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1.
Xu Ri-qing 《浙江大学学报(A卷英文版)》2000,1(4):408-413
This paper presents a new method (GA-ANN) developed by combining genetic algorithm (GA) and artificial neural networks (ANN)
for determining parameters of soils and retaining walls of deep excavation. This method has the advantages of nonlinear projection
of neural networks, networks reasoning, prediction and good overall characteristics. it was first used for back analysis of
the problem of mechanics parameters for excavation. Case studies showed that the GA-ANN method is effective and practical
for back analysis of determining parameters.
Project supported by NSFC(5973860) and National Civil Defence fund of China 相似文献
2.
Parameters optimization and nonlinearity analysis of grating eddy current displacement sensor using neural network and genetic algorithm 总被引：1，自引：0，他引：1
A grating eddy current displacement sensor （GECDS） can be used in a watertight electronic transducer to realize long range displacement or position measurement with high accuracy in difficult industry conditions. The parameters optimization of the sensor is essential for economic and efficient production. This paper proposes a method to combine an artificial neural network （ANN） and a genetic algorithm （GA） for the sensor parameters optimization. A neural network model is developed to map the complex relationship between design parameters and the nonlinearity error of the GECDS, and then a GA is used in the optimization process to determine the design parameter values, resulting in a desired minimal nonlinearity error of about 0.11%. The calculated nonlinearity error is 0.25%. These results show that the proposed method performs well for the parameters optimization of the GECDS. 相似文献
3.
Electrical discharge machining （EDM） process, at present is still an experience process, wherein selected parameters are often far from the optimum, and at the same time selecting optimization parameters is costly and time consuming. In this paper, artificial neural network （ANN） and genetic algorithm （GA） are used together to establish the parameter optimization model. An ANN model which adapts Levenberg-Marquardt algorithm has been set up to represent the relationship between material removal rate （MRR） and input parameters, and GA is used to optimize parameters, so that optimization results are obtained. The model is shown to be effective, and MRR is improved using optimized machining parameters. 相似文献
4.
INFLUENCE OF MEASUREMENT ERRORS ON STRUCTURAL DAMAGE IDENTIFICATION USING ARTIFICIAL NEURAL NETWORKS 总被引：2，自引：0，他引：2
ＩＮＴＲＯＤＵＣＴＩＯＮＦｒｏｍｔｈｅｖｉｅｗｐｏｉｎｔｏｆｓｅｒｖｉｃｅａｂｉｌｉｔｙａｎｄｓａｆｅ ｔｙｏｆｓｔｒｕｃｔｕｒｅｓ,ｔｈｅｄｅｔｅｃｔｉｏｎｏｆｓｔｒｕｃｔｕｒａｌｄａｍ ａｇｅｉｓａｎｉｍｐｏｒｔａｎｔｉｓｓｕｅｆｏｒｃｉｖｉｌｅｎｇｉｎｅｅｒｓ.Ｉｎｔｈｅｐａｓｔｔｗｏｄｅｃａｄｅｓ,ｍａｎｙｅｆｆｏｒｔｓｈａｖｅｂｅｅｎｍａｄｅｔｏｍｏｎｉｔｏｒｓｔｒｕｃｔｕｒａｌｉｎｔｅｇｒｉｔｙｕｓｉｎｇｃｈａｎｇｅｓｉｎｍｏｄａｌｐａｒａｍｅｔｅｒｓｔｈｒｏｕｇｈｖｉｂｒａｔｉｏｎｍｅ… 相似文献
5.
目的：建立不经分离同时测定安痛定注射液中三组份含量的方法。方法：运用误差反向传播(BP)的人工神经网络法(ANN)并将人工神经网络法与标准法测得实际样品的结果进行比较。结果：该法测得模拟样品中氨基比林、安替比林、巴比妥的平均回收率分别为99．4％、100．6％、100．2％，相对标准偏差分别为2．0％、1.8％、1．7％，经t检验表明，两者无显著差异。结论：人工神经网络法可快速、准确地测定复方药物制剂多组份含量。 相似文献
6.
提出了基于人工神经网络（ＡｒｔｉｆｉｃｉａｌＮｅｕｒａｌＮｅｔｗｏｒｋｓ）对动力结构进行系统辨识的方法，即应用人工神经网络预测结构地震响应．采用ＢＰ算法的前馈网络（简称ＢＰ网络）对剪切模型结构进行系统辨识．首先用实际地震波及相应的模拟地震响应训练本文提出的ＢＰ网络，然后用“已学会”的ＢＰ网络预测其它地震波激励下的结构地震响应．还讨论了网络拓扑结构、输入单元数等对网络学习和预测的影响．通过本文可以发现，合适的人工神经网络结构能准确地辨识结构动力特性和预测结构动力响应 相似文献
7.
