Extreme learning machine (ELM) is a feedforward neural network-based machine learning method that has the benefits of short training times, strong generalization capabilities, and will not fall into local minima. Howe...Extreme learning machine (ELM) is a feedforward neural network-based machine learning method that has the benefits of short training times, strong generalization capabilities, and will not fall into local minima. However, due to the traditional ELM shallow architecture, it requires a large number of hidden nodes when dealing with high-dimensional data sets to ensure its classification performance. The other aspect, it is easy to degrade the classification performance in the face of noise interference from noisy data. To improve the above problem, this paper proposes a double pseudo-inverse extreme learning machine (DPELM) based on Sparse Denoising AutoEncoder (SDAE) namely, SDAE-DPELM. The algorithm can directly determine the input weight and output weight of the network by using the pseudo-inverse method. As a result, the algorithm only requires a few hidden layer nodes to produce superior classification results when classifying data. And its combination with SDAE can effectively improve the classification performance and noise resistance. Extensive numerical experiments show that the algorithm has high classification accuracy and good robustness when dealing with high-dimensional noisy data and high-dimensional noiseless data. Furthermore, applying such an algorithm to Miao character recognition substantiates its excellent performance, which further illustrates the practicability of the algorithm.展开更多
To enhance the accuracy and efficiency of bridge damage identification,a novel data-driven damage identification method was proposed.First,convolutional autoencoder(CAE)was used to extract key features from the accele...To enhance the accuracy and efficiency of bridge damage identification,a novel data-driven damage identification method was proposed.First,convolutional autoencoder(CAE)was used to extract key features from the acceleration signal of the bridge structure through data reconstruction.The extreme gradient boosting tree(XGBoost)was then used to perform analysis on the feature data to achieve damage detection with high accuracy and high performance.The proposed method was applied in a numerical simulation study on a three-span continuous girder and further validated experimentally on a scaled model of a cable-stayed bridge.The numerical simulation results show that the identification errors remain within 2.9%for six single-damage cases and within 3.1%for four double-damage cases.The experimental validation results demonstrate that when the tension in a single cable of the cable-stayed bridge decreases by 20%,the method accurately identifies damage at different cable locations using only sensors installed on the main girder,achieving identification accuracies above 95.8%in all cases.The proposed method shows high identification accuracy and generalization ability across various damage scenarios.展开更多
在实际应用中,数据点中包含的冗余特征和异常值(噪声)严重影响了聚类中更显著的特征的发现,大大降低了聚类性能。本文提出了一种基于ELM-AE(extreme learning machine as autoencoder)特征表示的谱聚类算法(spectral clustering via ext...在实际应用中,数据点中包含的冗余特征和异常值(噪声)严重影响了聚类中更显著的特征的发现,大大降低了聚类性能。本文提出了一种基于ELM-AE(extreme learning machine as autoencoder)特征表示的谱聚类算法(spectral clustering via extreme learning machine as autoencoder,SC-ELM-AE)。ELM-AE通过奇异值分解学习源数据主要特征表示,使用输出权值实现从特征空间到原输入数据的重构;再将该特征表示空间作为输入进行谱聚类。实验表明,在5个UCI数据集验证中,SC-ELM-AE算法性能优于传统的K-Means、谱聚类等现有算法,特别是在复杂高维数据集PEMS-SF和TDT2_10上,聚类平均精确度均提高30%以上。展开更多
Software defect prediction plays an important role in software quality assurance.However,the performance of the prediction model is susceptible to the irrelevant and redundant features.In addition,previous studies mos...Software defect prediction plays an important role in software quality assurance.However,the performance of the prediction model is susceptible to the irrelevant and redundant features.In addition,previous studies mostly regard software defect prediction as a single objective optimization problem,and multi-objective software defect prediction has not been thoroughly investigated.For the above two reasons,we propose the following solutions in this paper:(1)we leverage an advanced deep neural network-Stacked Contractive AutoEncoder(SCAE)to extract the robust deep semantic features from the original defect features,which has stronger discrimination capacity for different classes(defective or non-defective).(2)we propose a novel multi-objective defect prediction model named SMONGE that utilizes the Multi-Objective NSGAII algorithm to optimize the advanced neural network-Extreme learning machine(ELM)based on state-of-the-art Pareto optimal solutions according to the features extracted by SCAE.We mainly consider two objectives.One objective is to maximize the performance of ELM,which refers to the benefit of the SMONGE model.Another objective is to minimize the output weight norm of ELM,which is related to the cost of the SMONGE model.We compare the SCAE with six state-of-the-art feature extraction methods and compare the SMONGE model with multiple baseline models that contain four classic defect predictors and the MONGE model without SCAE across 20 open source software projects.The experimental results verify that the superiority of SCAE and SMONGE on seven evaluation metrics.展开更多
文摘Extreme learning machine (ELM) is a feedforward neural network-based machine learning method that has the benefits of short training times, strong generalization capabilities, and will not fall into local minima. However, due to the traditional ELM shallow architecture, it requires a large number of hidden nodes when dealing with high-dimensional data sets to ensure its classification performance. The other aspect, it is easy to degrade the classification performance in the face of noise interference from noisy data. To improve the above problem, this paper proposes a double pseudo-inverse extreme learning machine (DPELM) based on Sparse Denoising AutoEncoder (SDAE) namely, SDAE-DPELM. The algorithm can directly determine the input weight and output weight of the network by using the pseudo-inverse method. As a result, the algorithm only requires a few hidden layer nodes to produce superior classification results when classifying data. And its combination with SDAE can effectively improve the classification performance and noise resistance. Extensive numerical experiments show that the algorithm has high classification accuracy and good robustness when dealing with high-dimensional noisy data and high-dimensional noiseless data. Furthermore, applying such an algorithm to Miao character recognition substantiates its excellent performance, which further illustrates the practicability of the algorithm.
