q-Gaussian activation function Circular Extreme learning machine for classification problems

2016 ◽  
Author(s):  
Sarutte Atsawaraungsuk
Keyword(s):  
Extreme Learning Machine ◽  
Activation Function ◽  
Classification Problems ◽  
Learning Machine

scholarly journals Protein Sequence Classification with Improved Extreme Learning Machine Algorithms

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Jiuwen Cao ◽  
Lianglin Xiong
Keyword(s):  
Extreme Learning Machine ◽  
Protein Sequence ◽  
Protein Sequences ◽  
Activation Function ◽  
Majority Voting ◽  
Training Algorithms ◽  
Sequence Classification ◽  
Protein Sequence Classification ◽  
Learning Machine ◽  
Majority Voting Method

Precisely classifying a protein sequence from a large biological protein sequences database plays an important role for developing competitive pharmacological products. Comparing the unseen sequence with all the identified protein sequences and returning the category index with the highest similarity scored protein, conventional methods are usually time-consuming. Therefore, it is urgent and necessary to build an efficient protein sequence classification system. In this paper, we study the performance of protein sequence classification using SLFNs. The recent efficient extreme learning machine (ELM) and its invariants are utilized as the training algorithms. The optimal pruned ELM is first employed for protein sequence classification in this paper. To further enhance the performance, the ensemble based SLFNs structure is constructed where multiple SLFNs with the same number of hidden nodes and the same activation function are used as ensembles. For each ensemble, the same training algorithm is adopted. The final category index is derived using the majority voting method. Two approaches, namely, the basic ELM and the OP-ELM, are adopted for the ensemble based SLFNs. The performance is analyzed and compared with several existing methods using datasets obtained from the Protein Information Resource center. The experimental results show the priority of the proposed algorithms.

scholarly journals A Novel Orthogonal Extreme Learning Machine for Regression and Classification Problems

Symmetry ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1284
Author(s):  
Licheng Cui ◽  
Huawei Zhai ◽  
Hongfei Lin
Keyword(s):  
Least Squares ◽  
Extreme Learning Machine ◽  
Weight Matrix ◽  
Testing Time ◽  
Least Squares Regression ◽  
Classification Problems ◽  
Training Time ◽  
Discrimination Ability ◽  
Learning Machine ◽  
Hidden Layer

An extreme learning machine (ELM) is an innovative algorithm for the single hidden layer feed-forward neural networks and, essentially, only exists to find the optimal output weight so as to minimize output error based on the least squares regression from the hidden layer to the output layer. With a focus on the output weight, we introduce the orthogonal constraint into the output weight matrix, and propose a novel orthogonal extreme learning machine (NOELM) based on the idea of optimization column by column whose main characteristic is that the optimization of complex output weight matrix is decomposed into optimizing the single column vector of the matrix. The complex orthogonal procrustes problem is transformed into simple least squares regression with an orthogonal constraint, which can preserve more information from ELM feature space to output subspace, these make NOELM more regression analysis and discrimination ability. Experiments show that NOELM has better performance in training time, testing time and accuracy than ELM and OELM.

Modeling of Drying Process of Bittim Nuts (pistacia terebinthus) in a Fixed Bed Dryer System by Using Extreme Learning Machine

2012 ◽  
Vol 8 (4) ◽  
Author(s):  
Asım Balbay ◽  
Engin Avci ◽  
Ömer Şahin ◽  
Resul Coteli
Keyword(s):  
Extreme Learning Machine ◽  
Fixed Bed ◽  
Experimental Studies ◽  
Learning Rule ◽  
Activation Function ◽  
Optimal Topology ◽  
Gradient Based ◽  
Learning Machine ◽  
Pistacia Terebinthus ◽  
Hidden Neurons

Abstract Artificial neural networks (ANNs) have been widely used in modeling of various systems. Training of ANNs is commonly performed by backpropagation based on a gradient-based learning rule. However, it is well-known that such learning rule has several shortcomings such as slow convergence and training failures. This paper proposes a modeling technique based on Extreme Learning Machine (ELM) eliminating disadvantages of backpropagation based on a gradient-based learning rule for the drying of bittim (pistacia terebinthus). The samples for ELM based model are obtained by experimental studies. In experimental studies, the sample mass loss rate as a function time was investigated in different air velocities (0.5 and 1 m/s) and air temperatures (40, 60 and 80°C) in a designed dryer system. The obtained samples from experiments are used for training and testing of ELM. Further, some parameters of ELM such as type of activation function and the number of hidden neurons are set to obtain the best possible modelling results. The obtained prediction results show that ELM algorithm with tangent sigmoid activation function and 20 hidden neurons is appeared to be most optimal topology since maximum R2 and minimum rms (0.0500) and cov (0.2256) values are obtained. Thus, it is concluded that ELM can be used as an effective modelling tool in the drying of bittim (pistacia terebinthus) in fixed bed dryer system.

