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hexavalent chromium using a porous titanium flow-through electrode and intelligent prediction based on a backpropagation neural network

《环境科学与工程前沿（英文）》 2023年第17卷第8期 doi: 10.1007/s11783-023-1697-x

摘要：

● Titanium-based flow-through electrode achieved high Cr(VI) reduction efficiency.

关键词： Flow-through electrode Hexavalent chromium Heavy metals Neural network Artificial intelligence

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A pre-compensation method of the systematic contouring error for repetitive command paths

D. L. ZHANG,Y. H. CHEN,Y. P. CHEN

《机械工程前沿（英文）》 2015年第10卷第4期页码 367-372 doi: 10.1007/s11465-015-0367-5

摘要：

For a repetitive command path, pre-compensating the contouring error by modifying the command path is practical. To obtain the pre-compensation value with better accuracy, this paper proposes the use of a back propagation neural network to extract the function of systematic contouring errors. Furthermore, by using the extracted function, the contouring error can be easily pre-compensated. The experiment results verify that the proposed compensation method can effectively reduce contouring errors.

关键词： contouring error pre-compensation motion control system back propagation (BP) neural network

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A modified neural learning algorithm for online rotor resistance estimation in vector controlled induction

A. CHITRA,S. HIMAVATHI

《能源前沿（英文）》 2015年第9卷第1期页码 22-30 doi: 10.1007/s11708-014-0339-1

摘要： Online estimation of rotor resistance is essential for high performance vector controlled drives. In this paper, a novel modified neural algorithm has been identified for the online estimation of rotor resistance. Neural based estimators are now receiving active consideration as they have a number of advantages over conventional techniques. The training algorithm of the neural network determines its learning speed, stability, weight convergence, accuracy of estimation, speed of tracking and ease of implementation. In this paper, the neural estimator has been studied with conventional and proposed learning algorithms. The sensitivity of the rotor resistance change has been tested for a wide range of variation from -50% to+50% on the stability of the drive system with and without estimator. It is quiet appealing to settle with optimal estimation time and error for the viable realization. The study is conducted extensively for estimation and tracking. The proposed learning algorithm is found to exhibit good estimation and tracking capabilities. Besides, it reduces computational complexity and, hence, more feasible for practical digital implementation.

关键词： neural networks back propagation (BP) rotor resistance estimators vector control induction motor

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Day-ahead electricity price forecasting using back propagation neural networks and weighted least square

S. Surender REDDY,Chan-Mook JUNG,Ko Jun SEOG

《能源前沿（英文）》 2016年第10卷第1期页码 105-113 doi: 10.1007/s11708-016-0393-y

摘要： This paper proposes the day-ahead electricity price forecasting using the artificial neural networks (ANN) and weighted least square (WLS) technique in the restructured electricity markets. Price forecasting is very important for online trading, e-commerce and power system operation. Forecasting the hourly locational marginal prices (LMP) in the electricity markets is a very important basis for the decision making in order to maximize the profits/benefits. The novel approach proposed in this paper for forecasting the electricity prices uses WLS technique and compares the results with the results obtained by using ANNs. To perform this price forecasting, the market knowledge is utilized to optimize the selection of input data for the electricity price forecasting tool. In this paper, price forecasting for Pennsylvania-New Jersey-Maryland (PJM) interconnection is demonstrated using the ANNs and the proposed WLS technique. The data used for this price forecasting is obtained from the PJM website. The forecasting results obtained by both methods are compared, which shows the effectiveness of the proposed forecasting approach. From the simulation results, it can be observed that the accuracy of prediction has increased in both seasons using the proposed WLS technique. Another important advantage of the proposed WLS technique is that it is not an iterative method.

关键词： day-ahead electricity markets price forecasting load forecasting artificial neural networks load serving entities

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Optimization of turbine cold-end system based on BP neural network and genetic algorithm

Chang CHEN,Danmei XIE,Yangheng XIONG,Hengliang ZHANG

《能源前沿（英文）》 2014年第8卷第4期页码 459-463 doi: 10.1007/s11708-014-0335-5

摘要： The operation condition of the cold-end system of a steam turbine has a direct impact on the economy and security of the unit as it is an indispensible auxiliary system of the thermal power unit. Many factors influence the cold-end operation of a steam turbine; therefore, the operation mode needs to be optimized. The optimization analysis of a 1000 MW ultra-supercritical (USC) unit, the turbine cold-end system, was performed utilizing the back propagation (BP) neural network method with genetic algorithm (GA) optimization analysis. The optimized condenser pressure under different conditions was obtained, and it turned out that the optimized parameters were of significance to the performance and economic operation of the system.

