神经网络在机器人控制中的应用

由于许多不确定性因素影响机器人的控制过程,针对机器人控制过程中的这种情况应用较好的改进的网络学习算法设计了一种神经网络结构,并应用于机器人的控制,取得了满意的仿真结果。     

基于生成对抗网络的深度Q学习空调冷负荷预测算法

针对传统预测算法在商场空调冷负荷预测中存在数据样本不足和预测精度低的问题,提出了一种基于生成对抗网络的深度Q学习的算法预测商场的空调冷负荷.利用生成对抗性网络生成与历史冷负荷数据相似的冷负荷数据,通过生成对抗网络解决真实负荷数据样本不足的问题.然后,利用深度Q学习网络预测未来时刻空调冷负荷数据.为了验证算法的有效性,以...     

基于改进YOLOV3算法的遥感图像油罐检测

针对遥感图像中的油罐检测问题,借鉴深度神经网络的思想,提出了一种基于改进YOLOV3算法的图像检测方法。首先在原有YOLOV3算法框架中添加空间变换网络(spatial transformer networks,STN),使其成为具备空间变换学习能力的模型;然后通过k-means聚类算法对数据集进行分析,重新设计初始候选框大小;最后训练和测试网络,建立包含9 724个油罐目标的遥感图像数据集。实验结果表明:改进的YOLOV3算法具有良好的性能,在测试集中的召回率可达到95.64%,比原算法提升了3.52%;准确率可达到93.92%,比原算法提升了2.81%。     

应用自递归神经网络(SRNN)预测结构响应

本文提出了一种新的自递归神经网络结构.这种网络结构由全递归网络改造而成,只有一个隐层,而且隐单元仅存在自递归.研究了这种网络的学习算法.为了保证快速学习收敛,应用Lyapunov函数得到一种自适应学习率方法.用这种方法对一两层建筑结构响应进行在线预测.计算机仿真结果表明,这种网络学习算法是有效的,并且是可行的     

基于模糊自适应BP算法的工程造价估算

工程造价估算是工程建设管理的重要环节 ,应用BP网络进行估算快速方便 ,但是BP网络学习收敛速度较慢 ,学习效率较低 ,常不能保证全局收敛而导致学习失败 .对其算法进行了改进 ,提出了一种模糊自适应BP算法 ,并通过一个工程实例 ,应用改进后的BP网络进行工程投资估算 ,结果表明 ;该方法可行 ,估算结果精度较高     

神经网络在高强粉煤灰混凝土强度预测及优化设计中的应用

首先介绍了神经网络应用中使用最为广泛的BP网络和RBF网络的模型及其学习算法，然后将其用于高强粉煤灰混凝土的强度预测和优化设计，并与线性回归进行了对比，结果表明神经网络方法具有较高的预测精度，在混凝土性能预测和优化设计中具有广阔的应用前景。     

基于微粒群算法和模糊神经网络的火灾探测研究

针对火灾探测的特点,将模糊系统和神经网络有机结合,实现模糊系统设计参数的自动调整。采用符合国家标准明火、阴燃火以及厨房环境下的干扰火等作为模糊神经网络的训练样本和测试样本,依据模糊神经网络算法要求,完成了网络结构的设计,并给出相应的计算模型,利用微粒群算法对网络的权值进行学习与训练。结果表明,该算法在探测国家标准火的火灾状态方面具有有效性和可行性。     

基于样式生成对抗网络的风景园林方案生成及设计特征识别

【目的】人工智能算法能否有效习得风景园林设计特征是一个值得探讨的问题。【方法】采用样式生成对抗网络2代（style generative adversarial network2, StyleGAN2）算法,通过算法训练生成风景园林设计方案;之后拆解StyleGAN2算法中的w向量,采用主成分分析（principal component analysis, PCA）降维方法和无监督学习K均值聚类方法可视化w向量特征;最后根据w向量的数据特征和生成结果的关联,分析算法对设计方案特征的提取能力。【结果】StyleGAN2可以为不同类型的场地生成高质量和多样化的设计方案,并且可以识别和提取一些高维抽象设计特征,如植被密度、水域面积、铺装面积、道路网络结构等。【结论】神经网络不仅可以识别图像形态特征,还可以在没有人类指导的情况下,无监督学习识别部分抽象的高维设计特征。但目前大部分设计特征耦合性较高,这是风景园林工作的复杂性和算法低可解释性共同导致的,需要未来进一步探索。     

