Extreme learning machine terrain-based navigation for unmanned aerial vehicles

Kan, Ee May; Lim, Meng‐Hiot; Ong, Yew-Soon; Tan, Ah-Hwee; Yeo, S.P.

doi:10.1007/s00521-012-0866-9

Cited by 25 publications

(15 citation statements)

References 20 publications

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“…To overcome such issue, a useful learning scheme, the extreme learning machine (ELM), was suggested in [13] for single layer feedforward neural networks and subsequently extended to different variations such as local connected structure [14] and multi hidden layers structure [15]. ELM and its variations [16,17,18,19,20,21,22,23] have been successfully used in the fields as object recognition [14],terrain-based navigation [24] activity recognition [25], time series [26], security assessment [27], written character recognition [28], face recognition [29], gene selection and cancer classification [30]. In essence ELM is a learning scheme whose hidden nodes need not be tuned and can be randomly generated.…”

Section: Extreme Learning Machinementioning

confidence: 99%

A Fast SVD-Hidden-nodes based Extreme Learning Machine for Large-Scale Data Analytics

2016

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Big dimensional data is a growing trend that is emerging in many real world contexts, extending from web mining, gene expression analysis, protein-protein interaction to high-frequency financial data. Nowadays, there is a growing consensus that the increasing dimensionality poses impeding effects on the performances of classifiers, which is termed as the "peaking phenomenon" in the field of machine intelligence. To address the issue, dimensionality reduction is commonly employed as a preprocessing step on the Big dimensional data before building the classifiers. In this paper, we propose an Extreme Learning Machine (ELM) approach for large-scale data analytic. In contrast to existing approaches, we embed hidden nodes that are designed using singular value decomposition (SVD) into the classical ELM. These SVD nodes in the hidden layer are shown to capture the underlying characteristics of the Big dimensional data well, exhibiting excellent generalization performances. The drawback of using SVD on the entire dataset, however, is the high computational complexity involved. To address this, a fast divide and conquer approximation scheme is introduced to maintain computational tractability on high volume data. The resultant algorithm proposed is labeled here as Fast Singular Value Decomposition-Hidden-nodes based Extreme Learning Machine or FSVD-H-ELM in short. In FSVD-H-ELM, instead of identifying the SVD hidden nodes directly from the entire dataset, SVD hidden nodes are derived from multiple random subsets of data sampled from the original dataset. Comprehensive experiments and comparisons are conducted to assess the FSVD-H-ELM against other state-of-the-art algorithms. The results obtained demonstrated the superior generalization performance and efficiency of the FSVD-H-ELM.

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Section: Extreme Learning Machinementioning

confidence: 99%

A Fast SVD-Hidden-nodes based Extreme Learning Machine for Large-Scale Data Analytics

2016

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“…In addition to image, video, and medical applications, ELM has also been widely researched and implemented on other high dimensional and large data applications [39,[88][89][90][91][92][93][94][95][96][97][98][99][100][101][102][103]. These achievements covered time series prediction and forecasting [88][89][90][91][92][93], terrain reconstruction and navigation [94,95], power loss analysis [96], company internationalization search [97], XML document classification and text categorization [98,99], cloud computing [100], activity recognition for miniwearable devices [101], imbalance data processing [39,102,103], and so forth. Such fruitful results enlarged the application fields of ELM.…”

Section: Other Applicationsmentioning

confidence: 99%

Extreme Learning Machines on High Dimensional and Large Data Applications: A Survey

Cao

Lin

2015

Mathematical Problems in Engineering

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Extreme learning machine (ELM) has been developed for single hidden layer feedforward neural networks (SLFNs). In ELM algorithm, the connections between the input layer and the hidden neurons are randomly assigned and remain unchanged during the learning process. The output connections are then tuned via minimizing the cost function through a linear system. The computational burden of ELM has been significantly reduced as the only cost is solving a linear system. The low computational complexity attracted a great deal of attention from the research community, especially for high dimensional and large data applications. This paper provides an up-to-date survey on the recent developments of ELM and its applications in high dimensional and large data. Comprehensive reviews on image processing, video processing, medical signal processing, and other popular large data applications with ELM are presented in the paper.

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“…ELMs not only tend to reach the smallest training error, but also the smallest output weight norm, which results in better generalization performance for feedforward networks [13]. Due to its effectiveness and fast learning process, ELM and its many variants proposed in the recent years have been successfully used in various fields, such as forecasting of photovoltaic power [14], face recognition [15], terrain-based navigation [16], time-series data analysis [17] and so on. Additionally, Suykens and Vandewalle [18] described a training method for SLFNs which applies the hidden layer output mapping as the feature mapping of support vector machine (SVM).…”

Section: Introductionmentioning

confidence: 99%

An Extreme Learning Machine Architecture Based on Volterra Filtering and PCA

Chen

Yang

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARYExtreme learning machine (ELM) has recently attracted many researchers' interest due to its very fast learning speed, good generalization ability, and ease of implementation. However, it has a linear output layer which may limit the capability of exploring the available information, since higher-order statistics of the signals are not taken into account. To address this, we propose a novel ELM architecture in which the linear output layer is replaced by a Volterra filter structure. Additionally, the principal component analysis (PCA) technique is used to reduce the number of effective signals transmitted to the output layer. This idea not only improves the processing capability of the network, but also preserves the simplicity of the training process. Then we carry out performance evaluation and application analysis for the proposed architecture in the context of supervised classification and unsupervised equalization respectively, and the obtained results either on publicly available datasets or various channels, when compared to those produced by already proposed ELM versions and a state-of-the-art algorithm: support vector machine (SVM), highlight the adequacy and the advantages of the proposed architecture and characterize it as a promising tool to deal with signal processing tasks.

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Extreme learning machine terrain-based navigation for unmanned aerial vehicles

Cited by 25 publications

References 20 publications

A Fast SVD-Hidden-nodes based Extreme Learning Machine for Large-Scale Data Analytics

A Fast SVD-Hidden-nodes based Extreme Learning Machine for Large-Scale Data Analytics

Extreme Learning Machines on High Dimensional and Large Data Applications: A Survey

An Extreme Learning Machine Architecture Based on Volterra Filtering and PCA

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