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This article presents a hybrid model involving artificial neural networks and biogeography-based optimization for long-term forecasting of India's sector-wise electrical energy demand. It involves socio-economic indicators, such as population and per capita gross domestic product, and uses two artificial neural networks, which are trained through a biogeography-based optimization algorithm with a goal of perfect mapping of the input–output data in the non-linear space through obtaining the global best weight parameters. The biogeography-based optimization based training of the artificial neural network improves the forecasting accuracy and avoids trapping in local optima besides enhancing the convergence to the lowest mean squared error at the minimum number of iterations than existing approaches. The model requires an input and the year of the forecast and predicts the sector-wise energy demand. Forecasts up to the year 2025 are compared with those of the regression model, the artificial neural network model trained by back-propagation, and the artificial neural network model trained by harmony search algorithm to exhibit its effectiveness. 相似文献
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随着3D技术的不断发展,立体图像的使用领域越来越广泛,同时人们对图像的清晰度要求越来越高,因此,立体图像的质量评价成为关注点,基于此,提出了一种基于双树复小波变换的立体图像质量评价算法。使用双树复小波变换对立体图像的左、右视图进行处理,生成纹理结构图像,且根据最小能量误差的原理,获取左右视图的视差图;对纹理结构图像和视差图提取非对称广义高斯分布模型的参数、梯度幅值、相对梯度方向方差和奇异值曲线与坐标轴的面积等特征;使用AdaBoosting BP神经网络,进行训练和预测立体图像的质量得分。在LIVE立体图像数据库上的实验结果表明,新方法预测得分与主观得分有较好的一致性,获得了比较好的实验结果。 相似文献
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由于模锻过程具有强时变性和非线性,因此精确控制模锻压机载荷预测至关重要。以7075铝合金模锻过程为例,提出了一种基于神经网络的模锻压机载荷在线建模方法。基于商业软件Deform-3D模拟了恒温恒速度工况下的载荷变化规律,根据获取的数据建立了初始神经网络模型。在实际模锻实验过程中,通过反向传播算法不断修正初始神经网络权值矩阵,以实现模型的在线更新。在50 t模锻实验台上进行实验,以验证所提方法的有效性。实验结果表明:所提出的在线建模方法可以准确预测复杂模锻工况下载荷的变化,与传统离线神经网络建模方法相比,其预测值更加准确,更能满足实际工程需求。 相似文献
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根据河南油田目前存在的情况,开发了一种井下油水分离系统的双级水力旋流样机.利用人工神经网络(Artificial NeuralNetworks,ANN)建立起井下双级水力旋流器的数学模型,通过实验室内柴油以及河南油田实际油样测试,根据前后的分析比较来评估该人工神经网络模型的可靠性和有效性.经过最终实验分析,在并下可以实现两级串联油水分离,当流量控制在6m3/h以上,同时入口油滴粒径大于80μm时,分离器的分离效率最好,可以达到99.5%以上.室内实验分离后水中含油浓度小于50PPm,现场试验小于200PPm,远低于国外室内试验的400PPm指标,测量数据可靠,为其应用于实际生产提供了理论分析基础. 相似文献
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Jalil Asadisaghandi Pejman Tahmasebi 《Journal of Petroleum Science and Engineering》2011,78(2):464-475
This paper presents a new approach to improve the performance of neural network method to PVT oil properties prediction. The true value of PVT properties which is determined based on the accurate data is a challenge of the petroleum industry. The main goal of the following investigation would be the performance comparison of various back-propagation learning algorithms in neural network that could be applied for PVT prediction. Up to now, no procedure has been presented to determine the network structure for some complicated cases, therefore; design and production of neural network would be almost dependent on the user's experience. To prevent this problem, neural network based recommended procedure in this study was applied to present the advantages. To show the performance of this procedure, several learning algorithms were investigated for comparison. One of the most common problems in neural network design is the topology and the parameter value accuracy that if those elements selection was correctly and optimally, the designer would achieve better results. Since, fluids of different regions have varying hydrocarbon properties, therefore, the empirical correlations in different hydrocarbon systems should be investigated to find their accuracies and limitations. In this study, an investigation of different empirical correlations along with the artificial neural networks in Iran oilfields has been presented. Then, the new model of artificial neural network for prediction of PVT oil properties in Iran crude oil presented. To test this new method, it was evaluated by collecting dataset from 23 different oilfields in Iran (south, central, western and continental shelf). In this study, two networks for prediction of bubble point pressure values (Pb) and the oil formation volume factor at bubble point (Bob) were designed. The parameters and topology of the optimum neural networks were determined and in order to consider the effect of these networks designing on results, their performances were compared with various empirical correlations. According to comparison between the obtained results, it shows that the improved method presented has better performance rather than empirical and current methods in neural network designing in petroleum applications for these predictions. 相似文献
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吴学霖 《华北水利水电学院学报》2012,(5):113-118
以熔融温度、射出时间、保压压力、保压时间、冷却时间等5个制程参数作为控制因子,利用Moldflow仿真软件对导光板模型进行模流分析,应用田口法搭配倒传递类神经网路Super PCNeuom 5.0程序,建立导光板总翘曲值、体积顶出收缩值、缩痕指数质量预测模型,再应用MATLAB基因演算程序来搜寻在控制参数水平范围内局部最佳解参数组合,使塑料产品的质量提升. 相似文献
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Tongxun Wang Xinyu Ying Qian Zhang Yanrui Xu Chunhui Jiang Jianwei Shang Zepeng Zang Fangxin Wan Xiaopeng Huang 《Journal of food science》2024,89(2):966-981
By using ultrasonic synergy vacuum far-infrared drying (US-VFID), the effects of different conditions on the drying kinetics, functional properties, and microstructure of Codonopsis pilosula slices were studied. The sparrow search algorithm (SSA) was used to optimize the back-propagation (BP) neural network to predict the moisture ratio during drying. With the increase of ultrasonic frequency, power and radiation temperature, the drying time of C. pilosula was shortened. The drying time of US-VFID was 25% shorter than VFID, when radiation temperature was 50°C, ultrasonic power was 48 W, and frequency was 28 kHz. The SSA-BP neural network, the average absolute error prediction was 0.0067. Compared with hot air drying (HAD), the total phenolic content and antioxidant activity of C. pilosula by US-VFID were increased by 29.47% and 8.67%, respectively, and a reduction in color contrast of 16.19%. The dilation and generation of microcapillary of C. pilosula were more obvious. The study revealed US-VFID could be used for the selection and process control of agro-processing methods for C. pilosula products. 相似文献