Local Receptive Fields Based Extreme Learning Machine

Huang, Guang-Bin; Bai, Zuo; Kasun, Liyanaarachchi Lekamalage Chamara; Vong, Chi‐Man

doi:10.1109/mci.2015.2405316

Cited by 329 publications

(165 citation statements)

References 50 publications

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“…Several studies have been investigated the development of good learning methods in the past decades. In contrast to well-known neural networks, e.g., the multilayer perceptron (MLP) and radial basis function network (RBFN), ELMs possess a real-time learning capability and good prediction ability as intelligent predictors [21,24,25]. Figure 1 shows the architecture of an ELM predictor.…”

Section: Elm As An Intelligent Predictormentioning

confidence: 99%

Short-Term Electricity-Load Forecasting Using a TSK-Based Extreme Learning Machine with Knowledge Representation

Yeom

Kwak

2017

Energies

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This paper discusses short-term electricity-load forecasting using an extreme learning machine (ELM) with automatic knowledge representation from a given input-output data set. For this purpose, we use a Takagi-Sugeno-Kang (TSK)-based ELM to develop a systematic approach to generating if-then rules, while the conventional ELM operates without knowledge information. The TSK-ELM design includes a two-phase development. First, we generate an initial random-partition matrix and estimate cluster centers for random clustering. The obtained cluster centers are used to determine the premise parameters of fuzzy if-then rules. Next, the linear weights of the TSK fuzzy type are estimated using the least squares estimate (LSE) method. These linear weights are used as the consequent parameters in the TSK-ELM design. The experiments were performed on short-term electricity-load data for forecasting. The electricity-load data were used to forecast hourly day-ahead loads given temperature forecasts; holiday information; and historical loads from the New England ISO. In order to quantify the performance of the forecaster, we use metrics and statistical characteristics such as root mean squared error (RMSE) as well as mean absolute error (MAE), mean absolute percent error (MAPE), and R-squared, respectively. The experimental results revealed that the proposed method showed good performance when compared with a conventional ELM with four activation functions such sigmoid, sine, radial basis function, and rectified linear unit (ReLU). It possessed superior prediction performance and knowledge information and a small number of rules.

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Section: Elm As An Intelligent Predictormentioning

confidence: 99%

Short-Term Electricity-Load Forecasting Using a TSK-Based Extreme Learning Machine with Knowledge Representation

Yeom

Kwak

2017

Energies

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“…The structure of ELM is as follows. (16)(17)(18) As illustrated in Fig. 1, the function of the hidden layer is denoted as g(x) and the number of hidden nodes is denoted as L. When a set of samples (x i , t i ), 1 ≤ i ≤ N, is given, where…”

Section: Prediction Model Based On Kelmmentioning

confidence: 99%

“…It was also based on a feed-forward neural network structure but a new and single hidden layer was adopted. (16)(17)(18) Unlike the classical neural network, only the number of hidden nodes in ELM needs to be set in advance. During training, input weights and thresholds are randomly assigned, and output weights are solved using these samples for training and the theory of the generalized inverse matrix.…”

Section: Introductionmentioning

confidence: 99%

A Prediction Method for Deck Motion of Aircraft Carrier Based on Particle Swarm Optimization and Kernel Extreme Learning Machine

2017

Sensors and Materials

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The prediction of deck motion is an effective and potential means of improving the landing/ take-off safety of carrier-based aircraft using current and historical deck-motion measurements when deck motion in six degrees of freedom cannot be effectively controlled or restrained. The prediction models of deck motion should have excellent nonlinear fitting ability to cope with the deck-motion characteristics of randomness and nonlinearity caused by waves and wind; and should not use heavy computation to fulfill the requirement of real-time prediction for deck motion. It is generally believed that classical feed-forward neural networks, such as the back-propagation (BP) network, have excellent nonlinear fitting ability but suffer from slow training processes and reduced local optimum, thus failing to satisfy the requirements of real-time and high accuracy for deck-motion prediction. In addition, the extreme learning machine (ELM) is easy to train but it is difficult for ELM to determine the number of hidden layer nodes; an incorrect number of hidden layer nodes will introduce poor stability and generalization ability. To fulfill the requirements of deck motion prediction, a prediction method based on ELM, support vector machine, and particle swarm optimization [particle swam optimization kernel extreme learning machine (PSO-KELM)] is designed. In this method, the fundamental structure of the ELM is used and the kernel function from the support vector machine (SVM) is introduced to replace the hidden function in ELM. Further aiming at the acquisition of optimal parameters, including the penalty coefficient and kernel parameters for the kernel function, autoadaptive particle swarm optimization is adopted. Simulation results indicate that a prediction method based on PSO-KELM has the advantages of a simple structure, fast training speed, and powerful generalization ability, and thus can satisfy the requirements of real-time and high-accuracy deck-motion prediction. Compared with the prediction data from BP and the ELM, high-precision prediction data can be obtained with PSO-KELM. PSO-KELM has a significantly reduced training time compared with BP.

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“…Specifically, for solder joint defect identification, we take histogram peaks distribution (HPD) [16] with morphological operation to extract image features and then apply extreme learning machine (ELM) [21][22][23][24] to classify the input images. As for solar panel position determination, accurate edges location is prerequisite.…”

Section: Introductionmentioning

confidence: 99%

“…As for solar panel position determination, accurate edges location is prerequisite. To achieve this, we extract the initial edge points by a novel signal processing method, fractional calculus [16,[22][23][24], and then utilize MLSR algorithm [25,26] to refine initial edges. Once the final edges of solder panel are acquired, the position parameters, position shift, deflection angle, and edge lengths, are easily determined.…”

Section: Introductionmentioning

confidence: 99%

Extreme Learning Machine and Moving Least Square Regression Based Solar Panel Vision Inspection

Liu

Zhang²,

Huang

2017

Journal of Electrical and Computer Engineering

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In recent years, learning based machine intelligence has aroused a lot of attention across science and engineering. Particularly in the field of automatic industry inspection, the machine learning based vision inspection plays a more and more important role in defect identification and feature extraction. Through learning from image samples, many features of industry objects, such as shapes, positions, and orientations angles, can be obtained and then can be well utilized to determine whether there is defect or not. However, the robustness and the quickness are not easily achieved in such inspection way. In this work, for solar panel vision inspection, we present an extreme learning machine (ELM) and moving least square regression based approach to identify solder joint defect and detect the panel position. Firstly, histogram peaks distribution (HPD) and fractional calculus are applied for image preprocessing. Then an ELM-based defective solder joints identification is discussed in detail. Finally, moving least square regression (MLSR) algorithm is introduced for solar panel position determination. Experimental results and comparisons show that the proposed ELM and MLSR based inspection method is efficient not only in detection accuracy but also in processing speed.

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Local Receptive Fields Based Extreme Learning Machine

Cited by 329 publications

References 50 publications

Short-Term Electricity-Load Forecasting Using a TSK-Based Extreme Learning Machine with Knowledge Representation

Short-Term Electricity-Load Forecasting Using a TSK-Based Extreme Learning Machine with Knowledge Representation

A Prediction Method for Deck Motion of Aircraft Carrier Based on Particle Swarm Optimization and Kernel Extreme Learning Machine

Extreme Learning Machine and Moving Least Square Regression Based Solar Panel Vision Inspection

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