Identification of Crop Diseases Based on Improved Genetic Algorithm and Extreme Learning Machine

As an indispensable task in crop protection,the detection of crop diseases directly impacts the income of farmers.To address the problems of low crop-disease identification precision and detection abilities,a new method of detection is proposed based on improved genetic algorithm and extreme learning machine.Taking five different typical diseases with common crops as the objects,this method first preprocesses the images of crops and selects the optimal features for fusion.Then,it builds a model of crop disease identification for extreme learning machine,introduces the hill-climbing algorithm to improve the traditional genetic algorithm,optimizes the initial weights and thresholds of the machine,and acquires the approximately optimal solution.And finally,a data set of crop diseases is used for verification,demonstrating that,compared with several other common machine learning methods,this method can effectively improve the crop-disease identification precision and detection abilities and provide a basis for the identification of other crop diseases.

Towards Improving the Intrusion Detection through ELM (Extreme Learning Machine)

An IDS(intrusion detection system)provides a foremost front line mechanism to guard networks,systems,data,and information.That’s why intrusion detection has grown as an active study area and provides significant contribution to cyber-security techniques.Multiple techniques have been in use but major concern in their implementation is variation in their detection performance.The performance of IDS lies in the accurate detection of attacks,and this accuracy can be raised by improving the recognition rate and significant reduction in the false alarms rate.To overcome this problem many researchers have used different machine learning techniques.These techniques have limitations and do not efficiently perform on huge and complex data about systems and networks.This work focused on ELM(Extreme Learning Machine)technique due to its good capabilities in classification problems and dealing with huge data.The ELM has different activation functions,but the problem is to find out which function is more suitable and performs well in IDS.This work investigates this problem.Here,Well-known activation functions like:sine,sigmoid and radial basis are explored,investigated and applied to measure their performance on the GA(Genetic Algorithm)features subset and with full features set.The NSL-KDD dataset is used as a benchmark.The empirical results are analyzed,addressed and compared among different activation functions of the ELM.The results show that the radial basis and sine functions perform better on GA feature set than the full feature set while the performance of the sigmoid function is almost equal on both features sets.So,the proposal of GA based feature selection reduced 21 features out of 41 that brought up to 98%accuracy and enhanced overall efficiency of extreme learning machine in intrusion detection.

Extreme learning with chemical reaction optimization for stock volatility prediction

Extreme learning machine(ELM)allows for fast learning and better generalization performance than conventional gradient-based learning.However,the possible inclusion of non-optimal weight and bias due to random selection and the need for more hidden neurons adversely influence network usability.Further,choosing the optimal number of hidden nodes for a network usually requires intensive human intervention,which may lead to an ill-conditioned situation.In this context,chemical reaction optimization(CRO)is a meta-heuristic paradigm with increased success in a large number of application areas.It is characterized by faster convergence capability and requires fewer tunable parameters.This study develops a learning framework combining the advantages of ELM and CRO,called extreme learning with chemical reaction optimization(ELCRO).ELCRO simultaneously optimizes the weight and bias vector and number of hidden neurons of a single layer feed-forward neural network without compromising prediction accuracy.We evaluate its performance by predicting the daily volatility and closing prices of BSE indices.Additionally,its performance is compared with three other similarly developed models—ELM based on particle swarm optimization,genetic algorithm,and gradient descent—and find the performance of the proposed algorithm superior.Wilcoxon signed-rank and Diebold–Mariano tests are then conducted to verify the statistical significance of the proposed model.Hence,this model can be used as a promising tool for financial forecasting.

Selective Ensemble Extreme Learning Machine Modeling of Effluent Quality in Wastewater Treatment Plants

Real-time and reliable measurements of the effluent quality are essential to improve operating efficiency and reduce energy consumption for the wastewater treatment process.Due to the low accuracy and unstable performance of the traditional effluent quality measurements,we propose a selective ensemble extreme learning machine modeling method to enhance the effluent quality predictions.Extreme learning machine algorithm is inserted into a selective ensemble frame as the component model since it runs much faster and provides better generalization performance than other popular learning algorithms.Ensemble extreme learning machine models overcome variations in different trials of simulations for single model.Selective ensemble based on genetic algorithm is used to further exclude some bad components from all the available ensembles in order to reduce the computation complexity and improve the generalization performance.The proposed method is verified with the data from an industrial wastewater treatment plant,located in Shenyang,China.Experimental results show that the proposed method has relatively stronger generalization and higher accuracy than partial least square,neural network partial least square,single extreme learning machine and ensemble extreme learning machine model.