基金The National Natural Science Foundation of China(No.52361165658,52378318,52078459).
文摘To enhance the accuracy and efficiency of bridge damage identification,a novel data-driven damage identification method was proposed.First,convolutional autoencoder(CAE)was used to extract key features from the acceleration signal of the bridge structure through data reconstruction.The extreme gradient boosting tree(XGBoost)was then used to perform analysis on the feature data to achieve damage detection with high accuracy and high performance.The proposed method was applied in a numerical simulation study on a three-span continuous girder and further validated experimentally on a scaled model of a cable-stayed bridge.The numerical simulation results show that the identification errors remain within 2.9%for six single-damage cases and within 3.1%for four double-damage cases.The experimental validation results demonstrate that when the tension in a single cable of the cable-stayed bridge decreases by 20%,the method accurately identifies damage at different cable locations using only sensors installed on the main girder,achieving identification accuracies above 95.8%in all cases.The proposed method shows high identification accuracy and generalization ability across various damage scenarios.
文摘在实际应用中,数据点中包含的冗余特征和异常值(噪声)严重影响了聚类中更显著的特征的发现,大大降低了聚类性能。本文提出了一种基于ELM-AE(extreme learning machine as autoencoder)特征表示的谱聚类算法(spectral clustering via extreme learning machine as autoencoder,SC-ELM-AE)。ELM-AE通过奇异值分解学习源数据主要特征表示,使用输出权值实现从特征空间到原输入数据的重构;再将该特征表示空间作为输入进行谱聚类。实验表明,在5个UCI数据集验证中,SC-ELM-AE算法性能优于传统的K-Means、谱聚类等现有算法,特别是在复杂高维数据集PEMS-SF和TDT2_10上,聚类平均精确度均提高30%以上。
基金This work is supported in part by the National Science Foundation of China(Grant Nos.61672392,61373038)in part by the National Key Research and Development Program of China(Grant No.2016YFC1202204).
文摘Software defect prediction plays an important role in software quality assurance.However,the performance of the prediction model is susceptible to the irrelevant and redundant features.In addition,previous studies mostly regard software defect prediction as a single objective optimization problem,and multi-objective software defect prediction has not been thoroughly investigated.For the above two reasons,we propose the following solutions in this paper:(1)we leverage an advanced deep neural network-Stacked Contractive AutoEncoder(SCAE)to extract the robust deep semantic features from the original defect features,which has stronger discrimination capacity for different classes(defective or non-defective).(2)we propose a novel multi-objective defect prediction model named SMONGE that utilizes the Multi-Objective NSGAII algorithm to optimize the advanced neural network-Extreme learning machine(ELM)based on state-of-the-art Pareto optimal solutions according to the features extracted by SCAE.We mainly consider two objectives.One objective is to maximize the performance of ELM,which refers to the benefit of the SMONGE model.Another objective is to minimize the output weight norm of ELM,which is related to the cost of the SMONGE model.We compare the SCAE with six state-of-the-art feature extraction methods and compare the SMONGE model with multiple baseline models that contain four classic defect predictors and the MONGE model without SCAE across 20 open source software projects.The experimental results verify that the superiority of SCAE and SMONGE on seven evaluation metrics.
