Artificial Neural Networks for Forecasting of Fuzzy Time Series

Abstract: In this article, an artificial neural network for modeling and forecasting of fuzzy time series is presented. Modeling fuzzy time series with fuzzy data as random realizations of an underlying fuzzy random process enables forecasting of future fuzzy data following the observed time series. Analysis and forecasting of time series with fuzzy data may be carried out with the aid of artificial neural networks. A significant advantage is the fact that neural networks do not require a predetermined process model to simulate and forecast time series possessing fuzzy random characteristics. Artificial neural networks have the ability to learn the characteristics of an existing fuzzy time series, to represent the underlying fuzzy random process, and to forecast future fuzzy data following the time series observed. The algorithms developed are demonstrated using a numerical example.     

CPT-Based Liquefaction Evaluation Using Artificial Neural Networks

This article presents various artificial neural network (ANN) models for evaluating liquefaction resistance and potential of sandy soils. Various issues concerning ANN modeling such as data preprocessing, training algorithms, and implementation are discussed. The desired ANN is trained and tested with a large historical database of liquefaction performance at sites where cone penetration test (CPT) measurements are available. The ANN models are found to be effective in predicting liquefaction resistance and potential. The developed ANN models are ported to a spreadsheet for ease of use. A simple procedure for conducting uncertainty analysis to address the issue of parameter and model uncertainties is also presented using the ANN‐based spreadsheet model. This uncertainty analysis is carried out using @Risk, which is an add-in macro that works well with popular spreadsheet programs such as Microsoft Excel and Lotus 1-2-3. The results of the present study show that the developed ANN model has potential as a practical design tool for assessing liquefaction resistance of sandy soils.     

Artificial Neural Networks for Predicting the Response of Structural Systems with Viscoelastic Dampers

Abstract: Passive energy dissipation systems have been identified as one of the modern structural protective systems against seismic disturbances. Research and development activities are on globally to develop appropriate design procedures and suitable technology for application in the field. Structural systems with energy‐dissipating devices call for rigorous nonlinear analysis, which is a complex one and the results are highly sensitive to the type of input motion and component behavior assumed in the analysis. The Federal Emergency Management Agency 273 (FEMA 273) (1997) has suggested simplified procedures for replacing the original nonlinear system by an equivalent linear system. Recently, artificial intelligence (AI) techniques based on artificial neural networks (ANN) have been profitably used for solving complex problems of an iterative nature. Combining the equivalent model with an appropriate AI technique would help one to quickly predict the dynamic response of such yielding systems. This article highlights the feed forward back‐propagation neural network using the Levenberg–Marquardt algorithm for predicting the response quantity of systems with energy‐dissipating devices. The neural network is trained to reflect the nonlinear relationship of strength, stiffness, and damping existing in the system. The methodology developed is illustrated and validated with a chosen example from the FEMA 274 and is found to predict well the average peak displacement, base shear, and roof displacement. Based on these, the sensitivity studies have been carried out and the influence of each parameter on the results have been brought out. It may be noted that sensitivity details and the influence of each parameter do not show up in the regular time‐series analysis. The main advantage of the methodology and the network developed is in quick preliminary decision on the amount and the number of dampers required to reduce peak displacement for a new design as well as for retrofitting.     

神经网络法在混凝土强度研究中的应用

讨论了如何应用人工神经网络（ANN）的方法预测混凝土抗压强度,详细论述了采用BP算法建立混凝土抗压强度神经网络模型的过程,以及在活化剂作用下高掺量粉煤灰混凝土的强度效应,仿真结果表明,通过学习,BP网络可成功地建立非线性的强度模型,预测强度可达到较高精度。     

基于AHP与人工神经网络的供应商资格排名研究

目前招标单位采取的资格预审通常都是简单的筛选出有资质合格的投标单位,而把反向竞拍这种新机制引入到招投标中就需要在资格预审阶段对这些合格的招标单位进行排序。本文在分析资格预审指标的基础上,建立了层次分析法(AHP)与人工神经网络(ANN)纵向结合的综合评审数学模型,并通过算例验证该方法在电力工程货物采购资格排名中的应用。     

人工神经网络在机械阻抗法测桩结果评定中的应用

应用人工神经网络的基本原理,建立了一个基于人工神经网络的机械阻抗法测柱评定系统,实现了测桩结果评定智能化,克服了人为因素,提高了准确率。     

数据规划处理在人工神经网络中的应用

神经网络所用的样本数据以及这些数据的规划处理对网络性能和实际运用有着至关重要的影响!本文首先对神经网络在结构可靠性领域中应用的重要意义进行了探讨,然后在已有的数据处理方法的基础上提出了简便的数据规划处理算法,最后,用结构可靠度的实例验证了该算法的正确性!     