MRMR-SA-EGA-ELM的叶绿素a浓度预测模型研究

为提高叶绿素a浓度的预测精度,以南太湖区域-湖州市新塘港2020年5月至11月份的水质监测数据为原始样本数据,使用最大相关最小冗余算法(MRMR)从原始样本数据中选取效果更优的特征值,作为预测模型的输入数据,将精英遗传算法(EGA)与模拟退火算法(SA)组合优化极限学习机(ELM)网络的初始参数,最终构建MRMR-SA-EGA-ELM叶绿素a浓度预测模型。实验结果表明,MRMR-SA-EGA-ELM模型预测叶绿素a浓度的平均绝对误差(MAE)、均方误差(MSE)、决定系数(R^(2))分别为1.009、1.607、0.903,而ELM模型预测结果的MAE、MSE、R^(2)分别为2.078、8.249、0.562,MRMR-SA-EGA-ELM模型的效果得到显著提升,可实现对叶绿素a浓度的准确预测。

基于GA-ELM的锂电池SOC估计及主动均衡

锂电池的状态估计和主动均衡是提高电池性能和延长使用寿命的关键技术,针对参数模型的荷电状态(State of charge,SOC)估计方法忽略电动汽车实际工况而导致的估计偏差较大的问题,提出一种基于遗传算法的极限学习机(GA-ELM)神经网络算法来估计电池的荷电状态SOC,通过遗传算法优化了ELM的参数,提高估计精度和泛化能力,并在UDDS工况数据下进行训练与测试。同时采用双向Buck-Boost均衡拓扑结构,该拓扑结构能够快速实现电池间的能量传递,同时又降低了传递路径的复杂性。通过遗传算法的极限学习机估计出的SOC作为均衡变量,利用Matlab/Simulink仿真平台进行试验。结果表明,提出的GA-ELM神经网络平均误差为0.15%,而传统的ELM神经网络平均误差为0.56%,因此提出的神经网络能够更精确地估计SOC;同时电池组之间能够快速完成能量均衡,证明了所提方案的可行性。

基于K-means聚类和极限学习机组合算法的短期光伏功率预测

考虑光伏功率的预测精度强依赖于天气模态和气候条件等因素影响,提出了基于极限学习机组合算法的短期光伏功率预测方法。首先,基于K-means聚类算法进行天气分型,分为4个季节下晴天、多云天气、阴雨天气共12组不同天气类别。其次,针对天气分型结果,基于极限学习机ELM、遗传算法改进的极限学习机GA-ELM、鸟群算法改进的极限学习机BSA-ELM3种算法构建光伏功率预测模型。最后,以某光伏电站数据进行所提模型验证。预测结果表明,BSA-ELM预测精度最高,12种天气预测精度达到90%左右,各季节中预测精度最高的天气类型均为晴天,多云天气精度高于阴雨天气精度,可为含高比例光伏并网的新型电力系统安全稳定运行提供有效数据支撑。

基于核极限学习机的下肢关节力矩预测方法

针对极限学习机(extreme learning machine,ELM)预测下肢关节力矩时,随机初始化输入权重和偏置影响模型准确度问题,提出一种基于核极限学习机(kernel based extreme learning machine,KELM)的下肢康复机器人关节力矩预测方法。该方法将高斯核函数与ELM相融合,并采用遗传算法(genetic algorithm,GA)与粒子群优化(particle swarm optimization,PSO)结合的基因粒子群GAPSO对KELM的参数进行优化。首先,采集1位在跑步机上以0.4、0.5、0.6、0.7和0.8 m/s等5个不同速度行走的右下肢偏瘫患者运动数据并对数据进行预处理;其次,通过GAPSO对KELM进行优化,获得最优正则化系数C和核函数宽度参数S,将输出关节力矩与反向生物力学分析计算的关节作比较;最后,利用均方根误差(root mean square error,RMSE)和相关系数P来评价算法优越性。实验结果表明,基于GAPSO优化后的KELM(GAPSO-KELM)算法相对于PSO-KELM算法、KELM算法和ELM算法的平均最大均方根误差分别降低14%、18%、28%,且P除了0.8 m/s右侧踝关节内外翻是0.79外,其余P最小是0.84,GAPSO-KELM算法进一步提高预测精度,使其为康复治疗提供更有效的算法支持。

