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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    

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Predicting the yield of pomegranate oil from supercritical extraction using artificial neural networks

J. Sargolzaei, A. Hedayati Moghaddam

《化学科学与工程前沿（英文）》 2013年 第7卷 第3期   页码 357-365 doi: 10.1007/s11705-013-1336-3

摘要： Various simulation tools were used to develop an effective intelligent system to predict the effects of temperature and pressure on an oil extraction yield. Pomegranate oil was extracted using a supercritical CO (SC-CO ) process. Several simulation systems including a back-propagation neural network (BPNN), a radial basis function neural network (RBFNN) and an adaptive-network-based fuzzy inference system (ANFIS) were tested and their results were compared to determine the best predictive model. The performance of these networks was evaluated using the coefficient of determination ( ) and the mean square error (MSE). The best correlation between the predicted and the experimental data was achieved using the BPNN method with an of 0.9948.

关键词： oil recovery     artificial intelligence     extraction     neural networks     supercritical extraction    

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The use of Artificial Neural Networks to estimate seismic damage and derive vulnerability functions for

Tiago Miguel FERREIRA, João ESTÊVÃO, Rui MAIO, Romeu VICENTE

《结构与土木工程前沿（英文）》 2020年 第14卷 第3期   页码 609-622 doi: 10.1007/s11709-020-0623-6

摘要： This paper discusses the adoption of Artificial Intelligence-based techniques to estimate seismic damage, not with the goal of replacing existing approaches, but as a mean to improve the precision of empirical methods. For such, damage data collected in the aftermath of the 1998 Azores earthquake (Portugal) is used to develop a comparative analysis between damage grades obtained resorting to a classic damage formulation and an innovative approach based on Artificial Neural Networks (ANNs). The analysis is carried out on the basis of a vulnerability index computed with a hybrid seismic vulnerability assessment methodology, which is subsequently used as input to both approaches. The results obtained are then compared with real post-earthquake damage observation and critically discussed taking into account the level of adjustment achieved by each approach. Finally, a computer routine that uses the ANN as an approximation function is developed and applied to derive a new vulnerability curve expression. In general terms, the ANN developed in this study allowed to obtain much better approximations than those achieved with the original vulnerability approach, which has revealed to be quite non-conservative. Similarly, the proposed vulnerability curve expression was found to provide a more accurate damage prediction than the traditional analytical expressions.

关键词： Artificial Neural Networks     seismic vulnerability     masonry buildings     damage estimation     vulnerability curves    

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Predicting the strength properties of slurry infiltrated fibrous concrete using artificial neural network

T. Chandra Sekhara REDDY

《结构与土木工程前沿（英文）》 2018年 第12卷 第4期   页码 490-503 doi: 10.1007/s11709-017-0445-3

摘要： This paper is aimed at adapting Artificial Neural Networks (ANN) to predict the strength properties of SIFCON containing different minerals admixture. The investigations were done on 84 SIFCON mixes, and specimens were cast and tested after 28 days curing. The obtained experimental data are trained using ANN which consists of 4 input parameters like Percentage of fiber (PF), Aspect Ratio (AR), Type of admixture (TA) and Percentage of admixture (PA). The corresponding output parameters are compressive strength, tensile strength and flexural strength. The predicted values obtained using ANN show a good correlation between the experimental data. The performance of the 4-14-3 architecture was better than other architectures. It is concluded that ANN is a highly powerful tool suitable for assessing the strength characteristics of SIFCON.

关键词： artificial neural networks     root mean square error     SIFCON     silica fume     metakaolin     steel fiber    

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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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Unconfined compressive strength prediction of soils stabilized using artificial neural networks and support

Alireza TABARSA, Nima LATIFI, Abdolreza OSOULI, Younes BAGHERI

《结构与土木工程前沿（英文）》 2021年 第15卷 第2期   页码 520-536 doi: 10.1007/s11709-021-0689-9

摘要： This study aims to improve the unconfined compressive strength of soils using additives as well as by predicting the strength behavior of stabilized soils using two artificial-intelligence-based models. The soils used in this study are stabilized using various combinations of cement, lime, and rice husk ash. To predict the results of unconfined compressive strength tests conducted on soils, a comprehensive laboratory dataset comprising 137 soil specimens treated with different combinations of cement, lime, and rice husk ash is used. Two artificial-intelligence-based models including artificial neural networks and support vector machines are used comparatively to predict the strength characteristics of soils treated with cement, lime, and rice husk ash under different conditions. The suggested models predicted the unconfined compressive strength of soils accurately and can be introduced as reliable predictive models in geotechnical engineering. This study demonstrates the better performance of support vector machines in predicting the strength of the investigated soils compared with artificial neural networks. The type of kernel function used in support vector machine models contributed positively to the performance of the proposed models. Moreover, based on sensitivity analysis results, it is discovered that cement and lime contents impose more prominent effects on the unconfined compressive strength values of the investigated soils compared with the other parameters.

