With the rapid development of urban economy, the development of urban rail transit is becoming more and more rapid. As an energy-saving, land-saving, and environment-friendly green travel mode, the subway provides realistic and feasible solutions to the increasingly prominent traffic environment and other urban diseases in our country and brings a booming development in the subway construction industry with efforts to promote and build in many large cities. For a large number of subway constructions, it is particularly important to judge the construction safety status in time during the entire safety management process. Regularly conducting safety risk assessments on subway construction status can accurately predict and judge the types of accidents that occur. In order to solve the current safety risk assessment problems in the process of subway construction in our country, this paper is based on the BP neural network to intelligently identify the safety risks of subway construction, choosing from three aspects: human factors, management factors, and risk factors. We evaluate the construction safety of subway projects under construction through the model, predict the types of accidents that may occur, so that the construction unit can take corresponding preventive and improvement measures, improve the relevant safety technology of subway construction in a targeted manner, and propose corresponding reductions. We provide suggestions and measures for risk probability, to ensure that the construction unit discovers the danger in time and takes safety measures. The rectification measures provided theoretical basis and guidance.

/pdf/intelligent-recognition-of-safety-risk-in-metro-engineering-1f6bzy2wds.pdf

Intelligent Recognition of Safety Risk in Metro Engineering Construction Based on BP Neural Network

Underwater unmanned vehicle (UUV) cooperative combat is an important pattern of underwater warfare.. Aiming at the problem of effectiveness evaluation, this paper presents an effectiveness evaluation model based on GAPSO-BP neural network, which can acquire the information of the expert by training, and calculate the effectiveness value fast and continuously. Firstly, according to the indicator system of UUV Cooperative Combat Effectiveness Evaluation, the evaluation system combined with AHP method improved by intuitionistic fuzzy and BP neural network is given. Secondly, by analyzing GA and PSO method, and combing their advantages, the GAPSO-BP neural network is constructed. Finally, simulation experiments are performed using simulation examples and the simulation results show that the model proposed can evaluate the UUV cooperative combat effectiveness accurately and effectively, and the iterative convergence of GAPSO optimization algorithm is superior to GA, PSO algorithm, and its Evaluation accuracy is the best.

Effectiveness Evaluation of UUV Cooperative Combat Based on GAPSO-BP Neural Network

In order to adapt to the increasingly rapid air combat rhythm and provide key basis for optimal operational decision, how to evaluate the operational effectiveness of unmanned aerial vehicle (UAV) quickly and accurately is a basic and important research topic for UAV combat system. Considering the problem of effectiveness evaluation, this paper presents an effectiveness evaluation model for UAV air-to-ground attack which is based on PSO-BP neural network. Firstly, the main influencing factors of effectiveness evaluation of air-to-ground attack are analyzed and summarized. Secondly, the basic principle of PSO algorithm and BP neural network are introduced respectively and then PSO-BP neural network is constructed. Finally, simulation experiments are performed using historical statistics and the simulation results show that the proposed method can reduce the training error and improve the evaluation accuracy, which implies that the proposed algorithm is a better method to solve the problem of effectiveness evaluation of UAV air-to-ground attack.

Effectiveness evaluation for UAV air-to-ground attack based on PSO-BP neural network

Particle Swarm Optimization of BP-ANN Based Soft Sensor for Greenhouse Climate

In this article, the authors develop the Particle Swarm Optimization algorithm (PSO) in order to optimise the BP network in order to elaborate an accurate dynamic model that can describe the behavior of the temperature and the relative humidity under an experimental greenhouse system. The PSO algorithm is applied to the Back-Propagation Neural Network (BP-NN) in the training phase to search optimal weights baded on neural networks. This approach consists of minimising the reel function which is the mean squared difference between the real measured values of the outputs of the model and the values estimated by the elaborated neural network model. In order to select the model which possess higher generalization ability, various models of different complexity are examined by the test-error procedure. The best performance is produced by the usage of one hidden layer with fourteen nodes. A comparison of measured and simulated data regarding the generalization ability of the trained BP-NN model for both temperature and relative humidity under greenhouse have been performed and showed that the elaborated model was able to identify the inside greenhouse temperature and humidity with a good accurately. 

Accurately determine the air material demand has been an important research topic in the general aviation enterprise, the selection of forecasting methods is very important to scientifically determine air material demand. Consider general aircraft material demand forecast problem, forecast method is proposed using particle swarm optimization (PSO) algorithm to optimize the BP neural network. Firstly analyzed the main influence factors of general aircraft material demand, and then introduces the basic principle and algorithm process of PSO algorithm and BP neural network and PSO-BP neural network forecast model is constructed, finally simulation experiments are carried out by using historical statistics of general aviation enterprise, the prediction accuracy is higher than BP neural network and good results have been achieved. The method also can be used to forecast other kinds of material resources.

http://www.sersc.org/journals/IJCA/vol9_no5/39.pdf

General Aircraft Material Demand Forecast Based on PSO-BP Neural Network

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General Aircraft Material Demand Forecast Based on PSO-BP Neural Network