降雨-径流过程的ANN建模

论证了应用人工神经网络进行降雨-径流建模的可行性;给出了建模用的BP算法;和应用人工神经网络进行建模的具体方法,并采用实例进行仿真,表明该模型对大、中、小各种洪水过程都能进行很好的模拟,对预报流量图上的一些最重要的特征可作出较准确的预报．     

抗HIC及SSCC管线钢中氢含量的分析与控制

为提高管线钢在高硫化氢( H2S)环境下的抗HIC及SSCC的能力,避免腐蚀性气体造成的裂纹,对钢中w(H)进行了分析.通过采取减少电炉出钢及精炼过程渣料的加入量;合理控制电炉电极喷淋水的开启;优化VD过程Ar气流量控制等措施,使冶炼抗HIC及SSCC管线钢的精炼终点w(H)达到1.5×10-6以下.     

《工业加热》2005年总目录

中国能源、电力与环境可持续发展的若干问题……………………………………………………………………………邱坤赞,朱燕群,岑可法(1-1)昆明市医疗废物的集中焚烧处理………………………………………………………………………………………胡建杭,王华,马媛媛,等(1-5)固定床煤气化过程机理模型综述………………………………………………………………………………………田红,蔡九菊,王爱华,等(2-1)基于人工神经网络的钢铁冶炼终点预报模型…………………………………………………………………………张慧书,战东平,姜周华,等(2-5)热能工程金属基…     

应用小波-人工神经网络组合模型研究电力负荷预报

针对负荷时间序列的非线性和多时间尺度特性．提出了将小波分析与人工神经网络相结合进行负荷预报的方法——小波-人工神经网络组合模型。该模型吸取了小波分析的多分辨功能和人工神经网络的非线性逼近能力。以月、日平均负荷预报为例对模型进行验证．结果表明：该模型的拟合、检验精度较高。     

基于GA-AHP法的径流组合预报

为进一步提高径流预报精度、增加预报结果的可靠性,提出了一种将遗传算法(GA)与层次分析法(AHP)相结合的优化组合预报权值方法(GA-AHP法),就是将小波分析法(WA)分别与自回归(AR)模型、人工神经网络(ANN)模型、支持向量机(SVM)模型进行耦合,选取MARE、SPR、REL、CPX四个评价指标量化单一模型的预报精度、泛化能力、结果可靠性、模型复杂度,依据GA-AHP法率定各模型的权值并进行组合预报。实例应用结果表明,该组合方法的预报精度更高,预报结果更可靠,对非一致性径流序列具有更强的适应性。     

福建省电网中长期水文预报系统设计与开发

利用Java技术平台,采用双机群集数据库系统(Orade 10g)成功设计并开发了基于Web的福建省电网中长期水文预报系统.将长期预报和中期预报分离,前者采用多种数理统计模型,以月或旬为预报时段提供未来1 a的径流预报及相关的频率预报;后者采用考虑城市气象预报的人工神经网络模型,以日为预报时段提供未来7 d的自动、滚动式径流预报.重点阐述了系统架构设计、业务逻辑设计、功能模块设计和程序开发过程中的关键技术问题.     

基于遗传程序设计的中长期径流预报模型研究与应用

应用遗传程序设计建立径流中长期预报模型,结合径流序列数据的特点通过自相关分析确定其滞时输入变量的个数,采用均方误差作为其适应度评价函数,以漫湾实测月径流序列(1953~2003年)和洪家渡实测月径流序列(1951~2004年)为例,通过与ARMA模型、人工神经网络模型的预报结果比较,显示该模型应用于径流中长期预报简单易行且精度较高。     

二滩水电站中长期径流预报研究

针对二滩水电站的实际径流特性和水电站发电调度的要求,应用季节性自回归模型和人工神经网络模型对二滩水电站的月径流、汛期分段和年径流预报进行研究.结果表明,这两种模型对二滩水电站的月径流预报、汛期定性预报均达到了一定精度,可为二滩水电站优化调度的径流输入提供参考依据,尤其是AR(P)模型的非汛期月径流预测和BP模型年径流预测结果可在实际运行中使用.     