关键词： unconfined compressive strength     artificial neural network     support vector machine     predictive models     regression    

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Lateral-torsional buckling capacity assessment of web opening steel girders by artificial neural networks

Yasser SHARIFI,Sajjad TOHIDI

《结构与土木工程前沿（英文）》 2014年 第8卷 第2期   页码 167-177 doi: 10.1007/s11709-014-0236-z

摘要： Bridge girders exposed to aggressive environmental conditions are subject to time-variant changes in resistance. There is therefore a need for evaluation procedures that produce accurate predictions of the load-carrying capacity and reliability of bridge structures to allow rational decisions to be made about repair, rehabilitation and expected life-cycle costs. This study deals with the stability of damaged steel I-beams with web opening subjected to bending loads. A three-dimensional (3D) finite element (FE) model using ABAQUS for the elastic flexural torsional analysis of I-beams has been used to assess the effect of web opening on the lateral buckling moment capacity. Artificial neural network (ANN) approach has been also employed to derive empirical formulae for predicting the lateral-torsional buckling moment capacity of deteriorated steel I-beams with different sizes of rectangular web opening using obtained FE results. It is found out that the proposed formulae can accurately predict residual lateral buckling capacities of doubly-symmetric steel I-beams with rectangular web opening. Hence, the results of this study can be used for better prediction of buckling life of web opening of steel beams by practice engineers.

关键词： steel I-beams     lateral-torsional buckling     finite element (FE) method     artificial neural network (ANN) approach    

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基于RBF神经网络的隧洞围岩变形预测方法

张俊艳,冯守中,刘东海

《中国工程科学》 2005年 第7卷 第10期   页码 87-90

摘要：

传统回归方法对于围岩变形时程曲线存在反弯点，这种情况的模拟具有难度。提出的基于RBF神经网络的隧道围岩变形预测方法，不仅能很好地描述复杂的围岩变形时程曲线，而且比BP神经网络具有更快的收敛速度和更好的全局搜索能力。实例研究验证了该方法的有效性与可行性。

关键词： RBF神经网络     隧洞施工     围岩变形     预测    

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可见光波段的深度衍射神经网络 Article

陈航, 冯佳楠, 江闽伟, 王逸群, 林杰, 谭久彬, 金鹏

《工程（英文）》 2021年 第7卷 第10期   页码 1485-1493 doi: 10.1016/j.eng.2020.07.032

摘要：

基于衍射光学元件的光学深度学习在并行处理、计算速度和计算效率方面有着独特优势。深度衍射神经网络（D2NN）是其中一项具有里程碑意义的研究工作。D2NN在太赫兹波段通过3D打印进行神经网络的物理固化。鉴于太赫兹波段下存在的粒子间耦合限制和材料损耗，本文将D2NN的应用波段延展至可见光波段，并提出了包括修订公式在内的一般理论，解决了工作波长、人工神经元特征尺寸和加工制备之间的矛盾。在632.8 nm的工作波长下，本文提出了一种新颖的可见光D2NN分类器，可用于原始目标（手写数字0~9）和已更改目标（被遮盖和涂改目标）的目标识别。本文获得的实验分类精度（84%）和数值分类精度（91.57%）量化了理论设计和制造系统性能之间的匹配程度。本文所提出的一般理论模型可将D2NN应用于各种实际问题或设计全新的应用场景。

关键词： 光计算     光学神经网络     深度学习     光学机器学习     深度衍射神经网络    

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immune-inspired optimum state-of-charge trajectory estimation using upcoming route information preview and neuralnetworks for plug-in hybrid electric vehicles fuel economy

Ahmad MOZAFFARI,Mahyar VAJEDI,Nasser L. AZAD

《机械工程前沿（英文）》 2015年 第10卷 第2期   页码 154-167 doi: 10.1007/s11465-015-0336-z

摘要：

The main proposition of the current investigation is to develop a computational intelligence-based framework which can be used for the real-time estimation of optimum battery state-of-charge (SOC) trajectory in plug-in hybrid electric vehicles (PHEVs). The estimated SOC trajectory can be then employed for an intelligent power management to significantly improve the fuel economy of the vehicle. The devised intelligent SOC trajectory builder takes advantage of the upcoming route information preview to achieve the lowest possible total cost of electricity and fossil fuel. To reduce the complexity of real-time optimization, the authors propose an immune system-based clustering approach which allows categorizing the route information into a predefined number of segments. The intelligent real-time optimizer is also inspired on the basis of interactions in biological immune systems, and is called artificial immune algorithm (AIA). The objective function of the optimizer is derived from a computationally efficient artificial neural network (ANN) which is trained by a database obtained from a high-fidelity model of the vehicle built in the Autonomie software. The simulation results demonstrate that the integration of immune inspired clustering tool, AIA and ANN, will result in a powerful framework which can generate a near global optimum SOC trajectory for the baseline vehicle, that is, the Toyota Prius PHEV. The outcomes of the current investigation prove that by taking advantage of intelligent approaches, it is possible to design a computationally efficient and powerful SOC trajectory builder for the intelligent power management of PHEVs.

