Estimation of elastic constant of rocks using an ANFIS approach

The engineering properties of the rocks have the most vital role in planning of rock excavation and construction for optimum utilization of earth resources with greater safety and least damage to surroundings. The design and construction of structure is influenced by physico-mechanical properties of rock mass. Young's modulus provides insight about the magnitude and characteristic of the rock mass deformation due to change in stress field. The determination of the Young's modulus in laboratory is very time consuming and costly. Therefore, basic rock properties like point load, density and water absorption have been used to predict the Young's modulus. Point load, density and water absorption can be easily determined in field as well as laboratory and are pertinent properties to characterize a rock mass. The artificial neural network (ANN), fuzzy inference system (FIS) and neuro fuzzy are promising techniques which have proven to be very reliable in recent years. In, present study, neuro fuzzy system is applied to predict the rock Young's modulus to overcome the limitation of ANN and fuzzy logic. Total 85 dataset were used for training the network and 10 dataset for testing and validation of network rules. The network performance indices correlation coefficient, mean absolute percentage error (MAPE), root mean square error (RMSE), and variance account for (VAF) are found to be 0.6643, 7.583, 6.799, and 91.95 respectively, which endow with high performance of predictive neuro-fuzzy system to make use for prediction of complex rock parameter.     

Prediction of scouring around an arch-shaped bed sill using Neuro-Fuzzy model

In this study, Adaptive Neuro-Fuzzy Inference System (ANFIS) has been used to model local scouring depth and pattern scouring around concave and convex arch shaped circular bed sills. The experimental part of this research study includes seven sets of laboratory test cases which were performed in an experimental flume under different flow conditions. A data set consists of 2754 data points of scouring depth were collected to use in the ANFIS model. The ratio of arch diameter, D, to flume width, W, is used as a non dimensional variables in all test cases. The results from ANFIS model were compared with the results of ANN model obtained by Homayoon et al. [24] and previously presented models. The results indicated that for D/W equal to 1 and 1.2, the ANFIS models produced a good performance for convex and concave bed sills. As a result, the ANFIS models can be used as an alternative to ANN for estimation of scour depth and scour pattern around a concave bed sill installed with a bridge pier.     

一种简化的三维模糊制器

针对三维模糊控制器规则多,结构复杂,难以实现的问题,提出了一种简化的三维模糊控制器。该方法是把三个输入量分别为偏差、偏差变化、偏差的偏差变化的一维模糊控制器加权融合实现简化的三维模糊控制器。并提出了根据不同类型的被控对象设置三个加权系数。加权系数具有粗调、细调和微调的作用,类似于传统PID调节器的比例、积分和微分,实现对系统静差的消除。     

火炮磁流变后坐系统仿真

将磁流变技术应用于火炮反后坐装置是目前正在发展的一种降低火炮后坐力的新技术,优化火炮反后坐装置控制,达到实时调节阻力。针对某型号火炮,在建立动力学模型和电磁模型的基础上设计了磁流变反后坐系统。为实现理想的后坐控制规律,提出了PID和模糊控制算法。利用ADAMS和MATLAB进行联合仿真,仿真结果表明,在后坐行程范围内,最大后坐阻力分别为3.71×105N,3.53×105N,相比传统火炮减小了13%和17%,并且模糊控制的后坐阻力曲线具有良好的"平台效应",实现了控制目标,表明磁流变阻尼器良好的可控性和应用于火炮后坐系统中的可行性。     

基于改进蚁群算法的模糊控制器优化设计

研究控制器优化问题,由于模糊控制系统参数无法同时优化,使得系统选择参数困难,使系统控制效果存在一定的缺陷,安全性和可靠性降低。为解决上述问题,提出了一种多种群进化蚁群算法对模糊控制器优化设计。采用懒蚂蚁效应的改进蚁群算法进行优化,在传统蚁群算法的基础上,采用多个种群并行,对算法的初始化、路径构建以及信息素更新改进,并引入到模糊控制器的隶属函数、模糊规则的优化搜索中,搜索出适应于不同控制阶段的模糊控制器参数及控制规则,并进行仿真。仿真结果证明了改进算法对模糊控制器的参数具有良好的搜索速度和精度,使系统有很强的鲁棒性。     

基于LQR直升机模糊滑模跟踪控制

研究直升机系统稳定性优化问题,由于直升机系统的强耦合和非线性特性的影响,使飞行的稳定性和实时跟踪性差。为解决上述问题,对直升机原始数学模型进行近似线性化和解耦处理,采用模糊滑模控制方法实现直升机姿态角度的跟踪控制。首先,在滑模面的设计中引入最优线性二次型调节器,构建一种积分型切换面。其次,以切换面及导数的乘积和滑模切换增益的变化量为模糊系统的变量,实时调整变结构控制项的切换增益。仿真结果表明,通过控制器设计能够实现直升机姿态角度跟踪的稳定性,对外界不确定干扰具有强鲁棒性且控制器输出抖振问题得到明显改善。     

选择性集成算法分类与比较

选择性集成是当前机器学习领域的研究热点之一。由于选择性集成属于NP"难"问题,人们多利用启发式方法将选择性集成转化为其他问题来求得近似最优解,因为各种算法的出发点和描述角度各不相同,现有的大量选择性集成算法显得繁杂而没有规律。为便于研究人员迅速了解和应用本领域的最新进展,本文根据选择过程中核心策略的特征将选择性集成算法分为四类,即迭代优化法、排名法、分簇法、模式挖掘法;然后利用UCI数据库的20个常用数据集,从预测性能、选择时间、结果集成分类器大小三个方面对这些典型算法进行了实验比较;最后总结了各类方法的优缺点,并展望了选择性集成的未来研究重点。     

图像处理中模糊算法问题的分析

图像处理是计算机常用技术之一,模糊算法的运用有助于提高图像处理的效果。传统图像处理存在诸多弊端,影响了用户获取信息的便捷性。本文分析了图像处理的基本操作内容,并阐述了模糊算法在图像处理流程中的运用,以进一步提升原始图像处理的效果。     

声发射源定位不确定度的计算

针对声发射检测中的声发射源定位的不确定性问题，就其影响因素，在常用的16MnR钢制板件上进行了检测实验，并开展了实验数据的计算、分析与讨论。     

基于Rough集的磨削参数决策系统

本文介绍了5层模糊神经网络的优缺点，提出了基于Rough集构造模糊神经网络的方法，并应用于多传感器的磨削参数决策系统，达到控制磨削加工质量的目的。