LF炉精炼过程钢液温度预报及控制

LF炉精炼过程钢液温度预报及控制是炼钢工艺过程优化的主要内容,它不但影响能否为连铸提供温度合格的钢液,能否实现节奏调整及多炉连浇,而且对整个炼钢过程的节能降耗以及现场的操作影响很大。通过LF炉精炼过程钢液温度预报及控制研究,建立LF炉升温期钢液目标温度数学模型,给出LF炉热效率公式及确定损失能量的方法,将供电制度与钢液温度控制结合在一起,给出LF炉精炼过程供电原则。其研究结果有利于进一步强化LF炉精炼过程,增加LF炉对电炉-连铸流程节奏的调节能力,实现多炉连浇及高效节能。     

白山水库径流中长期预报研究与应用

基于时间序列和物理成因分析，将成因分析，统计方法与人工神经网络相结合，挑选出影响白山水库汛期入库流量的前期大气流影响因子，建立了逐步周期分析模型和逐步多元回归与人工神经网络的耦合模型。预报实践表明，所建模型合理，预报效果好，精度高，具有较高的推广和应用价值。     

Modelling and prediction of bioethanol production from intermediates and byproduct of sugar beet processing using neural networks

The aim of this work was to model and predict the process of bioethanol production from intermediates and byproduct of sugar beet processing by applying artificial neural networks. Prediction of one substrate fermentation by neural networks had the same input variables (fermentation time and starting sugar content) and one output value (ethanol content, yeast cell number or sugar content). Results showed that a good prediction model could be obtained by networks with single hidden layer. The neural network configuration that gave the best prediction for raw or thin juice fermentation was one with 8 neurons in hidden layer for all observed outputs. On the other side, the optimal number of neurons in hidden layer was found to be 9 and 10 for thick juice and molasses, respectively. Further, all substrates data were merged, which led to introducing an additional input (substrate type) and defining all outputs optimal network architecture to 3-12-1. From the results the conclusion was that artificial neural networks are a good prediction tool for the selected network outputs. Also, these predictive capabilities allowed the application of the Garson's equation for estimating the contribution of selected process parameters on the defined outputs with satisfactory accuracy.     

Hydrogen purification layered bed optimization based on artificial neural network prediction of breakthrough curves

Artificial neural network has generally been used for a quantity of tasks such as classification, prediction, clustering and association analysis in different application fields. To the best of our knowledge, there are few researches on breakthrough curve used artificial neural network. In this paper, an artificial neural network model is established for breakthrough curves prediction in relation to a ternary components gas with a two-layered adsorbent bed piled up with activated carbon (AC) and zeolite, and an optimization is concluded by the artificial neural network. The performance data which acquired by Aspen model has been utilized for training artificial neural network (ANN) model. The ANN model trained has great competence for making prediction of hydrogen purification performance of PSA cycle with impressive speed and rational accuracy. On the strength of the ANN model, we implemented an optimization for seeking first-rank PSA cycle parameters. The optimization is concentrated on the effect of inlet flow rate, pressure and layer ratio of activated carbon height to zeolite height. Furthermore, this paper shows that the PSA cycle's optimal operation parameters can be obtained by use of ANN model and optimization algorithm, the ANN model has been trained according to the data generated by Aspen adsorption model.     

Neural network model for a commercial PEM fuel cell system

Performance prediction of a commercial proton exchange membrane (PEM) fuel cell system by using artificial neural networks (ANNs) is investigated. Two artificial neural networks including the back-propagation (BP) and radial basis function (RBF) networks are constructed, tested and compared. Experimental data as well as preprocess data are utilized to determine the accuracy and speed of several prediction algorithms. The performance of the BP network is investigated by varying error goals, number of neurons, number of layers and training algorithms. The prediction performance of RBF network is also presented. The simulation results have shown that both the BP and RBF networks can successfully predict the stack voltage and current of a commercial PEM fuel cell system. Speed and accuracy of the prediction algorithms are quite satisfactory for the real-time control of this particular application.     

