共查询到20条相似文献，搜索用时 15 毫秒
1.
El-Sayed M. El-kenawy Abdelhameed Ibrahim Seyedali Mirjalili Yu-Dong Zhang Shaima Elnazer Rokaia M. Zaki 《计算机、材料和连续体（英文）》2022,71(3):4989-5003
Metamaterial Antenna is a subclass of antennas that makes use of metamaterial to improve performance. Metamaterial antennas can overcome the bandwidth constraint associated with tiny antennas. Machine learning is receiving a lot of interest in optimizing solutions in a variety of areas. Machine learning methods are already a significant component of ongoing research and are anticipated to play a critical role in today's technology. The accuracy of the forecast is mostly determined by the model used. The purpose of this article is to provide an optimal ensemble model for predicting the bandwidth and gain of the Metamaterial Antenna. Support Vector Machines (SVM), Random Forest, K-Neighbors Regressor, and Decision Tree Regressor were utilized as the basic models. The Adaptive Dynamic Polar Rose Guided Whale Optimization method, named AD-PRS-Guided WOA, was used to pick the optimal features from the datasets. The suggested model is compared to models based on five variables and to the average ensemble model. The findings indicate that the presented model using Random Forest results in a Root Mean Squared Error (RMSE) of (0.0102) for bandwidth and RMSE of (0.0891) for gain. This is superior to other models and can accurately predict antenna bandwidth and gain. 相似文献
2.
Digital signal processing of electroencephalography (EEG) data is now widely utilized in various applications, including motor imagery classification, seizure detection and prediction, emotion classification, mental task classification, drug impact identification and sleep state classification. With the increasing number of recorded EEG channels, it has become clear that effective channel selection algorithms are required for various applications. Guided Whale Optimization Method (Guided WOA), a suggested feature selection algorithm based on Stochastic Fractal Search (SFS) technique, evaluates the chosen subset of channels. This may be used to select the optimum EEG channels for use in Brain-Computer Interfaces (BCIs), the method for identifying essential and irrelevant characteristics in a dataset, and the complexity to be eliminated. This enables (SFS-Guided WOA) algorithm to choose the most appropriate EEG channels while assisting machine learning classification in its tasks and training the classifier with the dataset. The (SFS-Guided WOA) algorithm is superior in performance metrics, and statistical tests such as ANOVA and Wilcoxon rank-sum are used to demonstrate this. 相似文献
3.
支持向量机及其在机械故障诊断中的应用 总被引：4，自引：6，他引：4
支持向量机（SVM）是一种基于统计学习理论的新型机器学习方法,对小样本决策具有较好的学习推广性。对近年来支持向量机的研究进展及其在故障诊断中的应用做了简要介绍,讨论了支持向量机的特点和存在的问题,展望了其在机械故障诊断的研究前景。 相似文献
4.
支持向量机(SVM)是一种对小样本决策具有良好学习性能的机器学习方法。常规SVM算法是从二类分类问题推导得出的,针对于故障诊断这种典型的多类决策问题,研究了一种网格式支持向量机多类算法,每个类别和其他2至4个类别之间采用常规SVM二值分类器进行分类,所需二值分类器总数少,可扩展性强。把转轴上不同位置的裂纹当作不同的故障,运用网格式支持向量机进行转轴裂纹位置故障诊断,结果表明该算法具有计算量小、诊断速度快、故障识别率高、容易扩展等优点,适合于较大规模的多类别故障诊断应用。 相似文献
5.
The design of microstrip antennas is a complex and time-consuming process, especially the step of searching for the best design parameters. Meanwhile, the performance of microstrip antennas can be improved using metamaterial, which results in a new class of antennas called metamaterial antenna. Several parameters affect the radiation loss and quality factor of this class of antennas, such as the antenna size. Recently, the optimal values of the design parameters of metamaterial antennas can be predicted using machine learning, which presents a better alternative to simulation tools and trial-and-error processes. However, the prediction accuracy depends heavily on the quality of the machine learning model. In this paper, and benefiting from the current advances in deep learning, we propose a deep network architecture to predict the bandwidth of metamaterial antenna. Experimental results show that the proposed deep network could accurately predict the optimal values of the antenna bandwidth with a tiny value of mean-square error (MSE). In addition, the proposed model is compared with current competing approaches that are based on support vector machines, multi-layer perceptron, K-nearest neighbors, and ensemble models. The results show that the proposed model is better than the other approaches and can predict antenna bandwidth more accurately. 相似文献
6.