基于改进遗传算法的多源数据继电保护定值优化策略

针对传统继电保护策略存在准确度差且效率低的问题,文中基于多源数据提出了一种性能更优的继电保护策略。对于多源数据一致性差、分析困难的特点,采用经验模态分解对数据进行预处理,以获得不同梯度的时域以及频域数据。同时为了增强传统遗传算法的分类性能,还引入了极限学习机对输入数据进行分类,并使用遗传算法对极限学习机的参数加以优化。所设计的模型可以对配电网发生的故障进行判定,并引导继电保护设施做出正确响应。实验结果表明,相较于对比算法,该算法具有更高的准确性及更优的效率,且优化后继电保护装置的判断综合准确率可达89%。

Quantitative analysis of steel and iron by laser-induced breakdown spectroscopy using GA-KELM

According to the multiple researches in the last couple of years, laser-induced breakdown spectroscopy(LIBS) has shown a great potential for rapid analysis in steel industry.Nevertheless, the accuracy and precision may be limited by complex matrix effect and selfabsorption effect of LIBS seriously. A novel multivariate calibration method based on genetic algorithm-kernel extreme learning machine(GA-KELM) is proposed for quantitative analysis of multiple elements(Si, Mn, Cr, Ni, V, Ti, Cu, Mo) in forty-seven certified steel and iron samples.First, the standardized peak intensities of selected spectra lines are used as the input of model.Then, the genetic algorithm is adopted to optimize the model parameters due to its obvious capability in finding the global optimum solution. Based on these two steps above, the kernel method is introduced to create kernel matrix which is used to replace the hidden layer's output matrix. Finally, the least square is applied to calculate the model's output weight. In order to verify the predictive capability of the GA-KELM model, the R-square factor(R^2), Root-meansquare Errors of Calibration(RMSEC), Root-mean-square Errors of Prediction(RMSEP) of GAKELM model are compared with the traditional PLS algorithm, respectively. The results confirm that GA-KELM can reduce the interference from matrix effect and self-absorption effect and is suitable for multi-elements calibration of LIBS.

基于GA-KELM的光伏短期出力预测研究

提出一种基于遗传算法的核函数极限学习机模型来预测光伏的短期出力,采用遗传算法GA优化核函数极限学习机K-ELM模型参数。该方法首先利用交叉验证算法训练均方差作为目标函数,再结合遗传算法,对惩罚因子c和高斯核函数的宽度参数σ进行寻优,从而提高了预测精度并加快了收敛速度。在对某地光伏发电量进行统计和研究的基础上,将提出的预测算法应用中实际数据中并且与传统GA-BP模型预测比较。实例验证表明：GA-KELM模型能够更准确、高效的对光伏的短期出力进行预测,解决了传统GA-BP神经网络算法存在执行速度慢且易陷入局部最优的缺点,具有更广阔的应用价值和研究空间。

中国省际碳排放预测及达峰情景模拟研究

中国不同省级的碳排放量呈现明显差异。文章采用STIRPAT模型分析得到碳排放影响因素,运用遗传算法优化的极限学习机模型和中国30个省(自治区、直辖市)1997—2020年的面板数据,对不同发展情景下中国30个省(自治区、直辖市)未来20年的碳排放量进行了预测分析,并将预测结果和误差指标与ELM、BP、GWO-SVM模型进行对比。同时,文章以行政区域为单位划分东北、华北等7个区域进行碳达峰、碳减排能力分析。研究结果显示:使用遗传算法改进的极限学习机模型可以克服ELM模型容易陷入局部最优解的缺点,获得更高的预测精度;在绿色发展情景下中国7个区域均能在2030前实现碳达峰。

基于UFELM的DHP在水泥回转窑系统中的应用

水泥是建筑等行业必不可少的原材料之一,回转窑是水泥烧成的核心设备。水泥回转窑烧成系统是典型的多扰动、强耦合、大时滞的非线性系统,其导致回转窑温度难以控制,为克服这一难题,建立了基于遗传算法改进BP网络水泥烧成系统模型。在此基础上,利用全反馈极限学习机的双启发式动态规划(UFELM-DHP)算法设计回转窑烧成系统优化控制器。仿真结果表明,此算法可以实现回转窑烧成系统的稳定控制,且具有更快的控制速率。

微小通道内过冷流动沸腾阻力特性实验及预测研究

实验探究了微小圆管内(内径1 mm)过冷水流动沸腾的阻力特性,参数范围:热通量4.0~5.6 MW/m^(2),压力3.0~5.0 MPa,质量流速2000~4200 kg/(m^(2)‧s),进口热力学干度-0.50~-0.10。获取了质量流速、压力、热通量等参数对过冷沸腾阻力的影响,并重点关注其预测方法。将测试数据与典型阻力关联式对比,结果表明,由于高热流、微通道等特殊因素,导致大部分阻力关联式的预测精度不够理想。为更准确预测高热流过冷沸腾阻力,基于LeakyReLU函数,建立了遗传算法优化的极限学习机模型(GA-ELM),其预测精度优于传统关联式(平均绝对误差为2.0%),且泛化性良好。研究工作可为微小尺度流动换热系统设计优化提供支撑。

