Fuzzy BP Neural Network Research Articles

Comprehensive Evaluation of Marine Ship Fires Risk Based on Fuzzy Broad Learning System

Ship fires exhibit the main characteristics of a high possibility of occurrence, large load, fast spreading, high difficulty in extinguishing, and serious losses. Therefore, once a fire occurs, it will cause huge loss in terms of economic and personnel safety. Firstly, a ship fire risk evaluation indicator system was constructed based on the causes and severity of the fires. Secondly, a comprehensive evaluation method for the fuzzy broad learning system (FBLS) was proposed. The fuzzy system was used to implement feature mapping on the input data, and the extracted fuzzy features were further input into the BLS enhancement layer. A fuzzy broad learning neural network structure was constructed by combining fuzzy features, feature nodes, and enhancement nodes. The method was applied to the field of risk assessment for the first time, and is a reference for subsequent studies. Finally, the risk levels of ship fires were classified and compared with evaluation methods such as fuzzy support vector machine (FSVM) and Fuzzy BP neural network (FBPNN) to demonstrate effectiveness and accuracy. The proposed FBLS method was used to predict actual cases, and the results showed consistency with the level determined by the accident investigation report published by the Maritime Bureau Administration.

Retracted: Integrated Evaluation of Corporate Investment Decision Performance Based on Fuzzy Sets and BP Neural Networks

Retracted: Integrated Evaluation of Corporate Investment Decision Performance Based on Fuzzy Sets and BP Neural Networks

Retraction Note: Multi-level Dynamic Fuzzy Evaluation and BP Neural Network Method for Performance Evaluation of Chinese Private Enterprises

Retraction Note: Multi-level Dynamic Fuzzy Evaluation and BP Neural Network Method for Performance Evaluation of Chinese Private Enterprises

Research on the Multimodal Digital Teaching Quality Data Evaluation Model Based on Fuzzy BP Neural Network.

We propose in this paper a fuzzy BP neural network model and DDAE-SVR deep neural network model to analyze multimodal digital teaching, establish a multimodal digital teaching quality data evaluation model based on a fuzzy BP neural network, and optimize the initial weights and thresholds of BP neural network by using adaptive variation genetic algorithm. Since the BP neural network is highly dependent on the initial weights and points, the improved genetic algorithm is used to optimize the initial weights and thresholds of the BP neural network, reduce the time for the BP neural network to find the importance and points that satisfy the training termination conditions, and improve the prediction accuracy and convergence speed of the neural network on the teaching quality evaluation results. The entropy value method, a data-based objectivity evaluation method, is introduced as the guidance mechanism of the BP neural network. The a priori guidance sample is obtained by the entropy method. Then, the adaptive variational genetic algorithm is used to optimize the BP neural network model to learn the a priori sample knowledge and establish the evaluation model, which reduces the subjectivity of the BP neural network learning sample. To better reflect and compare the effects of the two neural network evaluation models, BP and GA-BP, the sample data were continued to be input into the original GA and BSA to obtain the evaluation results and errors; then, the evaluation results of the two evaluation models, BP and GA-BP, were compared with the evaluation results of the two algorithms, GA and BSA. It was found that the GA-BP neural network evaluation model has higher accuracy and can be used for multimodal digital teaching quality evaluation, providing a more feasible solution.

Integrated Evaluation of Corporate Investment Decision Performance Based on Fuzzy Sets and BP Neural Networks

Enterprise investment decision performance evaluation is a complex system, which is affected by many factors. Based on the consideration of corporate strategy and stakeholders, a six-level index system is designed to evaluate the performance of corporate investment decision-making. The index-selection method combining benchmarking management and principal component analysis is established, and the enterprise management index analysis method based on neural network and fuzzy decision-making is designed. The multiattribute decision-making problem in enterprise performance evaluation is solved by using the triangular fuzzy-weighted Einstein-Bonferroni mean (TF-WEBM). The algorithm is suitable for multiattribute decision-making in a triangular fuzzy environment. Finally, the effectiveness of this method is verified by an enterprise performance evaluation example.