基于社交网络的社会化推荐算法研究

社交网络日趋活跃,基于社交网络的推荐成为电子商务推荐系统研究的热点领域之一;如何利用社交网络数据给用户进行推荐物品,是基于社交网络的推荐算法的研究重点。对社交网络的定义、社交网络数据的分类进行概述,研究基于邻域的社会化推荐和基于图的社会化推荐算法;结合实际推荐系统对社会化推荐算法进行改进设计。     

冒泡排序及其改进算法的教学设计与实现

在C语言程序设计中,排序算法是使用频率最高的算法之一,而冒泡排序是其中一种典型且相对简单的方法,学习它是为后面的选择排序作铺垫。文章在最初的冒泡排序算法上改进了两次,使算法达到一个更好的效果。通过冒泡排序及其改进算法的学习,可以提高学生的程序设计能力,为今后在算法与程序设计方面的进一步研究和学习打下基础。     

基于EKF的小波神经网络在变形预测中的应用

提出一种新颖的用于变形预测的基于扩展Kalman滤波的小波神经网络学习算法,与BP算法相比,该方法具有更好的收敛率和学习能力,并通过实例计算证明了该方法具有较高的精度和较快的计算速度。     

Novel integration of extreme learning machine and improved Harris hawks optimization with particle swarm optimization-based mutation for predicting soil consolidation parameter

The study proposes an improved Harris hawks optimization(IHHO) algorithm by integrating the standard Harris hawks optimization(HHO) algorithm and mutation-based search mechanism for developing a high-performance machine learning solution for predicting soil compression index. HHO is a newly introduced meta-heuristic optimization algorithm(MOA) used to solve continuous search problems.Compared to the original HHO, the proposed IHHO can evade trapping in local optima, which in turn raises the sear...     

Extensible prototype learning for real-time traffic signal control

Congestion resolution continues to remain a challenge even though various signal control systems have been developed for traffic-intersection control. To address this issue, reinforcement learning (RL)-based approaches that focus on solving the associated data-driven problems have been proposed. However, only a few methods have been developed and applied to dual-ring traffic signal control systems. Therefore, we develop an RL-based traffic signal control model for such a system to efficiently allocate the green interval in different oversaturation states of the conflicting phases. The proposed model employs a deep deterministic policy gradient algorithm to optimize the green value in the continuous action space. Further, we develop an extensible prototype learning framework for application to new intersections without additional transfer learning. The proposed model is validated based on morning peak hours in a simulation environment that reflects the actual intersection phase system and minimum green time constraints. The proposed model achieves an average 20% intersection delay reduction, compared with the fixed control method.     

基于小波网络的三峡永久船闸中隔墩稳定性分析和预测模型

三峡永久船闸中隔墩的稳定性影响着船闸的安全运营，但其安全系数与控制因素之间具有很强的非线性映射关系。引入小波网络，利用其良好的时频局域化性质和强自学习功能，通过一些工程实例作为网络的训练模式，来刻划和模拟它们之间的非线性映射关系，并将训练好的网络来分析和预测永久船闸中隔墩的稳定性。分析结果表明，模型的拟合和预测精度均较高，可用于定量评价其稳定性；并且，模型具有强抗噪音能力，而这对解决实际工程问题是非常重要的。同时，设计的网络学习算法性能较优，这是由于在小波网络整个参数空间中的子空间能最小化误差目标函数。     

Debugging Finite Element Programs Input Data with Machine Learning

Abstract: Debugging of the input data of a structural analysis program is a troublesome task which is heavily dependent on empirical knowledge. The paper describes an effort that applies machine learning to build an expert system for debugging faults in structural analysis program input data. ID3 decision tree induction algorithm is employed to build the automatic learning mechanism. This method Oflkrs an attractive potential for knowledge acquisition in the civil engineering domain in which expertise plays a dominant role. This paper is novel in at least three aspects: (1) machine learning is employed to build expert systems automatically; (2) a novel impurity function is proposed for splitting the decision tree; (3) a formulated comparison is proposed for evaluating learning results.     

