Research on Optimization Methods of ELM Classification Algorithm for Hyperspectral Remote Sensing Images

Huang, Fang; Lu, Jun; Jian, Tao; Li, Li; Tan, Xicheng; Liu, Peng

doi:10.1109/access.2019.2932909

Cited by 23 publications

(22 citation statements)

References 24 publications

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“…To verify the classification effect of the method used in this article on EEG, the comparison algorithm has support vector machine (SVM) [32], reference [33], TSK [23], and ELM [34]. e experimental data are the data collected in the way shown in Section 3.2.…”

Section: Experimental Backgroundmentioning

confidence: 99%

A Novel Human-Machine Collaboration Model of an Ankle Joint Rehabilitation Robot Driven by EEG Signals

Shi

Yang

Zhang

2021

Mathematical Problems in Engineering

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With the emergence of the phenomenon of social aging, the elderly have frequent physical movement disorders. In particular, the movement disorder of the ankle joint seriously affects the daily life of the elderly. Rehabilitation robots are of great significance for improving the efficiency of rehabilitation, ensuring the quality of rehabilitation, and reducing the labor intensity of workers. As an auxiliary treatment tool, rehabilitation robots should have rich and effective motion modes. The exercise mode should be adaptable for patients with different conditions and different recovery periods. To improve the accuracy of human-computer interaction of ankle joint rehabilitation robots (AJRR), this study proposes a man-machine collaboration model of an EEG-driven AJRR. The model mainly expands from two levels (1) to establish the connection between EEG and intention so as to identify the intention. In the recognition process, first feature extraction is given on the preprocessed EEG. Convolutional neural network (CNN) is selected to extract the deep features of the EEG signal, and support vector machine (SVM) is used for classifying the deep features, thereby realizing intent recognition. (2) The result of intention recognition is input to the human-computer interaction (HCI) system, which controls the movement of the rehabilitation robot after receiving the instruction. This study truly realizes patient-oriented rehabilitation training. Experiments show that the human-machine collaboration model used can show higher accuracy of intention recognition, thereby increasing the satisfaction of using AJRR.

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Section: Experimental Backgroundmentioning

confidence: 99%

A Novel Human-Machine Collaboration Model of an Ankle Joint Rehabilitation Robot Driven by EEG Signals

Shi

Yang

Zhang

2021

Mathematical Problems in Engineering

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“…Therefore, this paper labels the last 10% of each engine data as the abnormal samples. In this paper, the number of hidden layer neurons is determined by a stepwise testing method [33], [34], where the step of the method is to set an initial value first, and then the number of the hidden layer neurons increases gradually based on the initial one. The classification performance of the model is considered as the selection criteria for selecting the number of neurons.…”

Section: Anomaly Monitoring Indexmentioning

confidence: 99%

A Multimode Anomaly Detection Method Based on OC-ELM for Aircraft Engine System

Chen

Wen

et al. 2021

IEEE Access

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The practical industrial processes possess the characteristics of multimode, unbalanced data distribution, and complex types of abnormalities, which are challenging to the anomaly detection task of complex industrial systems. In this paper, a novel anomaly detection framework based on one-class extreme learning machine (OC-ELM) for the multimode system is presented. To tackle the multiple operation modes, a clustering algorithm is first applied to distinguish the operation modes of the system. The corresponding detection models are built under different operation modes resulting in the multiple models operated in parallel. In addition, the proposed method constructs the reasonable boundary of the complex data distribution, reflecting the equipment running in the healthy or the normal state. The anomaly detection index is obtained according to the deviation degree between the testing sample and the normal model. As a result, a global monitoring index reflecting the degradation state is obtained by combining the anomaly monitoring indices of the equipment under multiple operation modes. The proposed method is verified on a public dataset of aircraft engines, and the advantages are demonstrated by comparing with the implemented detection model without handling the information of operation modes, and the multiple principal component analysis method.

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“…Different from the previous work, other authors propose their architecture instead of using a known network for the transfer learning, such as [49], [46], [64], [102], [103], [116] that propose CELM architectures with a different number of convolutional and pooling layers. The authors use CNN architectures for training the data with the fully connected layers.…”

Section: Pre-trained Cnn In Same Application Domain For Feature Extraction and Elm For Fast Learningmentioning

confidence: 99%

“…All these architectures presented better training results than classic machine learning models. [51], [48], [54], [50], [49], [46], and [47] used the Pavia dataset for remote sensing classification. Note that remote sensing approaches use another evaluation metrics such as average accuracy (AA), overall accuracy (OA), and Kappa, as shown in Table 14.…”

Section: Rq 3: Which Are the Main Findings When Applying Celm In Problems Based On Image Analysis?mentioning

confidence: 99%

Convolutional Extreme Learning Machines: A Systematic Review

Rodrigues¹,

Rogério²,

Kelner³

et al. 2021

Preprint

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Many works have recently identified the need to combine deep learning with extreme learning to strike a performance balance with accuracy especially in the domain of multimedia applications. Considering this new paradigm, namely convolutional extreme learning machine (CELM), we present a systematic review that investigates alternative deep learning architectures that use extreme learning machine (ELM) for a faster training to solve problems based on image analysis. We detail each of the architectures found in the literature, application scenarios, benchmark datasets, main results, advantages, and present the open challenges for CELM. We follow a well structured methodology and establish relevant research questions that guide our findings. We hope that the observation and classification of such works can leverage the CELM research area providing a good starting point to cope with some of the current problems in the image-based computer vision analysis.

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Research on Optimization Methods of ELM Classification Algorithm for Hyperspectral Remote Sensing Images

Cited by 23 publications

References 24 publications

A Novel Human-Machine Collaboration Model of an Ankle Joint Rehabilitation Robot Driven by EEG Signals

A Novel Human-Machine Collaboration Model of an Ankle Joint Rehabilitation Robot Driven by EEG Signals

A Multimode Anomaly Detection Method Based on OC-ELM for Aircraft Engine System

Convolutional Extreme Learning Machines: A Systematic Review

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