Artificial Neural Network (ANN) beyond cots remote sensing packages: Implementation of Extreme Learning Machine (ELM) in MATLAB

2012 ◽  
Author(s):  
Shailesh Shrestha ◽  
Zbigniew Bochenek ◽  
Claire Smith
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Extreme Learning Machine ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Learning Machine

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

scholarly journals Deep Extreme Learning Machine and Its Application in EEG Classification

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Shifei Ding ◽  
Nan Zhang ◽  
Xinzheng Xu ◽  
Lili Guo ◽  
Jian Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Deep Learning ◽  
Extreme Learning Machine ◽  
Learning Algorithm ◽  
Eeg Classification ◽  
Neural Network Algorithm ◽  
Artificial Neural ◽  
Learning Machine

Recently, deep learning has aroused wide interest in machine learning fields. Deep learning is a multilayer perceptron artificial neural network algorithm. Deep learning has the advantage of approximating the complicated function and alleviating the optimization difficulty associated with deep models. Multilayer extreme learning machine (MLELM) is a learning algorithm of an artificial neural network which takes advantages of deep learning and extreme learning machine. Not only does MLELM approximate the complicated function but it also does not need to iterate during the training process. We combining with MLELM and extreme learning machine with kernel (KELM) put forward deep extreme learning machine (DELM) and apply it to EEG classification in this paper. This paper focuses on the application of DELM in the classification of the visual feedback experiment, using MATLAB and the second brain-computer interface (BCI) competition datasets. By simulating and analyzing the results of the experiments, effectiveness of the application of DELM in EEG classification is confirmed.

scholarly journals Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network

2018 ◽  
Vol 12 (1) ◽  
pp. 738-749 ◽  
Author(s):  
Pezhman Taherei Ghazvinei ◽  
Hossein Hassanpour Darvishi ◽  
Amir Mosavi ◽  
Khamaruzaman bin Wan Yusof ◽  
Meysam Alizamir ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Extreme Learning Machine ◽  
Meteorological Parameters ◽  
Growth Prediction ◽  
Artificial Neural ◽  
Learning Machine

scholarly journals Prediction of wind speed using a new Grey-extreme learning machine hybrid algorithm: A case study

2018 ◽  
Vol 30 (1) ◽  
pp. 44-62 ◽  
Author(s):  
Mojtaba Qolipour ◽  
Ali Mostafaeipour ◽  
Mohammad Saidi-Mehrabad ◽  
Hamid R Arabnia
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wind Speed ◽  
Mean Square Error ◽  
Extreme Learning Machine ◽  
Hybrid Algorithm ◽  
Mean Square ◽  
Grey Systems Theory ◽  
Artificial Neural ◽  
Learning Machine

Wind energy is becoming one of the most important sources of renewable energy for many countries in the future. The purpose of this study is to predict wind speed using different algorithms. In this study, a new hybrid algorithm is developed to predict the wind speed behavior, and 24 h predictions of changes in wind speed are obtained with the aid of Homer software. The proposed algorithm is a combination of a well-known artificial neural network predictor called extreme learning machine as an artificial neural network algorithm and the Grey model (1, 1) as a method of Grey systems theory. Long-term wind speed forecasts are obtained using three-year data (2013–2016) of eight variables: TMAX, TMIN, VP, RHMIN, RHMAX, WINDSPEED, SUNSHINE HOURS, and PERCIPITATION for the Zanjan city in Iran, and 24 h wind speed forecast is obtained using 10-year data (2005–2015) pertaining to this city. The results show that proposed algorithm with relative measure of fit R2 of 0.99376 and mean square error of 0.000376 provides better predictions of wind speed in the study area than ordinary extreme learning machine algorithm with R2 of 0.98075 and mean square error of 0.00720. Also, the 24 h prediction of changes in wind speed is done using Homer software. The methodology in this research is more efficient in terms of execution performance and accuracy.

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