基于长短期记忆神经网络的地表太阳辐照度预测

针对地表太阳辐照度(GHI)短期预测问题,提出一种基于长短期记忆神经网络的短期太阳辐照度预测模型。采用递归结构的训练样本,以保证训练样本内部的时间耦合性。为验证所提模型预测GHI的有效性,采用算例与传统人工神经网络模型预测结果进行对比分析。结果表明:基于长短期记忆神经网络预测模型将均方误差降低88.48%,表明所建模型更适用于GHI预测。     

BP人工神经网络在钢铁生产过程信息流分析中的应用

钢铁企业生产过程的信息流蕴藏着丰富的生产工艺规律。BP人工神经网络广泛用于信息流分析中,将其概括为四个方面:过程状态参数预测、产品性能参数预测、能耗信息预测和原材料参数优化。分别介绍相关研究和应用工作,指出应用流程中存在的不足,并给出规范流程。最后给出某大型钢铁企业新区焦炉单元的日能耗预测实例,验证了BP人工神经网络在钢铁生产过程信息流分析中的作用和应用流程的有效性。     

基于BP神经网络的球磨机磨矿浓度的预测分析

对球磨机磨矿过程的能耗进行分析,并利用灰色系统关联度分析法分析影响能耗因数,找出主要影响因数,通过BP神经网络建立球磨机磨矿浓度预测模型对磨矿浓度进行预测,对于降低球磨机能耗、解决冶金企业能耗问题具有一定的指导意义。     

Neural network approach for food temperature prediction during solar drying

In the present study, the application of artificial neural network (ANN) for prediction of temperature variation of food product during solar drying is investigated. The important climatic variables namely, solar radiation intensity and ambient air temperature are considered as the input parameters for ANN modeling. Experimental data on potato cylinders and slices obtained with mixed mode solar dryer for 9 typical days of different months of the year were used for training and testing the neural network. A methodology is proposed for development of optimal neural network. Results of analysis reveal that the network with 4 neurons and logsig transfer function and trainrp back propagation algorithm is the most appropriate approach for both potato cylinders and slices based on minimum measures of error. In order to test the worthiness of ANN model for prediction of food temperature variation, the analytical heat diffusion model with appropriate boundary conditions and statistical model are also proposed. Based on error analysis results, the prediction capability of ANN model is found to be the best of all the prediction models investigated, irrespective of food sample geometry.     

ABC BP模型在混凝土双曲拱坝变形监控中的应用

因大坝变形具有很强的非线性、随机性,致使预测困难,将人工蜂群算法(ABC)与BP神经网络相结合,利用人工蜂群算法具有强全局优化能力、强鲁棒性等优点,克服BP神经网络收敛速度慢、易陷入局部极小点等缺点,建立ABC BP、BP神经网络大坝变形预测模型对小湾大坝变形监测数据进行预测。结果表明,与单纯的BP神经网络预测模型相比,ABC BP算法提高了大坝变形预报的精度,加快了网络的收敛速度,能更高效准确地进行大坝变形监控预报。     

A new strategy for wind speed forecasting using artificial intelligent methods

A new strategy in wind speed prediction based on fuzzy logic and artificial neural networks was proposed. The new strategy for fuzzy logic not only provides significantly less rule base but also has increased estimated wind speed accuracy when compared to traditional one. Meanwhile, applying the proposed approach to artificial neural network leads to less neuron numbers and less learning time process along with accurate wind speed prediction results. The experimental results demonstrate that the proposed method not only provides less computational time but also a better wind speed prediction performance.