The establishment of prediction model for soil liquefaction based on the seismic energy using the neural network

In the development of the prediction model for soil liquefaction, compared to the stress-based method, the energy-based methods proposed and developed in recent years are closer to the essence of soil liquefaction which is about the energy dissipation. Therefore, considering the weak nonlinear relationship found by the previous research, the fuzzy neural network (FNN) and BP neural network (BPNN) were adopted to try to obtain a prediction model which is the most proper to this nonlinear relationship. Firstly, the database including 284 cases obtained from laboratory test was divided into 3 separate groups denoted as training, validation set and testing sets by the ratio of 5:1:1; then, the FNN model and BPNN model were iterated to determine the model parameter by referring to the variation of fitness value and relative error of validation set; at the same time, the optimization algorithm of genetic algorithm (GA) was adopted to BPNN to find the best coefficients; besides, the parameter of \({C}_{c}\) and \({D}_{50}\) was respectively excluded from the database to test their influence degree according to the prediction error; finally, six prediction results of FNN and genetic algorithm BP neural network (GABP) were compared with the previously proposed models. The results showed that the relationship of capacity energy to the influencing parameters could not be fitted as a fully linear relationship; the FNN model can learn the role of \({C}_{c}\) in affecting the capacity energy while the GABP model needs not to take it into account; the FNN and GABP model all fitted a good weakly nonlinear relationship for the capacity energy, and the GABP model is a better prediction model for capacity energy so far.

Research on Dynamic Control Method of Loom Spindle Braking System Based on Fuzzy Neural Network

Aiming at the problems of slow response speed of loom electromagnetic braking system and low accuracy of parking angle after braking, resulting in driving marks and thin and dense roads of fabrics, a braking control system based on fuzzy theory and BP neural network is proposed to improve the response speed and control accuracy of the braking system. Firstly, the transmission torque in the braking process of the loom is dynamically analyzed by establishing the mathematical model of electromagnetic braking and the Ansoft electromagnetic finite element model. The causes of braking angle slip are explored, and the driving mechanism of the electromagnetic clutch control circuit based on PWM pulse is analyzed. Based on the control strategy of excitation current, by introducing the fuzzy theory and BP neural network, a fuzzy electromagnetic braking control system based on a neural network is proposed to improve the adaptive ability of the system. At the same time, the IBA-BP algorithm is used to train the neural network to avoid the generation of local optimization and improve the control accuracy of the system. The simulation and experimental results show that compared with PID control and fuzzy PID control, the neural network method based on fuzzy theory has a smaller braking slip angle, and the accuracy of parking angle after braking action is less than 10 °, which improves the control accuracy of loom electromagnetic braking system and weaving quality.

Exchange Rate Transaction of International Trade Goods Based on Fuzzy Granulation and Deep Learning

The change of international trade goods exchange rate transaction has an impact on economic operations and economic stability. Therefore, an international trade goods exchange rate transaction based on fuzzy granulation and in-depth learning is proposed. Based on fuzzy information granulation and BP neural network, this paper analyzes the interest rate evaluation theory. For the future expectation of currency exchange rate, portfolio equilibrium determines the proportional relationship of each component in the portfolio and analyzes the impact of asset price and exchange rate change according to this relationship. Then, it points out the risk evaluation index system, calculates the risk degree of exchange rate transaction of international trade goods, and then evaluates the risk of exchange rate transaction of international trade goods. It completes the research on exchange rate transactions of international trade goods based on fuzzy granulation and in-depth learning. The experimental results show that excessive exchange rate fluctuation will bring the same proportion fluctuation to the asset price in the financial market, and the coordination between exchange rates and the coordination of exchange rate and asset price can promote the steady growth of national economy.

Modeling and optimization of OC fault diagnosis for inverters based on GR-PTA-BN

In order to diagnose the open circuit (OC) faults of IGBTs in inverters quickly and accurately, the paper puts forth a novel OC fault diagnosis methodology of IGBTs stated as GR-PTA-BN organically combining a genetic algorithm-rough set attribute reduction (GR) method with a precision and time performance evaluation algorithm (PTA) based on Bayesian network (BN). For the feature attributes extracted from fault signals, the GR method is employed to reduce the redundant attributes without changing the classification ability to obtain the globally optimal and least attribute set. The PTA algorithm considering the performance is advanced to select the optimal model among the several reduction results. Since modeling, the model optimization and validation require a large amount of data, the paper sets different fault modes by combining the experimental platform in the laboratory and the corresponding simulation model, obtaining numerous fault samples. The diagnostic results of test data show that the model established and optimized by the proposed method has higher diagnostic precision than that of the fuzzy neural network and BP neural network diagnostic methods, which verifies the effectiveness of the proposed method.