Fu‐Kwun Wang Berihun Bizuneh Xiao‐Bin Cheng 《Quality and Reliability Engineering International》2019,35(6):1634-1645
The statistical learning classification techniques have been successfully applied to statistical process control problems. In this paper, we proposed a one‐sided control chart based on support vector machines (SVMs) and differential evolution (DE) algorithm to monitor a process with multivariate quality characteristics. The SVM classifier provides a continuous distance from the boundary, and the DE algorithm is used to obtain the optimal parameters of the SVM model by minimizing mean absolute error (MAE). The average run length of the proposed chart is computed using the Monte Carlo simulation approach. Several simulated cases are conducted using a multivariate normal distribution with 10 and 20 dimensions and three different process shift scenarios. In addition, we consider two non‐normal distribution cases. The ARL performance of the proposed chart is better than the distance‐based SVM chart. A real example is used to illustrate the application of the proposed control chart. 相似文献
7.
Despite the advancement within the last decades in the field of smart grids, energy consumption forecasting utilizing the metrological features is still challenging. This paper proposes a genetic algorithm-based adaptive error curve learning ensemble (GA-ECLE) model. The proposed technique copes with the stochastic variations of improving energy consumption forecasting using a machine learning-based ensembled approach. A modified ensemble model based on a utilizing error of model as a feature is used to improve the forecast accuracy. This approach combines three models, namely CatBoost (CB), Gradient Boost (GB), and Multilayer Perceptron (MLP). The ensembled CB-GB-MLP model’s inner mechanism consists of generating a meta-data from Gradient Boosting and CatBoost models to compute the final predictions using the Multilayer Perceptron network. A genetic algorithm is used to obtain the optimal features to be used for the model. To prove the proposed model’s effectiveness, we have used a four-phase technique using Jeju island’s real energy consumption data. In the first phase, we have obtained the results by applying the CB-GB-MLP model. In the second phase, we have utilized a GA-ensembled model with optimal features. The third phase is for the comparison of the energy forecasting result with the proposed ECL-based model. The fourth stage is the final stage, where we have applied the GA-ECLE model. We obtained a mean absolute error of 3.05, and a root mean square error of 5.05. Extensive experimental results are provided, demonstrating the superiority of the proposed GA-ECLE model over traditional ensemble models. 相似文献
8.
Statistical process control charts have been successfully used to monitor process stability in various industries. The need to simultaneously monitor two or more quality characteristics has led to the prevalent adoption of multivariate control charts. However, out-of-control signals in multivariate control charts may be caused by one or more variables, or a set of variables. Therefore, effective quality control requires not only the rapid detection of process fluctuations, but also the correct identification of the variable(s) responsible for those changes. This study approaches the diagnosis of out-of-control signals as a classification task and proposes a support vector machine (SVM)-based ensemble classification model focused on variance shifts in multivariate processes. We address the issues of data diversity and ensemble method in constructing an ensemble model. Simulation results demonstrate the effectiveness of the proposed ensemble classification model in identifying the source of variance change. The proposed method clearly outperforms single classifiers as well as other comparable models including bagging and boosting. The results also reveal that the use of extracted features as input vectors for SVM provides better classification performance than the use of raw data. The proposed SVM-based ensemble classification system provides a reliable tool for the interpretation of out-of-control signals in multivariate process control. 相似文献
9.
目的 比较Arrhenius方程和机器学习方法在TC21 钛合金本构模型建立中的优劣,为TC21 钛合金在实际工程应用中的性能预测、优化设计和安全评估提供理论指导.方法 通过使用Gleeble-3500 热模拟机,获取了锻态TC21 钛合金在不同温度和应变速率下的真实应力应变数据.基于实验结果,分别采用Arrhenius方法和支持向量机方法建立了相应的本构模型.相较于基于热力学原理的Arrhenius本构方程,采用支持向量机方法的本构模型更为先进.该模型能够从有限的数据中深入挖掘材料性能与温度、应变速率之间的复杂非线性关系,从而更准确地预测TC21钛合金在不同条件下的力学性能.为了全面评估这2 种模型的预测准确性,计算了它们的模型相关系数和均方根误差.结果 研究结果表明,基于机器学习的本构模型在预测TC21 钛合金的应力应变行为方面展现出显著的优势.其相关系数高达 0.977 4,远高于 Arrhenius 模型的0.931 7.在评估预测精度的均方根误差上,机器学习方法也表现出色,仅为5.49,相较于Arrhenius模型的20.67显著降低.结论 利用机器学习方法建立的TC21 钛合金本构模型具有更高的精度和可靠性.在实际工程应用中,这将为钛合金的性能预测、优化设计和安全评估提供更为准确的科学依据. 相似文献
10.