嵌岩桩极限承载力的径向基函数神经网络（RBF）预测

文章采用径基函数（RBF）神经网络来预测嵌岩桩的极限承地菌,是人神经网络在解决岩土工程方面的一种新的尝试。结果表明,采用RBF网络预测嵌嵌岩桩极限录载力具有较高精度,克服了反向传播（BP网络收敛慢、局部极值等缺点。     

用模糊神经网络对建筑物变形进行短期预测

提出采用模糊处理与人工神经网络相结合的方法，有效地实现对建筑物变形的短期预测，并用实例加以验证说明。     

Structural Health Monitoring via Measured Ritz Vectors Utilizing Artificial Neural Networks

Abstract:   A pattern recognition approach for structural health monitoring (SHM) is presented that uses damage-induced changes in Ritz vectors as the features to characterize the damage patterns defined by the corresponding locations and severity of damage. Unlike most other pattern recognition methods, an artificial neural network (ANN) technique is employed as a tool for systematically identifying the damage pattern corresponding to an observed feature. An important aspect of using an ANN is its design but this is usually skipped in the literature on ANN-based SHM. The design of an ANN has significant effects on both the training and performance of the ANN. As the multi-layer perceptron ANN model is adopted in this work, ANN design refers to the selection of the number of hidden layers and the number of neurons in each hidden layer. A design method based on a Bayesian probabilistic approach for model selection is proposed. The combination of the pattern recognition method and the Bayesian ANN design method forms a practical SHM methodology. A truss model is employed to demonstrate the proposed methodology.     

利用人工神经网络预测火灾下管桁架的温度

3层反向传播(BP)神经网络已用于预测火灾下平面管桁架钢的极限温度。网络模型的输入参数有直径比(β)、墙宽厚比(τ)、径厚比(γ)和荷载比,输出参数有极限温度。利用有限元软件ABAQUS建立神经网络模型。105组数据用于建立BP神经网络,15组数据用于测试和验证BP网络。建立BP网络的过程中,选用Levenberg-Marquardt反向传播算法。隐藏层选用tansig函数,输出层选用purelin函数。分析结果表明,使用BP网络模型预测的极限温度是准确有效的。     

A Comparative Analysis of Two Artificial Neural Networks Using Pavement Performance Prediction

The management of pavements requires the ongoing allocation of substantial manpower and capital resources by the responsible agencies. These agencies ultimately report to the executive and legislative branches of government, which require justification and proof of the efficacy of these expenditures. This and the need for improved engineering technical feedback have encouraged the development of pavement management systems (PMS). One goal of a PMS is to provide decision makers at all levels with optimal resource-allocation strategies. This requires evaluation of alternatives over an analysis period based on predicted values of pavement performance. This necessitates more reliable pavement performance prediction models. Traditional modeling uses multiple regression techniques to predict pavement performance from traffic, time, and pavement distress or various combinations of these factors. Within the last 10 years, new modeling techniques, including artificial neural networks (ANNs), have been applied to transportation problems. The ANNs examined usually have been of a single type called a dot product ANN. This paper examines a different type called the quadratic function ANN and compares the results to the dot product ANN. The quadratic function ANN is a generalized adaptive, feedforward neural network that combines supervised and self-organizing learning. Models were developed to predict roughness using both types of ANN on the same data samples and the results compared. The data samples were drawn from the Kansas Department of Transportation's PMS database. The results indicate a significant improvement in the use of the self-organizing quadratic function ANNs and lead to recommendations for specific areas of additional research.     

基于人工神经网络市政工程安全评价模型的研究

引对传统的市政工程安全评价方法存在的局限性，把市政工程看作一个复杂的人机，环境系统，将人工神经网络基本理论引入市政工程的安全评价中，建立较全面的市政工程安全评价指标体系，构建基于人工神经网络的非线性安全评价模型，并验证评价模型的可靠性。     

Use of Radial Basis Function Type Artificial Neural Networks for Runoff Simulation

In this paper, the applicability of the radial basis function (RBF) type artificial neural networks (ANNs) approach for modeling a hydrologic system is investigated. The method differs from the more widely used multilayer perceptron (MLP) approach in that the nonlinearity of the model is embedded only in the hidden layer of the network. Search for optimal model parameters is carried out in two steps, each of which can be made to be more efficient and much faster than in MLP. This approach is applied to simulate runoff discharges in a small catchment. The results show that the models based on RBF networks can predict runoff with accuracy comparable with that with the MLP approach. An added advantage of RBF network-based models is that they can be developed with relative ease and with much less time compared with their MLP counterparts.     

大跨屋盖结构风压分布特性的模糊神经网络预测

针对影响大跨屋盖结构风荷载分布特征的诸多复杂因素,并结合深圳会议展览中心风洞试验项目的研究,本文应用模糊神经网络方法,成功地预测了大跨屋盖结构的风压分布特性。研究结果表明,采用该方法可以综合考虑各种因素的影响,并十分有效、简捷地处理常规方法难以解决的问题,进一步的应用表明这种方法可以用于其它类型建筑物风洞试验的数据处理和风压分布预测。     

人工神经网络在强夯工程中的运用分析与讨论

鉴于当前人工神经网络在岩土工程中的应用越来越广泛的情况下 ,本文分析和比较了多种人工神经网络模型对强夯问题的适用性和可靠性 ,并提出了几个人工神经网络在应用过程中应注意的问题 ,使之能够更好地指导强夯工程实践     

基于Neuman理论的神经网络优化确定水文地质参数

针对利用非稳定流抽水试验资料确定潜水含水层参数传统方法的不足,系统分析考虑垂直分量和弹性释水的Neuman潜水井流模型解析解的基础上,利用实码加速遗传算法(RAGA)和自适应BP神经网络模型相结合对Neuman潜水井流模型解析解进行优化求解,提出确定潜水含水层水文地质参数的Neuman-BP法。以计算实例表明,Neuman-BP法不需分抽水时间———降深过程的前、后段分别进行参数确定,避免了前、后段所求导水系数T的不一致,既充分利用了抽水试验数据,又获得了较高精度的参数,简化了参数确定过程。