位移反分析的进化支持向量机研究

将支持向量机与遗传算法相结合，提出了一种用于位移反分析的进化支持向量机方法。这种方法基于试验设计和有限元计算获得学习样本和检验样本，用遗传算法搜索最优的支持向量机参数，用获得的最优模型进行学习，从而获得岩体的力学参数与位移之间的非线性映射关系，再用遗传算法从全局空间上搜索，进行岩体力学参数的识别。给出的两个算例结果是令人满意的。     

Machine learning based novelty detection using modal analysis

Among many structural assessment methods, the change of modal characteristics is considered a well‐accepted damage detection method. However, the presence of environmental or operational variations may pollute the baseline and prevent a dependable assessment of the change. In recent years, the use of machine learning algorithms gained interest within structural health community, especially due to their ability and success in the elimination of ambient uncertainty. This paper proposes an end‐to‐end architecture to detect damage reliably by employing machine learning algorithms. The proposed approach streamlines (a) collection of structural response data, (b) modal analysis using system identification, (c) learning model, and (d) novelty detection. The proposed system aims to extract latent features of accessible modal parameters such as natural frequencies and mode shapes measured at undamaged target structure under temperature uncertainty and to reconstruct a new representation of these features that is similar to the original using well‐established machine learning methods for damage detection. The deviation between measured and reconstructed parameters, also known as novelty index, is the essential information for detecting critical changes in the system. The approach is evaluated by analyzing the structural response data obtained from finite element models and experimental structures. For the machine learning component of the approach, both principal component analysis (PCA) and autoencoder (AE) are examined. While mode shapes are known to be a well‐researched damage indicator in the literature, to our best knowledge, this research is the first time that unsupervised machine learning is applied using PCA and AE to utilize mode shapes in addition to natural frequencies for effective damage detection. The detection performance of this pipeline is compared to a similar approach where its learning model does not utilize mode shapes. The results demonstrate that the effectiveness of the damage detection under temperature variability improves significantly when mode shapes are used in the training of learning algorithm. Especially for small damages, the proposed algorithm performs better in discriminating system changes.     

Machine Learning in Engineering Analysis and Design: An Integrated Fuzzy Neural Network Learning Model

Applying neural network computing to structural engineering problems has received increasing interest, with particular emphasis placed on a supervised neural network with the backpropagation (BP) learning algorithm. In this article, we present an integrated fuzzy neural network (IFN) learning model by integrating a newly developed unsupervised fuzzy neural network (UFN) reasoning model with a supervised learning model in structural engineering. The UFN reasoning model is developed on the basis of a single-layer laterally connected neural network with an unsupervised competing algorithm. The IFN learning model is compared with the BP learning algorithm as well as with a counterpropagation learning algorithm (CPN) using two engineering analysis and design examples from the recent literature. This comparison indicates not only a superior learning performance in solved instances but also a substantial decrease in computational time for the IFN learning model. In addition, the IFN learning model is applied to a complicated engineering design problem involving steel structures. The IFN learning model also demonstrates superior learning performance in a complicated structural design problem with a reasonable computational time.     

A new systematic firefly algorithm for forecasting the durability of reinforced recycled aggregate concrete

This study presents a new systematic algorithm to optimize the durability of reinforced recycled aggregate concrete. The proposed algorithm integrates machine learning with a new version of the firefly algorithm called chaotic based firefly algorithm (CFA) to evolve a rational and efficient predictive model. The CFA optimizer is augmented with chaotic maps and Lévy flight to improve the firefly performance in forecasting the chloride penetrability of strengthened recycled aggregate concrete (RAC). A comprehensive and credible database of distinctive chloride migration coefficient results is used to establish the developed algorithm. A dataset composite of nine effective parameters, including concrete components and fundamental characteristics of recycled aggregate (RA), is used as input to predict the migration coefficient of strengthened RAC as output. k-fold cross validation algorithm is utilized to validate the hybrid algorithm. Three numerical benchmark analyses are applied to prove the superiority and applicability of the CFA algorithm in predicting chloride penetrability. Results show that the developed CFA approach significantly outperforms the firefly algorithm on almost tested functions and demonstrates powerful prediction. In addition, the proposed strategy can be an active tool to recognize the contradictions in the experimental results and can be especially beneficial for assessing the chloride resistance of RAC.