El-Sayed M. El-kenawy Hattan F. Abutarboush Ali Wagdy Mohamed Abdelhameed Ibrahim 《计算机、材料和连续体（英文）》2021,69(3):2983-2995
Machine learning (ML) has taken the world by a tornado with its prevalent applications in automating ordinary tasks and using turbulent insights throughout scientific research and design strolls. ML is a massive area within artificial intelligence (AI) that focuses on obtaining valuable information out of data, explaining why ML has often been related to stats and data science. An advanced meta-heuristic optimization algorithm is proposed in this work for the optimization problem of antenna architecture design. The algorithm is designed, depending on the hybrid between the Sine Cosine Algorithm (SCA) and the Grey Wolf Optimizer (GWO), to train neural network-based Multilayer Perceptron (MLP). The proposed optimization algorithm is a practical, versatile, and trustworthy platform to recognize the design parameters in an optimal way for an endorsement double T-shaped monopole antenna. The proposed algorithm likewise shows a comparative and statistical analysis by different curves in addition to the ANOVA and T-Test. It offers the superiority and validation stability evaluation of the predicted results to verify the procedures’ accuracy. 相似文献
11.
基于Moore-Penrose逆矩阵的选择性集成 总被引：1，自引：0，他引：1
本文提出了一种基于Moore-Penrose逆矩阵的新型选择性集成学习算法.先独立训练出一批个体学习器并为每个学习器指定一个初始权值,然后应用基于Moore-Penrose逆矩阵的算法对这些权值进行优化,最后选择权值较大的个体学习器进行最终集成.本文提出的选择性集成学习算法方法简单、易于实现,执行效率高.对8个真实数据集的实验表明,该集成学习算法相对于一般的集成学习算法,可以采用更少的学习器而获得更高的泛化能力. 相似文献
12.
Amin Bemani Alireza Baghban Shahaboddin Shamshirband Amir Mosavi Peter Csiba Annamaria R. Varkonyi-Koczy 《计算机、材料和连续体（英文）》2020,63(3):1175-1204
In the present work, a novel machine learning computational investigation is carried out to accurately predict the solubility of different acids in supercritical carbon dioxide. Four different machine learning algorithms of radial basis function, multi-layer perceptron (MLP), artificial neural networks (ANN), least squares support vector machine (LSSVM) and adaptive neuro-fuzzy inference system (ANFIS) are used to model the solubility of different acids in carbon dioxide based on the temperature, pressure, hydrogen number, carbon number, molecular weight, and the dissociation constant of acid. To evaluate the proposed models, different graphical and statistical analyses, along with novel sensitivity analysis, are carried out. The present study proposes an efficient tool for acid solubility estimation in supercritical carbon dioxide, which can be highly beneficial for engineers and chemists to predict operational conditions in industries. 相似文献
13.
Clinical Study and automatic diagnosis of electrocardiogram (ECG) data always remain a challenge in diagnosing cardiovascular activities. The analysis of ECG data relies on various factors like morphological features, classification techniques, methods or models used to diagnose and its performance improvement. Another crucial factor in the methodology is how to train the model for each patient. Existing approaches use standard training model which faces challenges when training data has variation due to individual patient characteristics resulting in a lower detection accuracy. This paper proposes an adaptive approach to identify performance improvement in building a training model that analyze global training methodology against an individual training methodology and identifying a gap between them. We provide our investigation and comparative study on these methods and model with standard classification techniques with basic morphological features and Heart Rate Variability (HRV) that may aid real time application. This approach helps in analyzing and evaluating the performance of different techniques and can suggests adoption of a best model identification with efficient technique and efficient attribute set for real-time systems.
相似文献14.
为了进一步提高SVM集成的泛化能力,提出了基于Choquet模糊积分的SVMs集成方法,综合考虑各个子SVM输出重要性,避免了现有SVM集成方法中忽略次要信息的问题.应用该方法,以高校的区域经济贡献度为例进行仿真试验,结果表明基于Choquet模糊积分的SVMs集成方法较基于Sugeno模糊积分SVMs集成方法和基于投票策略的SVMs集成方法具有更高的准确性.该方法是可行、有效的,具有一定的推广价值. 相似文献
15.
This paper investigates performance improvement via the incorporation of the support vector machine (SVM) into the vector tracking loop (VTL) for the Global Positioning System (GPS) in limited satellite visibility. Unlike the traditional scalar tracking loop (STL), the tracking and navigation modules in the VTL are not independent anymore since the user’s position can be determined by using the information from other satellites and can be predicted on the basis of the states of the user. The method proposed in this paper makes use of the SVM to bridge the GPS signal and prevent the error growth due to signal outage. Similar to the neural network, the SVM is motivated by its ability to approximate an unknown nonlinear input-output mapping through supervised training. The SVM is employed for predicting adequate numerical control oscillator (NCO) inputs, i.e., providing better prediction of residuals for the Doppler frequency and code phase in order to maintain regular operation of the navigation system. When the navigation processing is in good condition, the SVM is at the training stage, and the output information from the discriminator and navigation filter is adopted as the inputs. Other machine learning (ML) algorithms such as the radial basis function neural network (RBFNN) and the Adaptive Network-Based Fuzzy Inference System (ANFIS) are employed for comparison. Performance evaluation for the SVM assisted architecture as compared to the RBFNN- and ANFIS-assisted methods and the un-assisted VTL will be carried out and the performance evaluation during GPS signal outage will be presented. The proposed design is very useful for navigation during the environment of limited satellite visibility to effectively overcome the problem in the environment of GPS outage. 相似文献
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17.
