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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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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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基于BP神经网络的工程图形数据远程安全快速传输法

秦威,秦书玉

《中国工程科学》 2007年第9卷第1期页码 49-52

摘要：根据图形的几何元素相关性的特征，建立参数结构，采用人工神经网络BP算法同时进行数据编码压缩和数据加密，实现复杂工程图形数据的远程高效安全传输。实例表明，此方法可用于实际工程。

关键词：神经网络 BP算法相关性加密快速传输图形数据

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基于GA－BP网络的人工湿地污水净化效果研究

黄娟,王世和,雒维国,钱卫一,鄢璐

《中国工程科学》 2007年第9卷第2期页码 79-83

摘要：基于大量可靠的试验数据，首次采用遗传神经网络方法模拟湿地除污系统，详细论述了网络拓扑结构优化和训练数据预处理等关键问题，建立了可靠的GA－BP模型，并采用该模型仿真湿地系统正交试验，依据正交试验结果对影响因素进行分级

关键词：人工湿地污水净化 GA-BP网络正交试验

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智能预报模式与水文中长期智能预报方法

陈守煜,郭瑜,王大刚

《中国工程科学》 2006年第8卷第7期页码 30-35

摘要：

建立了以模糊优选、BP神经网络及遗传算法有机结合的智能预报模式与方法。在应用该方法进行中长期水文智能预报时，首先选取训练样本的数量，根据预报因子与预报对象的相关关系得到相对隶属度矩阵;再将其作为BP神经网络输入值以训练连接权重；最后将得到的连接权重值用于预报检验。

关键词：模糊优选 BP神经网络遗传算法智能预报模式中长期水文智能预报

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一种改进BP算法在机械手逆运动学中的应用

吴爱国,郝润生

《中国工程科学》 2005年第7卷第7期页码 34-38

摘要：

通过对传统BP算法的分析，提出了一种改进激励函数的学习方法，并且在神经网络的每一层采用不同的学习速率，以提高训练速度；采用所提出的改进BP算法，训练多层前向神经网络，建立机械手逆运动学模型，仿真结果表明了该算法的有效性；与传统BP算法相比，大大提高了机械手逆运动学的精度。

关键词：神经网络 BP算法激励函数机械手逆运动学

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Multiscale computation on feedforward neural network and recurrent neural network

Bin LI, Xiaoying ZHUANG

《结构与土木工程前沿（英文）》 2020年第14卷第6期页码 1285-1298 doi: 10.1007/s11709-020-0691-7

摘要： Homogenization methods can be used to predict the effective macroscopic properties of materials that are heterogenous at micro- or fine-scale. Among existing methods for homogenization, computational homogenization is widely used in multiscale analyses of structures and materials. Conventional computational homogenization suffers from long computing times, which substantially limits its application in analyzing engineering problems. The neural networks can be used to construct fully decoupled approaches in nonlinear multiscale methods by mapping macroscopic loading and microscopic response. Computational homogenization methods for nonlinear material and implementation of offline multiscale computation are studied to generate data set. This article intends to model the multiscale constitution using feedforward neural network (FNN) and recurrent neural network (RNN), and appropriate set of loading paths are selected to effectively predict the materials behavior along unknown paths. Applications to two-dimensional multiscale analysis are tested and discussed in detail.

关键词： multiscale method constitutive model feedforward neural network recurrent neural network

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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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基于RBF神经网络的水文地质参数识别

张俊艳,魏连伟,韩文秀,邵景力,崔亚丽,张建立

《中国工程科学》 2004年第6卷第8期页码 74-78

摘要：针对传统水文地质参数识别方法的局限性，提出了水文地质参数识别的径向基函数(RBF )神经网络方法，并通过算例验证了它的可行性与有效性，实现了水文地质参数的自动识别，提高了计算效率，比BP神经网络具有更好的参数识别效果

关键词：地下水水文地质参数径向基函数(RBF)神经网络 BP神经网络

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A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

《机械工程前沿（英文）》 2022年第17卷第2期 doi: 10.1007/s11465-022-0673-7

摘要： Convolutional neural network (CNN) has achieved remarkable applications in fault diagnosis. However, the tuning aiming at obtaining the well-trained CNN model is mainly manual search. Tuning requires considerable experiences on the knowledge on CNN training and fault diagnosis, and is always time consuming and labor intensive, making the automatic hyper parameter optimization (HPO) of CNN models essential. To solve this problem, this paper proposes a novel automatic CNN (ACNN) for fault diagnosis, which can automatically tune its three key hyper parameters, namely, learning rate, batch size, and L2-regulation. First, a new deep reinforcement learning (DRL) is developed, and it constructs an agent aiming at controlling these three hyper parameters along with the training of CNN models online. Second, a new structure of DRL is designed by combining deep deterministic policy gradient and long short-term memory, which takes the training loss of CNN models as its input and can output the adjustment on these three hyper parameters. Third, a new training method for ACNN is designed to enhance its stability. Two famous bearing datasets are selected to evaluate the performance of ACNN. It is compared with four commonly used HPO methods, namely, random search, Bayesian optimization, tree Parzen estimator, and sequential model-based algorithm configuration. ACNN is also compared with other published machine learning (ML) and deep learning (DL) methods. The results show that ACNN outperforms these HPO and ML/DL methods, validating its potential in fault diagnosis.

关键词： deep reinforcement learning hyper parameter optimization convolutional neural network fault diagnosis

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Novel interpretable mechanism of neural networks based on network decoupling method

《工程管理前沿（英文）》 2021年第8卷第4期页码 572-581 doi: 10.1007/s42524-021-0169-x

摘要： The lack of interpretability of the neural network algorithm has become the bottleneck of its wide application. We propose a general mathematical framework, which couples the complex structure of the system with the nonlinear activation function to explore the decoupled dimension reduction method of high-dimensional system and reveal the calculation mechanism of the neural network. We apply our framework to some network models and a real system of the whole neuron map of Caenorhabditis elegans. Result shows that a simple linear mapping relationship exists between network structure and network behavior in the neural network with high-dimensional and nonlinear characteristics. Our simulation and theoretical results fully demonstrate this interesting phenomenon. Our new interpretation mechanism provides not only the potential mathematical calculation principle of neural network but also an effective way to accurately match and predict human brain or animal activities, which can further expand and enrich the interpretable mechanism of artificial neural network in the future.

关键词： neural networks interpretability dynamical behavior network decouple