Computational Inverse Techniques in Nondestructive Evaluation-猫眼课题宝

DOI：

10.1201/9780203494486

发表时间：

2003-06

期刊：

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中科院分区：

其他

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作者：

Gui-rong Liu;Xu Han

简介结构系统中遇到的正向和逆向问题解决逆向问题的一般程序本书概要逆向问题的基础知识一个简单的例子：单杆稍微复杂的问题：复合杆类型 III 不适定不适定反问题的类型显式矩阵系统矩阵形式系统的逆解通过奇异值分解进行一般逆函数形式的 (SVD) 系统：通过优化解决方案选择输出或效果模拟测量不适定问题的正则化不适定问题的正则化 Tikhonov 正则化 SVD 正则化迭代正则化方法离散化（投影）正则化过滤正则化传统优化技术1 优化在反演问题中的作用优化公式直接搜索基于梯度的方法非线性最小二乘法优化方法的一些参考遗传算法简介遗传算法的基本概念微型遗传算法代际工程遗传算法（IP-GA）改进的IP-GA 三参数IP-GA（IP3-GA）搜索空间缩减的遗传算法（SR-GA）遗传算法与梯度法相结合逆问题遗传算法实现中的其他小技巧遗传算法的一些参考GA 神经网络神经网络的一般概念神经网络在解决反问题中的作用多层感知器 MLP 的性能渐进式学习神经网络 NN 在神经网络上的简单应用参考文献冲击载荷的逆识别简介作为系统效应的位移梁表面冲击载荷的识别复合材料层压板表面上的线载荷复合材料表面上的点载荷层压板不适定分析复合材料材料常数的逆辨识问题简介使用均匀 mGA 使用实 mGA 使用组合优化方法使用渐进神经网络辨识弹性常数功能梯度材料材料特性的逆辨识问题简介混合规则使用基于梯度的方法优化使用统一 mGA 使用组合优化方法使用渐进 NN 模型使用弯曲波逆检测梁中的裂纹介绍具有水平分层的梁弯曲波梁模型用于冲击载荷瞬态响应的梁模型广泛的实验研究使用均匀 mGA 逆裂纹检测使用渐进 NN 逆检测复合材料中的分层裂纹层压板简介问题陈述使用均匀 mGA 分层检测使用 IP-GA 分层检测使用改进的 IP-GA 分层检测使用组合优化方法分层检测使用渐进式 NN 结构缺陷逆检测简介逆辨识公式使用均匀 mGA 使用牛顿求根法使用 Levenberg-Marquardt 方法其他应用电子系数辨识冷却系统 PCB 材料参数的识别薄膜材料特性的识别使用光纤测量的整体应变进行裂纹检测桁架结构中的缺陷检测蛋白质结构预测原子间势的拟合无阀微泵中的参数识别工程系统整体解决方案：新概念整体解决方案介绍方法逆算法数值示例

INTRODUCTION Forward and Inverse Problems Encountered in Structural Systems General Procedures to Solve Inverse Problems Outline of the Book FUNDAMENTALS OF INVERSE PROBLEMS A Simple Example: A Single-Bar A Slightly Complex Problem: A Composite Bar Type III Ill-Posedness Types of Ill-Posed Inverse Problems Explicit Matrix Systems Inverse Solution for Systems with Matrix Form General Inversion by Singular Value Decomposition (SVD) Systems in Functional Forms: Solution by Optimization Choice of the Outputs or Effects Simulated Measurement Examination of Ill-Posedness REGULARIZATION FOR ILL-POSED PROBLEMS Tikhonov Regularization Regularization by SVD Iterative Regularization Method Regularization by Discretization (Projection) Regularization by Filtering CONVENTIONAL OPTIMIZATION TECHNIQUES1 The Role of Optimization in Inverse Problems Optimization Formulations Direct Search Gradient-Based Methods Nonlinear Least Squares Method Some References for Optimization Methods GENETIC ALGORITHMS Introduction Basic Concept of GAs Micro-GAs Intergeneration Project Genetic Algorithm (IP-GA) Improved IP-GA IP-GA with Three Parameters (IP3-GA) GAs with Search Space Reduction (SR-GA) GA Combined with the Gradient-Based Method Other Minor Tricks in the Implementation of GAs for Inverse Problems Some References for GA NEURAL NETWORKS General Concepts of Neural Networks Role of Neural Networks in Solving Inverse Problems Multilayer Perceptrons Performance of MLP A Progressive Learning Neural Network A Simple Application of NN References on Neural Networks INVERSE IDENTIFICATION OF IMPACT LOADS Introduction Displacement as System Effects Identification of Impact Loads on the Surface of Beams Line Loads on the Surface of Composite Laminates Point Loads on the Surface of Composite Laminates Ill-Posedness Analysis INVERSE IDENTIFICATION OF MATERIAL CONSTANTS OF COMPOSITES Introduction Statement of the Problem Using the Uniform mGA Using the Real mGA Using the Combined Optimization Method Using the Progressive NN for Identifying Elastic Constants INVERSE IDENTIFICATION OF MATERIAL PROPERTY OF FUNCTIONALLY GRADED MATERIALS Introduction Statement of the Problem Rule-of-Mixture Use of Gradient-Based Optimization Methods Use of Uniform mGA Use of Combined Optimization Method Use of Progressive NN Model INVERSE DETECTION OF CRACKS IN BEAMS USING FLEXURAL WAVES Introduction Beams with a Horizontal Delamination Beam Model of Flexural Wave Beam Model of for Transient Response to an Impact Load Extensive Experimental Study Inverse Crack Detection Using Uniform mGA Inverse Crack Detection Using Progressive NN INVERSE DETECTION OF DELAMINATIONS IN COMPOSITE LAMINATES Introduction Statement of the Problem Delamination Detection Using Uniform mGA Delamination Detection Using the IP-GA Delamination Detection Using the Improved IP-GA Delamination Detection Using the Combined Optimization Method Delamination Detection Using the Progressive NN INVERSE DETECTION OF FLAWS IN STRUCTURES Introduction Inverse Identification Formulation Use of Uniform mGA Use of Newton's Root Finding Method Use of Levenberg -Marquardt Method OTHER APPLICATIONS Coefficients Identification for Electronic Cooling System Identification of the Material Parameters of a PCB Identification of Material Property of Thin Films Crack Detection Using Integral Strain Measured by Optic Fibers Flaw Detection in Truss Structure Protein Structure Prediction Fitting of Interatomic Potentials Parameter Identification in Valve-Less Micropumps TOTAL SOLUTION FOR ENGINEERING SYSTEMS: A NEW CONCEPT Introduction Approach Towards a Total Solution Inverse Algorithms Numerical Examples

Computational Inverse Techniques in Nondestructive Evaluation

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