant) control technology based on BP (Back Propagation) neural network. Combining the traditional digital PID control technology and the novel PR control technology, an increment PR controller algorithm as well as its parameter self-tuning method based on BP neural network were proposed, Simulation experiment, confirmed that the output current of gird-connected inverter controlled by PR control technology based on BP neural network had better steady state adaptive ability and self-learning ability. Trained results turn out to be a good effect of control of grid-connected current.

ecause of the importance of dust abatement by sprayer, this paper studies the characteristic of fogdrop generated by one kind of nozzle on basis of Back Propagation (BP) Neural Network, using Marvin-3000 type laser granularity instrument in lab. It is pointed that the maximum and minimum errors of widely used BP Neural Network are 2.18% and 0.61%, when we compute the fogdrop diameter computing repeatedly. In more general case, if the nozzle diameter change, the maximum and minimum errors using BP Neural Network are 1.92% and 0.34% by comparing with other’s work, while the errors are 2.13% and 1.50% when pressure change. The experimental results show that BP neural network is an effective tool to predict the variation of the non-linear fogdrop diameter. Furthermore, it is potential to be used in other kinds of fogdrop and real industry application.

Icing condition prediction of wind turbine blade by using artificial neural network based on modal frequency

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.

After analyzing effect elements of University Science Research Capability (USRC) based on dynamic capability theory, combined the substance of university science research with the highly self-organized, self-adapted and self-learned characteristics of Back Propagation (BP) Neural Network, the paper conducts a research on evaluation of USRC, in which an evaluation index system of USRC is constructed and a 15-7-1-typed BP Neural Network with three layers is presented to evaluate USRC, which provides a BP Neural Network-based methodology for evaluation of USRC with multiple inputs.

Reentry guidance is one of the key technologies in hypersonic vehicle research field. In addition to the constraints on its final position coordinates, the vehicle must also impact the target from a specified direction with high precision. And therefore the adaptability of guidance law is critical to control the velocity of hypersonic vehicle and firing accuracy properly in different surroundings of large airspace. In this paper, a new adaptive guidance strategy based on Back Propagation (BP) neural network for the reentry mission of a generic hypersonic vehicle is presented. Depending on the nicer self-learn ability of BP neural network, the guidance law considers the influence of biggish mis-modeling of aerodynamics, structure error and other initial disturbances on the flight capability of vehicle. Consequently, terminal position accuracy and velocity are guaranteed, while many constraints are satisfied. Numerical simulation results clearly bring out the fact that the proposed reentry guidance law based on BP neural network is rational and effective.

This paper presents a neural network method to locate common fault exactly in a distribution power system (DPS) with renewable sources. The back propagation (BP) neural network method is applied to identify patterns of voltage and current measured from distribution branches. The input matrix of BP network consists of the voltage and current values, which can identify the accurate fault position. The fault location of a common short-circuit fault is analyzed thoroughly in an active distribution network (ADN) with the renewable power sources. Simulation results prove the feasibility and usefulness of the fault location method based on the BP neural network, wherein the fault location accuracy can reach 0.09%.