An Active Construction Dynamic Schedule Management Model: Using the Fuzzy Earned Value Management and BP Neural Network

Project schedule management is an important aspect of project management. Earned value management (EVM) and its extension methods are widely adopted in project schedule management in various previous studies, however, most of these studies adopted schedule performance indicators based on definiteness condition and historical data, which ignored the impact of uncertainty and dynamics. So the decision risk in schedule management has been increased dramatically. Therefore, this paper proposed an active construction dynamic schedule management model based on fuzzy earned value management (F-EVM) and BP neural network (BP-NN). Firstly, the fuzzy theory was introduced into EVM to evaluate the schedule performance when the project finish schedule cannot be deterministic expression. Then, the main resource planning was functioned as input variables to predict the schedule in advance through BP-NN. Finally, the future action plans was adjusted based on the results to achieve active control of the project schedule. The case results indicated that the introduction of F-EVM effectively reduced influence of subjective estimation on uncertainty in the data measurement process. And the proposed dynamic schedule management model gave a 32.06% better mean absolute percentage error (MAPE) than the existing methods for estimated project duration. Hence, the proposed model provided a more accurate estimation of the project duration and considered the uncertainty and dynamics of the construction project, thereby achieving more effective management of project schedule.

Comparative study on control effect of permanent magnet synchronous motor based on Fuzzy PID control and BP neural network PID control

Aiming at the problems of low control precision, poor anti-interference ability and poor stability of traditional PID controller, the improvement effect of fuzzy control and BP neural network in closed-loop control of PMSM is compared. In the case of variable speed and variable load, the stability, real-time, anti disturbance and robustness of PMSM system with traditional PID controller, fuzzy self-tuning PID controller and BP neural network PID controller as speed regulator are simulated and analyzed. The simulation results show that the fuzzy self-tuning PID controller has more advantages in the response speed of the system, and can make the system quickly recover to the initial state in case of sudden change. The BP neural network PID controller can ensure the relative stability of the system is better, and can restrain the sudden fluctuation.

A Intelligent Evaluation Model Framework of Online Public Opinion Crisis in Higher Vocational Colleges

Higher vocational colleges should establish a real-time online public opinion monitoring mechanism. Before deterioration trend occurs, take relevant measures to relieve and guide. The current ability to deal with online public opinion crisis needs to improve. This paper puts forward the principle of the warning index system of the online public in higher vocational colleges, designs a new evaluation index system, builds the intelligent evaluation model framework based on AHP, fuzzy evaluation method and BP neural network which can effectively learn the knowledge and experience of evaluation experts. This model framework can make full use of available data in the big data environment and reduce manpower.

Fuzzy BP neural network in radar intelligence quality evaluation

This paper presents a novel approach of radar station intelligence quality evaluation which based on fuzzy Backpropagation neural network (BPNN). Firstly, the index system of the radar station intelligence quality evaluation is established according to the analysis of the process, the characteristics, and the main influencing factors of the radar station intelligence production. And then the factor set, comment set and the membership matrix are structured, the fuzzy BPNN for evaluating the quality of the radar station intelligence is designed referring to the index system. Finally, the experiment shows that the accuracy and stability can be improved effectively by using fuzzy BPNN to evaluate the radar station intelligence quality

Power Load Forecast Based on Fuzzy BP Neural Networks with Dynamical Estimation of Weights

The short-term power load forecast is deeply studied by integrating fuzzy BP neural networks and composite adaptive filtering in this paper. Due to the difficulty on accurate modeling of complex factors in smart grid, the fuzzy technology is introduced to deal with the uncertain factors. Meanwhile, data-driven method, such as neural networks, is used as the basic frame of the power load forecast by combing the fuzzy technology. Thereby, to design a highly effective training scheme becomes the main problem on the weights of the BP-NNs. To realize better weights training, the combined filtering technology is fully adopted by fusing adaptive filtering methods based on observable degree (OD) analysis, Sage–Husa adaptive technology, and variational Bayesian method. Thereby, a novel power load forecast algorithm is proposed based on the fuzzy BP-NNs and the combined adaptive cubature Kalman filter. Experiment based on practical power load data is presented to validate the effectiveness of the proposed short-term power load algorithm.