In order to sufficiently exploit the advantages of different signal processing methods, such as wavelet transformation (WT), artificial neural networks (ANN) and expert rules (ER), a synthesized multi-method was introduced to detect and classify the epileptic waves in the EEG data. Using this method, at first, the epileptic waves were detected from pre-processed EEG data at different scales by WT, then the characteristic parameters of the chosen candidates of epileptic waves were extracted and sent into the well-trained ANN to identify and classify the true epileptic waves,and at last, the detected epileptic waves were certificated by ER. The statistic results of detection and classification show that, the synthesized multi-method has a good capacity to extract signal features and to shield the signals from the random noise. This method is especially fit for the analysis of the biomedical signals in biomedical engineering which are usually non-placid and nonlinear. 相似文献
8.
对BP型ANN网络用于模拟电路故障诊断的特点进行了介绍,探讨了利用遗传算法确定BP型ANN网络参数的方法,并给出了遗传算法与BP型ANN相结合实现模拟电路故障诊断的应用.实践表明,该方法的诊断精度、诊断速度以及建立诊断模型的自动化程度都有了较大的提高． 相似文献
9.
10.
This paper deals with a multi-objective parameter optimization framework for energy saving in injection molding process. It
combines an experimental design by Taguchi’s method, a process analysis by analysis of variance (ANOVA), a process modeling
algorithm by artificial neural network (ANN), and a multi-objective parameter optimization algorithm by genetic algorithm
(GA)-based lexicographic method. Local and global Pareto analyses show the trade-off between product quality and energy consumption.
The implementation of the proposed framework can reduce the energy consumption significantly in laboratory scale tests, and
at the same time, the product quality can meet the pre-determined requirements. 相似文献
11.
Thomas F. Rienzo Kuriakose K. Athappilly 《Decision Sciences Journal of Innovative Education》2012,10(4):515-520
Business students taking data mining classes are often introduced to artificial neural networks (ANN) through point and click navigation exercises in application software. Even if correct outcomes are obtained, students frequently do not obtain a thorough understanding of ANN processes. This spreadsheet model was created to illuminate the roles of the following ANN parameters: weights, learning rates, threshold functions, and transformation functions. The spreadsheet ANN model project is given early in the semester, just after ANN is introduced. Students can see effects of ANN parameters as they make changes to spreadsheet model inputs, greatly enhancing discussion of ANN processes. After working with the spreadsheet model, students have expressed an appreciation for decisions based on patterns of historic data, and they like the ability to peek “behind the curtain” at processes of predictive software packages. 相似文献
12.
WU Xiao-juan ZHU Xin-jian CAO Guang-yi TU Heng-yong 《浙江大学学报(A卷英文版)》2007,8(9):1505-1509
The solid oxide fuel cell (SOFC) is a nonlinear system that is hard to model by conventional methods. So far,most existing models are based on conversion laws,which are too complicated to be applied to design a control system. To facilitate a valid control strategy design,this paper tries to avoid the internal complexities and presents a modelling study of SOFC per-formance by using a radial basis function (RBF) neural network based on a genetic algorithm (GA). During the process of mod-elling,the GA aims to optimize the parameters of RBF neural networks and the optimum values are regarded as the initial values of the RBF neural network parameters. The validity and accuracy of modelling are tested by simulations,whose results reveal that it is feasible to establish the model of SOFC stack by using RBF neural networks identification based on the GA. Furthermore,it is possible to design an online controller of a SOFC stack based on this GA-RBF neural network identification model. 相似文献
13.
1 Introduction Direct methanol fuel cell ( DMFC) is desirable toserve as the power systemfor portable devices such ascellular phones , portable computers ,etc. due to thetheoretically high energy density and the liquid fuelused that can be stored and tran… 相似文献
14.