基于小波核极限学习机的隧道围岩变形预报

隧道围岩变形预报可视为一时间序列问题处理,由于围岩变形序列的非线性复杂程度较高,针对该问题,提出一种适用于隧道围岩变形预报的遗传小波核极限学习机模型(GA-ELM-k)。联用核方法和极限学习机算法,保证了极限学习机算法的稳健性,又融合了小波方法所具有的处理非平稳信号的优势。针对小波核函数参数的确定,将遗传算法用于参数寻优。通过黄榜岭隧道围岩变形预报表明,提出的新模型预报准确度优于ARIMA模型和灰色新陈代谢模型,可作为实际工程的一种参考方法。

基于改进VMD去噪和优化ELM方法的变压器早期故障诊断

变压器内部漏磁场是判断变压器绕组早期故障的重要依据。实际运行中噪声会对漏磁场检测产生干扰从而影响对故障状态的判断。为此,首先使用遗传算法以样本熵作为适应度函数来优化变分模态分解(VMD)参数,然后将VMD分解后的相关模态使用小波阈值法去除残余噪声;其次,选择并提取降噪漏磁场信号的特征向量,将特征向量输入到改进极限学习机(ELM)中进行训练和分类,实现变压器绕组的早期故障诊断。仿真及动模实验表明:该方法去噪效果良好,能有效地还原原漏磁场信号,最终能实现变压器绕组早期故障的准确识别。

基于温度和SOC的锂离子电池特征提取及SOH估计

为了解决电池日常使用过程中数据量获取不足和健康因子提取难的问题,通过分析不同温度下锂离子电池的荷电状态(SOC)与充电电压的变化曲线,提出基于温度和SOC的锂离子电池健康因子提取及健康状态(SOH)在线估计的方法.在电池的实际充电过程中,根据环境温度差异选取电压和电流作为健康因子.利用遗传-爬山算法优化极限学习机的网络参数,建立健康因子和SOH的映射关系,实现SOH在线估计.使用9组NASA电池老化数据进行验证,结果表明,本文方法具有估计精度高、环境温度适应性强的优点.

融合多特征参数的电力工程数据应用智能算法设计研究

为了提升电力工程数据分析的效率及准确度,文中开展了融合多特征参数的电力工程数据应用智能算法设计研究。在考虑多种电力工程特征参数的基础上,建立了基于线性判别分析与遗传算法优化极限学习机的电力工程数据分析模型。其通过对原始参数进行线性判别分析进而得到主要参数,不仅消除了原始参数的相关性,还降低了参数的维度。同时采用遗传算法优化了极限学习机的输入权值与阈值,再对电力工程数据的分析模型加以训练。仿真分析结果表明,所提模型在电力工程数据分析上的计算速度快且准确度较高,可以辅助电力工程施工进行决策、及时落实管控措施,避免事后评估所带来的损失。

基于改进极限学习机的颜色校正模型

为了改善极限学习机随机初始化输入权重和隐藏层节点偏置,导致模型性能不稳定的问题,提出一种自适应模拟退火遗传算法优化的极限学习机颜色校正模型。模型采用遗传算法对极限学习机的输入权重和隐藏层偏置实施全局优化搜索,结合自适应机制和模拟退火算法优化遗传算法中的选择、交叉和变异操作,以改善遗传算法中“早熟”和后期收敛速度慢的问题。利用Pantone色卡数据集测试,实验结果表明,使用所提方法进行校正,色差平均值、最大值、标准差和大于3 NBS的占比分别降低为校正前的14.40%、37.05%、35.41%和11.98%,优于其他相关方法,并且,提出的颜色校正模型的鲁棒性较好。

基于遗传算法优化极限学习机的投标人画像评价研究

采用遗传算法优化极限学习机的连接权值和阈值,得到GA-ELM算法,同时选择资质信息、投标行为、技术实力、信用评价、履约表现为投标人画像评价指标,搭建用于投标人画像评价的GA-ELM模型,并将该模型应用于电网供应商画像评价中。结果表明,所搭建的GA-ELM评价模型在性能上明显优于PSO-ELM模型和ELM模型,这对投标人画像评价具有一定的实际参考价值。