关键词： optimization turbine cold-end system BP neural network genetic algorithm

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Using hybrid models to predict blood pressure reactivity to unsupported back based on anthropometric

Gurmanik KAUR,Ajat Shatru ARORA,Vijender Kumar JAIN

《信息与电子工程前沿（英文）》 2015年第16卷第6期页码 474-485 doi: 10.1631/FITEE.1400295

摘要： Accurate blood pressure (BP) measurement is essential in epidemiological studies, screening programmes, and research studies as well as in clinical practice for the early detection and prevention of high BP-related risks such as coronary heart disease, stroke, and kidney failure. Posture of the participant plays a vital role in accurate measurement of BP. Guidelines on measurement of BP contain recommendations on the position of the back of the participants by advising that they should sit with supported back to avoid spuriously high readings. In this work, principal component analysis (PCA) is fused with forward stepwise regression (SWR), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and the least squares support vector machine (LS-SVM) model for the prediction of BP reactivity to an unsupported back in normotensive and hypertensive participants. PCA is used to remove multi-collinearity among anthropometric predictor variables and to select a subset of components, termed ‘principal components’ (PCs), from the original dataset. The selected PCs are fed into the proposed models for modeling and testing. The evaluation of the performance of the constructed models, using appropriate statistical indices, shows clearly that a PCA-based LS-SVM (PCA-LS-SVM) model is a promising approach for the prediction of BP reactivity in comparison to others. This assessment demonstrates the importance and advantages posed by hybrid models for the prediction of variables in biomedical research studies.

关键词： Blood pressure (BP) Principal component analysis (PCA) Forward stepwise regression Artificial neural network (ANN) Adaptive neuro-fuzzy inference system (ANFIS) Least squares support vector machine (LS-SVM)

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基于正交实验的BP神经网络预测研究

蔡安辉,刘永刚,孙国雄

《中国工程科学》 2003年第5卷第7期页码 67-71

摘要：

用不同的L₉（3⁴）正交实验方案结果作为训练学习样本集，对BP神经网络预测应用过程的策略进行了探讨，结果表明：完备的正交实验样本集是基本训练学习单元，在完备的正交实验样本集上添加或减少样本数量同一实验的条件下，完备的信息量大的正交实验样本集，能以很高的精度预测完备的信息量小的正交实验样本集；提出了一条新的实验设计思路——通过实验得出一个完备的正交实验样本集，通过计算机用BP

关键词： BP神经网络正交实验策略实验设计思路样本集

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基于神经网络的建筑工程造价预测研究

聂规划,刘平峰,何柳

《中国工程科学》 2005年第7卷第10期页码 56-59

摘要：

采用误差反向传播人工神经网络模型（BP网络模型），以建筑特征参数为输入变量，通过实际资料对网络进行训练和模拟，并用贡献分析法筛选输入变量，对网络结构进行优化，结果显示了该模型在建筑工程造价预测中的有效性

关键词： BP神经网络建筑造价预测

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基于BP-AGA的非线性组合预测方法研究

王硕,张有富,金菊良

《中国工程科学》 2005年第7卷第4期页码 83-87

摘要：

运用神经网络和加速遗传算法建立非线性组合预测模型，在BP算法训练网络出现收敛速度缓慢时启用加速遗传算法（AGA）来优化网络参数，把AGA的优化结果作为BP算法的初始值，再用BP算法训练网络，如此交替运行BP算法和AGA以加快网络的收敛速度，同时改善局部最小问题。

关键词：神经网络加速遗传算法非线性组合预测预测精度

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一种BP神经网络的改进方法及其应用

李宏刚,吕辉,李刚

《中国工程科学》 2005年第7卷第5期页码 63-65

摘要：

针对BP神经网络中学习因子取值小、收敛性好但训练时间长，学习因子取值大、权值变化剧烈但可能导致振荡的情况，提出了一种修正学习因子的方法，即给学习因子前加一比例因子，在网络权值调整过程中自动调整学习因子的大小

关键词：神经网络改进算法仿真

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Assessing artificial neural network performance for predicting interlayer conditions and layer modulus

Lingyun YOU, Kezhen YAN, Nengyuan LIU

《结构与土木工程前沿（英文）》 2020年第14卷第2期页码 487-500 doi: 10.1007/s11709-020-0609-4

摘要： The objective of this study is to evaluate the performance of the artificial neural network (ANN) approach for predicting interlayer conditions and layer modulus of a multi-layered flexible pavement structure. To achieve this goal, two ANN based back-calculation models were proposed to predict the interlayer conditions and layer modulus of the pavement structure. The corresponding database built with ANSYS based finite element method computations for four types of a structure subjected to falling weight deflectometer load. In addition, two proposed ANN models were verified by comparing the results of ANN models with the results of PADAL and double multiple regression models. The measured pavement deflection basin data was used for the verifications. The comparing results concluded that there are no significant differences between the results estimated by ANN and double multiple regression models. PADAL modeling results were not accurate due to the inability to reflect the real pavement structure because pavement structure was not completely continuous. The prediction and verification results concluded that the proposed back-calculation model developed with ANN could be used to accurately predict layer modulus and interlayer conditions. In addition, the back-calculation model avoided the back-calculation errors by considering the interlayer condition, which was barely considered by former models reported in the published studies.

关键词： asphalt pavement interlayer conditions finite element method artificial neural network back-calculation