本文提出了一种基于模糊方向线索特征 (fuzzydirectionallineelementfeature,FDLEF)与人工神经网络 (artificialneuralnetworks,ANN)相结合的手写体汉字识别方法 (FDLEF -ANN) ,解决了单一FDLEF方法对相似字识别率低的问题 .这种方法分两级识别 ,先由FDLEF识别模块进行识别 ,将识别结果送至选择器 ,若识别结果不属于预定义的相似字集合簇 ,则该结果即为最终识别结果 ,否则 ,将其送至人工神经网络识别模块进行相似字的识别 .本方法既保留了原FDLEF方法的优点又提高了对相似字的识别率 ,FDLEF -ANN系统对相似字的识别率由 78 0 9%提高到 82 97% . 相似文献
15.
Fault diagnosis of valve clearance in diesel engine based on BP neural network and support vector machine 总被引：1，自引：0，他引：1
Based on wavelet packet transformation(WPT), genetic algorithm(GA), back propagation neural network(BPNN)and support vector machine(SVM), a fault diagnosis method of diesel engine valve clearance is presented. With power spectral density analysis, the characteristic frequency related to the engine running conditions can be extracted from vibration signals. The biggest singular values(BSV)of wavelet coefficients and root mean square (RMS)values of vibration in characteristic frequency sub-bands are extracted at the end of third level decomposition of vibration signals, and they are used as input vectors of BPNN or SVM. To avoid being trapped in local minima, GA is adopted. The normal and fault vibration signals measured in different valve clearance conditions are analyzed. BPNN, GA back propagation neural network (GA-BPNN), SVM and GA-SVM are applied to the training and testing for the extraction of different features, and the classification accuracies and training time are compared to determine the optimum fault classifier and feature selection. Experimental results demonstrate that the proposed features and classification algorithms give classification accuracy of 100%. 相似文献
16.
Artificial neural networks (ANNs) have been widely used to solve a number of problems to which analytical solutions are difficult to obtain using traditional mathematical approaches. Such problems exist also in the analysis of industrial robots. This paper presents an overview of ANN applications to robot kinematics, dynamics, control, trajectory and path planning, and sensing. Reasons for using or not using ANNs to industrial robots are explained as well. 相似文献
17.
Influence of measurement errors on structural damage identification using artificial neural networks
The effect of measurement errors on structural damage identification using artificial neural networks (ANN) was investigated
in this study. By using back-propagation (BP) networks with proper input vectors, numerical simulation tests for damage detection
on a six-storey frame were conducted with measurement errors in deterministic as well as probabilistic senses. The identifiability
using ANN for damage location and extent was studied for the cases of measurement errors with different degrees. The results
showed that there exists a critical level of measurement error beyond which the probability of correct identification is sharply
decreased. The identifiability using the neural networks in the presence of modeling and measurement errors is finally verified
using experimental data on a two-storey steel frame.
Project supported by Hong Kong Polytechnic University. 相似文献
18.
付佩宣 《暨南大学华文学院学报》2014,(4):71-78
实用汉语水平认定考试（简称C．TEST）是用来测试母语非汉语的外籍人士在国际环境下社会生活以及日常工作中实际运用汉语能力的考试。由于C．TEST的考试题目公开,题库数量较小,所以通过一般标准化考试采用的在部分目标被试中实施预测（fieldtest）的方法来获取考试题目的难度参数存在困难。然而,人工神经网络技术作为现代人工智能研究的成果,在预测（prediction）领域发挥了很大作用。本文选取C．TEST（A—D级）的阅读理解题目作为研究材料,运用人工神经网络技术对其难度进行预测,得到了网络预测难度值与实际考试难度值显著相关的研究结果。这一结果表明,利用人工神经网络模型对语言测验的题目难度等参数进行预测是可行的。 相似文献
19.
基于主成分分析的GA-BPNN遥感图像分类研究 总被引：2，自引：0，他引：2
在高原山地地区,传统遥感分类方法分类精度低,而标准BP神经网络分类方法在实际应用中也难以胜任.探讨对数据源主成分分析特征选择的基础上,用量化共轭梯度法改进标准BP算法,采用GA优化BP网络的隐层神经元数目、初始权重.并以香格里拉县ETM+遥感图像为例,在DEM地形数据辅助下,训练网络使其收敛,仿真输出.结果表明,其分类总精度为84.52%,Kappa系数为0.8317,比最大似然法分类精度提高了9.08个百分点,验证了GA优化的BP网络遥感图像分类的可行性和有效性. 相似文献
20.
周晓海 《河北职业技术学院学报》2009,(2)
介绍了人工神经网络的生理基础以及算法的基本结构,对其在水文地质学中进行反求参数的应用进行了阐述,并就应用的具体步骤及所建立的网络模型进行讨论。 相似文献