关键词： trip information preview     intelligent transportation     state-of-charge trajectory builder     immune systems     artificial neural network    

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Service life prediction of fly ash concrete using an artificial neural network

《结构与土木工程前沿（英文）》 2021年 第15卷 第3期   页码 793-805 doi: 10.1007/s11709-021-0717-9

摘要： Carbonation is one of the most aggressive phenomena affecting reinforced concrete structures and causing their degradation over time. Once reinforcement is altered by carbonation, the structure will no longer fulfill service requirements. For this purpose, the present work estimates the lifetime of fly ash concrete by developing a carbonation depth prediction model that uses an artificial neural network technique. A collection of 300 data points was made from experimental results available in the published literature. Backpropagation training of a three-layer perceptron was selected for the calculation of weights and biases of the network to reach the desired performance. Six parameters affecting carbonation were used as input neurons: binder content, fly ash substitution rate, water/binder ratio, CO2 concentration, relative humidity, and concrete age. Moreover, experimental validation carried out for the developed model shows that the artificial neural network has strong potential as a feasible tool to accurately predict the carbonation depth of fly ash concrete. Finally, a mathematical formula is proposed that can be used to successfully estimate the service life of fly ash concrete.

关键词： concrete     fly ash     carbonation     neural networks     experimental validation     service life    

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high-density polyethylene pyrolysis using kinetic parameters based on thermogravimetric and artificial neuralnetworks

《环境科学与工程前沿（英文）》 2023年 第17卷 第1期 doi: 10.1007/s11783-023-1606-3

摘要：

● Reducting the sampling frequency can enhance the modelling process.

关键词： HDPE     Pyrolysis     Kinetics     Thermogravimetric     ANOVA     Artificial neural network    

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过程神经网络的训练及其应用

何新贵,梁久祯,许少华

《中国工程科学》 2001年 第3卷 第4期   页码 31-35

摘要：

研究过程神经网络的学习算法及其在过程模式识别中的应用。针对权值基展开的过程神经网络讨论了权值基的选取规则和对采样曲线的标准化处理问题，给出了含一个隐层的过程神经网络的误差反传播学习算法。以聚合化学反应和渗流实验两个具体实例验证了算法的有效性，也说明了过程神经网络的广泛应用前景。

关键词： 过程神经网络     学习算法     模式识别     化学反应     渗流    

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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：

水文地质参数识别问题是水文地质学上的一个难题。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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人工神经网络在弹体侵彻混凝土深度中的应用

李建光,李永池,王玉岚

《中国工程科学》 2007年 第9卷 第8期   页码 77-81

摘要：

针对弹体对混凝土材料侵彻深度问题，通过量纲分析和神经网络理论，建立了弹体侵彻深度h网络输出量与弹体长度lp、弹的长径比 lp/d、弹体形状系数ψ、弹体与混凝土的比强度σyt/σyp、弹体与混凝土的密度比ρp/ρt等13个网络输入量之间的非线性映射关系。并采用 RBF网络模型，通过Forrestal等文献的试验样本对网络模型训练，获得了弹体对混凝土材料侵彻深度的网络模型，输出结果满意。

关键词： 神经网络     量纲分析     侵彻混凝土深度     非线性映射关系     RBF网络    

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标题 作者 时间 类型 操作

Novel interpretable mechanism of neural networks based on network decoupling method

期刊论文

Predicting the yield of pomegranate oil from supercritical extraction using artificial neural networks

J. Sargolzaei, A. Hedayati Moghaddam

期刊论文

The use of Artificial Neural Networks to estimate seismic damage and derive vulnerability functions for

Tiago Miguel FERREIRA, João ESTÊVÃO, Rui MAIO, Romeu VICENTE

期刊论文

Predicting the strength properties of slurry infiltrated fibrous concrete using artificial neural network

T. Chandra Sekhara REDDY

期刊论文

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

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

期刊论文

Unconfined compressive strength prediction of soils stabilized using artificial neural networks and support

Alireza TABARSA, Nima LATIFI, Abdolreza OSOULI, Younes BAGHERI

期刊论文

Lateral-torsional buckling capacity assessment of web opening steel girders by artificial neural networks

Yasser SHARIFI,Sajjad TOHIDI

期刊论文

基于RBF神经网络的隧洞围岩变形预测方法

张俊艳,冯守中,刘东海

期刊论文

可见光波段的深度衍射神经网络

陈航, 冯佳楠, 江闽伟, 王逸群, 林杰, 谭久彬, 金鹏

期刊论文

immune-inspired optimum state-of-charge trajectory estimation using upcoming route information preview and neuralnetworks for plug-in hybrid electric vehicles fuel economy

Ahmad MOZAFFARI,Mahyar VAJEDI,Nasser L. AZAD

期刊论文

Service life prediction of fly ash concrete using an artificial neural network

期刊论文

high-density polyethylene pyrolysis using kinetic parameters based on thermogravimetric and artificial neuralnetworks

期刊论文

过程神经网络的训练及其应用

何新贵,梁久祯,许少华

期刊论文

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

期刊论文

人工神经网络在弹体侵彻混凝土深度中的应用

李建光,李永池,王玉岚

期刊论文