In multivariate statistical process control (MSPC), most multivariate control charts can effectively monitor anomalies based on overall statistic, however, they cannot provide guidelines to classify the source(s) of out-of-control signals. Classifying the source(s) of process mean shifts is critical for quality control in multivariate manufacturing process since the immediate identification of them can greatly help quality engineer to narrow down the set of possible root causes and take corrective actions. This study presents an improved particle swarm optimisation with simulated annealing-based selective multiclass support vector machines ensemble (PS-SVME) approach, in which some selective multiclass SVMs are jointly used for classifying the source(s) of process mean shifts in multivariate control charts. The performance of the proposed PS-SVME approach is evaluated by computing its classification accuracy. Simulation experiments are conducted and a real application is illustrated to validate the effectiveness of the developed approach. The analysis results indicate that the developed PS-SVME approach can perform effectively for classifying the source(s) of process mean shifts. 相似文献
18.
Visvasam Devadoss Ambeth Kumar Chetan Swarup Indhumathi Murugan Abhishek Kumar Kamred Udham Singh Teekam Singh Ramu Dubey 《计算机、材料和连续体（英文）》2022,71(1):855-869
Cardio Vascular disease (CVD), involving the heart and blood vessels is one of the most leading causes of death throughout the world. There are several risk factors for causing heart diseases like sedentary lifestyle, unhealthy diet, obesity, diabetes, hypertension, smoking and consumption of alcohol, stress, hereditary factory etc. Predicting cardiovascular disease and improving and treating the risk factors at an early stage are of paramount importance to save the precious life of a human being. At present, the highly stressful life with bad lifestyle activities causes heart disease at a very young age. The main aim of this research is to predict the premature heart disease based on machine learning algorithms. This paper deals with a novel approach using the machine learning algorithm for predicting the cardiovascular disease at the premature stage itself. Support Vector Machine (SVM) is used for segregating the CVD patients based on their symptoms and medical observation. The experimentation results by using the proposed method will facilitate the medical practitioners to provide suitable treatment for the patients on time. A sophisticated model has been developed with the current approach to examine the various stages of CVD and the performance metrics used have given effective and fruitful results as compared to other machine learning techniques. 相似文献
19.
With the development of artificial intelligence, it is urgent to empower traditional electronics with the ability to “think,” to “analyze,” and to “advise.” Here, a new product concept namely triboelectric nanogenerator (TENG) based smart electronics via the automatic machine learning data analysis algorithm is proposed. In this work, a simple water processing technique is used to fabricate porous polydimethylsiloxane, together with the weaving copper mesh, forming a high sensitivity flexible TENG. The as‐prepared TENG presents high sensitivity for the voice signal and handwriting signal detection with ≈0.2 V amplitude in the common talking and writing condition. Three words' pronunciation are recorded and the ensemble method is used as the machine learning model for the voice signal recognition with a recognition accuracy of 93.3%. To further demonstrate the possibility of applying machine learning algorithm for automatic analysis and recognition, larger database is analyzed. Twenty‐six letters' handwriting signals with total 520 samples are collected and a letter fingerprint library is established for further analysis. Hierarchical clustering and similarity matrix are used to study the intrinsic relationship between letters. “Medium Gaussian support vector machine” is used as machine learning model for the 26‐letter fingerprint identification with recognition accuracy of 93.5%. 相似文献
20.
为了准确预测小样本条件下露天矿山岩石的爆破块度,并得到小样本条件下预测露天矿山爆破块度的有效方法,借助最小二乘支持向量机工具(LS-SVMlab)构建基于最小二乘支持向量机回归(LS-SVR)预测模型并合理优化模型参数。分别使用15组露天矿山爆破数据和35组爆破数据作为小样本容量和正常样本容量,对模型的预测精度进行检验。结果表明:两种样本容量下LS-SVR预测模型的预测结果精度都比同样本容量下人工神经网络(ANN)回归预测的结果精度更高,说明所提出的LS-SVR模型适用于预测露天矿山爆破块度,并且在小样本条件下更具优势。 相似文献