Study on the Evaluation Model of Undergraduates' Ideological and Political Status Based on BP Neural Network

Given the different defects of evaluation on undergraduates’ ideological and political status in terms of scientificity, objectivity and operability, fuzzy evaluation method and BP neural network were combined in this paper. Firstly, the evaluation index system and membership function were built according to the characteristics of the study object. Different variables were evaluated by means of dimensionless membership. Then undergraduates’ ideological and political status was evaluated by using the sound nonlinear evaluation ability of BP neural network. Empirical analysis shows that evaluating undergraduates’ ideological and political status by using BP neural network has good effect and high precision, thus providing a new way and basis for evaluating undergraduates’ ideological and political status.

Data Prediction and Real-time State Estimation of Distribution Network

With the improvement of real-time measurement technology in distribution network, the data collected by power dispatching department is explosively growing. As a result, the real-time detection and analysis of the operation status of distribution network is facing new challenges. In this paper, a prediction model based on the combination of fuzzy c-means (FCM) algorithm and BP neural network is proposed to describe the range of future distribution network operation data. The purpose of this work is to provide a reference range for real-time operation state analysis. Then, considering the problem of massive measurement data in distribution network, a robust state estimation method based on exponential function weighted least squares (EFWLS) is proposed, which has better accuracy and efficiency than traditional weighted least squares (WLS) method. Finally, the accuracy of the prediction model is verified by comparing the predicted data with the real data, and the advantages of the proposed state estimation method are verified by comparing the results of different methods.

Integration performance statistics of green suppliers based on fuzzy mathematics and BP neural network

With the enhancement of people’s environmental awareness, improving environmental performance has become an important way for manufacturing enterprises to achieve sustainable development. At present, one of the key challenges facing enterprises in terms of environmental sustainability is to extend it to other supply chain members. In this paper, the author analyzes the integration performance statistics of green suppliers based on fuzzy mathematics and BP neural network. Supply chain integration represents the company’s ability to formulate strategic alliances, integrate resources, establish seamless processes and share information. The empirical results show that it is feasible to evaluate the performance of green logistics enterprise integration. Through reasonable calculation method and model, the effective and reasonable evaluation results can be obtained. Enterprise managers can reasonably evaluate the key links of enterprise management, allocate enterprise resources correctly, completely and reasonably, minimize costs and maximize profits. Therefore, for the green logistics enterprises, should pay attention to the enterprise’s own performance evaluation, timely adjust their own development direction, plan and goal.

Intelligent algorithm in a smart wearable device for predicting and alerting in the danger of vehicle collision

We have designed a smart wearable device to protect actively pedestrian from impact of vehicle. This device consists of several modules, including radar sensor, transmission module, alarm module and intelligent security program module. In the dominant program module, the safety intelligent algorithm based on fuzzy comprehensive evaluation and BP neural network is proposed. From the perspective of a pedestrian, the moving data sensed by radar, combining with multiple effects of local surroundings, people and vehicles, road and transportation situation, weather, physiological and psychological situation of the pedestrian, are used as the data source for the algorithm. Based on the weight of the index determined by BP neural network, we use the fuzzy comprehensive evaluation to calculate the vehicle risk index. The smart wearable device can effectively predict and warn the situation of vehicles impacting pedestrians. The simulation has confirmed the accuracy of the prewarning algorithm under various conditions.

Human-computer interactive teaching model based on fuzzy set and BP neural network

Human-computer interactive teaching model based on fuzzy set and BP neural network

Risk Evaluation Model of Highway Tunnel Portal Construction Based on BP Fuzzy Neural Network.

Risk assessment for tunnel portals in the construction stage has been widely recognized as one of the most critical phases in tunnel construction as it easily causes accident than the overall length of a tunnel. However, the risk in tunnel portal construction is complicated and uncertain which has made such a neural network very attractive to the construction projects. This paper presents a risk evaluation model, which is obtained from historical data of 50 tunnels, by combining the fuzzy method and BP neural network. The proposed model is used for the risk assessment of the Tiefodian tunnel. The results show that the risk evaluation level is IV, slope instability is the greatest impact index among four risk events, and the major risk factors are confirmed. According to the evaluation results, corresponding risk control measures are suggested and implemented. Finally, numerical simulation is carried out before and after the implementation of risk measures, respectively. The rationality of the proposed risk evaluation model is proved by comparing the numerical simulation results.