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  • Open Access


    Analysis and Prediction of Regional Electricity Consumption Based on BP Neural Network

    Pingping Xia1, *, Aihua Xu2, Tong Lian1

    Journal of Quantum Computing, Vol.2, No.1, pp. 25-32, 2020, DOI:10.32604/jqc.2019.09232

    Abstract Electricity consumption forecasting is one of the most important tasks for power system workers, and plays an important role in regional power systems. Due to the difference in the trend of power load and the past in the new normal, the influencing factors are more diversified, which makes it more difficult to predict the current electricity consumption. In this paper, the grey system theory and BP neural network are combined to predict the annual electricity consumption in Jiangsu. According to the historical data of annual electricity consumption and the six factors affecting electricity consumption, the More >

  • Open Access


    Ground Nephogram Recognition Algorithm Based on Selective Neural Network Ensemble

    Tao Li1, Xiang Li1, *, Yongjun Ren2, Jinyue Xia3

    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 621-631, 2020, DOI:10.32604/cmc.2020.06463

    Abstract In view of the low accuracy of traditional ground nephogram recognition model, the authors put forward a k-means algorithm-acquired neural network ensemble method, which takes BP neural network ensemble model as the basis, uses k-means algorithm to choose the individual neural networks with partial diversities for integration, and builds the cloud form classification model. Through simulation experiments on ground nephogram samples, the results show that the algorithm proposed in the article can effectively improve the Classification accuracy of ground nephogram recognition in comparison with applying single BP neural network and traditional BP AdaBoost ensemble algorithm on More >

  • Open Access


    Data-Driven Structural Design Optimization for Petal-Shaped Auxetics Using Isogeometric Analysis

    Yingjun Wang1, Zhongyuan Liao1, Shengyu Shi1, *, Zhenpei Wang2, *, Leong Hien Poh3

    CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.2, pp. 433-458, 2020, DOI:10.32604/cmes.2020.08680

    Abstract Focusing on the structural optimization of auxetic materials using data-driven methods, a back-propagation neural network (BPNN) based design framework is developed for petal-shaped auxetics using isogeometric analysis. Adopting a NURBS-based parametric modelling scheme with a small number of design variables, the highly nonlinear relation between the input geometry variables and the effective material properties is obtained using BPNN-based fitting method, and demonstrated in this work to give high accuracy and efficiency. Such BPNN-based fitting functions also enable an easy analytical sensitivity analysis, in contrast to the generally complex procedures of typical shape and size sensitivity More >

  • Open Access


    Assessing the Forecasting of Comprehensive Loss Incurred by Typhoons: A Combined PCA and BP Neural Network Model

    Shuai Yuan1, Guizhi Wang1,*, Jibo Chen1, Wei Guo2

    Journal on Artificial Intelligence, Vol.1, No.2, pp. 69-88, 2019, DOI:10.32604/jai.2019.06535

    Abstract This paper develops a joint model utilizing the principal component analysis (PCA) and the back propagation (BP) neural network model optimized by the Levenberg Marquardt (LM) algorithm, and as an application of the joint model to investigate the damages caused by typhoons for a coastal province, Fujian Province, China in 2005-2015 (latest). First, the PCA is applied to analyze comprehensively the relationship between hazard factors, hazard bearing factors and disaster factors. Then five integrated indices, overall disaster level, typhoon intensity, damaged condition of houses, medical rescue and self-rescue capability, are extracted through the PCA; Finally,… More >

  • Open Access


    Ship Trajectory Prediction Based on BP Neural Network

    Hai Zhou1,2,*, Yaojie Chen1,2, Sumin Zhang3

    Journal on Artificial Intelligence, Vol.1, No.1, pp. 29-36, 2019, DOI:10.32604/jai.2019.05939

    Abstract In recent years, with the prosperity of world trade, the water transport industry has developed rapidly, the number of ships has surged, and ship safety accidents in busy waters and complex waterways have become more frequent. Predicting the movement of the ship and analyzing the trajectory of the ship are of great significance for improving the safety level of the ship. Aiming at the multi-dimensional characteristics of ship navigation behavior and the accuracy and real-time requirements of ship traffic service system for ship trajectory prediction, a ship navigation trajectory prediction method combining ship automatic identification… More >

  • Open Access


    The Application of BP Neural Networks to Analysis the National Vulnerability

    Guodong Zhao1, Yuewei Zhang1, Yiqi Shi2, Haiyan Lan1,*, Qing Yang3

    CMC-Computers, Materials & Continua, Vol.58, No.2, pp. 421-436, 2019, DOI:10.32604/cmc.2019.03782

    Abstract Climate change is the main factor affecting the country’s vulnerability, meanwhile, it is also a complicated and nonlinear dynamic system. In order to solve this complex problem, this paper first uses the analytic hierarchy process (AHP) and natural breakpoint method (NBM) to implement an AHP-NBM comprehensive evaluation model to assess the national vulnerability. By using ArcGIS, national vulnerability scores are classified and the country’s vulnerability is divided into three levels: fragile, vulnerable, and stable. Then, a BP neural network prediction model which is based on multivariate linear regression is used to predict the critical point… More >

  • Open Access


    GA-BP Air Quality Evaluation Method Based on Fuzzy Theory

    Ma Ning1,*, Jianhe Guan1, Pingzeng Liu2, Ziqing Zhang3, Gregory M. P. O’Hare4

    CMC-Computers, Materials & Continua, Vol.58, No.1, pp. 215-227, 2019, DOI:10.32604/cmc.2019.03763

    Abstract With the rapid development of China’s economy, the scale of the city has been continuously expanding, industrial enterprises have been increasing, the discharge of multiple pollutants has reached the top of the world, and the environmental problems become more and more serious. The air pollution problem is particularly prominent. Air quality has become a daily concern for people. In order to control air pollution, it is necessary to grasp the air quality situation in an all-round way. It is necessary to evaluate air quality. Accurate results of air quality evaluation can help people know more… More >

  • Open Access


    Research on Activated Carbon Supercapacitors Electrochemical Properties Based on Improved PSO-BP Neural Network

    Xiaoyi Liang1, Zhen Yang1,2, Xingsheng Gu3, Licheng Ling1

    CMC-Computers, Materials & Continua, Vol.13, No.2, pp. 135-152, 2009, DOI:10.3970/cmc.2009.013.135

    Abstract Supercapacitors, also called electrical double-layer capacitors (EDLCs), occupy a region between batteries and dielectric capacitors on the Ragone plot describing the relation between energy and power. BET specific surface area and specific capacitance are two important electrochemical property parameters for activated carbon EDLCs, which are usually tested by experimental method. However, it is misspent time to repeat lots of experiments for EDLCs' studies. In this investigation, we developed one theoretical model based on improved particle swarm optimization algorithm back propagation (PSO-BP) neural network (NN) to simulate and optimize BET specific surface area and specific capacitance. More >

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