scholarly journals Correntropy induced loss based sparse robust graph regularized extreme learning machine for cancer classification

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Liang-Rui Ren ◽  
Ying-Lian Gao ◽  
Jin-Xing Liu ◽  
Junliang Shang ◽  
Chun-Hou Zheng
Keyword(s):  
Extreme Learning Machine ◽  
High Performance ◽  
Structural Information ◽  
Optimization Method ◽  
Classification Performance ◽  
Weight Matrix ◽  
New Method ◽  
Classification Problems ◽  
Graph Regularization ◽  
Learning Machine

Abstract Background As a machine learning method with high performance and excellent generalization ability, extreme learning machine (ELM) is gaining popularity in various studies. Various ELM-based methods for different fields have been proposed. However, the robustness to noise and outliers is always the main problem affecting the performance of ELM. Results In this paper, an integrated method named correntropy induced loss based sparse robust graph regularized extreme learning machine (CSRGELM) is proposed. The introduction of correntropy induced loss improves the robustness of ELM and weakens the negative effects of noise and outliers. By using the L2,1-norm to constrain the output weight matrix, we tend to obtain a sparse output weight matrix to construct a simpler single hidden layer feedforward neural network model. By introducing the graph regularization to preserve the local structural information of the data, the classification performance of the new method is further improved. Besides, we design an iterative optimization method based on the idea of half quadratic optimization to solve the non-convex problem of CSRGELM. Conclusions The classification results on the benchmark dataset show that CSRGELM can obtain better classification results compared with other methods. More importantly, we also apply the new method to the classification problems of cancer samples and get a good classification effect.

Fuzziness-based online sequential extreme learning machine for classification problems

2018 ◽  
Vol 22 (11) ◽  
pp. 3487-3494 ◽  
Author(s):  
Weipeng Cao ◽  
Jinzhu Gao ◽  
Zhong Ming ◽  
Shubin Cai ◽  
Zhiguang Shan
Keyword(s):  
Extreme Learning Machine ◽  
Classification Problems ◽  
Learning Machine

scholarly journals Forecasting China’s Renewable Energy Terminal Power Consumption Based on Empirical Mode Decomposition and an Improved Extreme Learning Machine Optimized by a Bacterial Foraging Algorithm

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1331 ◽  
Author(s):  
Peng Jiang ◽  
Jun Dong ◽  
Hui Huang
Keyword(s):  
Renewable Energy ◽  
Energy Consumption ◽  
Power Consumption ◽  
Extreme Learning Machine ◽  
Activation Function ◽  
Forecasting Model ◽  
Fossil Energy ◽  
Mode Decomposition ◽  
Bacterial Foraging Algorithm ◽  
Learning Machine

The energy consumption pattern dominated by traditional fossil energy has led to global energy resource constraints and the deterioration of the ecological environment. These challenges have become a major issue all over the world. At present, the Chinese government aims to significantly reduce the fossil energy consumption contribution in the terminal energy consumption. The development of renewable energy in the terminal energy and energy conversion links has significantly increased the proportion of clean low-carbon energy. In order to accurately get the proportion of renewable energy terminal power consumption, firstly, this paper selects a primary influencing-factors set including the gross GDP, fixed investment in renewable energy industry, total length of cross-provincial and cross-regional high-voltage transmission lines, etc. as influencing factors of China’s electricity consumption fraction produced by renewable energy based on a multitude of papers. Secondly, from the perspective of signal decomposition, the data inevitably has a lot of interference and noise. This paper uses the empirical mode decomposition (EMD) algorithm to reduce the degree of signal distortion and decomposes the signal into natural modes including several intrinsic mode functions (IMFs) and a residual term (Res); afterwards, a new extreme learning machine (ELM) forecasting model optimized by an Inverse Square Root Linear Units (ISRLU) activation function is proposed, and the ISRLU function is used to replace the implicit layer activation function in the original ELM algorithm. Then, a new bacterial foraging algorithm (BFOA) is applied to optimize the parameters of the optimized ELM forecasting model. After multiple learning and training operations, the optimal parameters are obtained. Finally, we superimpose the output of each IMF and Res training task to get the amount of China’s power consumption produced by renewable energy. Some statistical indicators including root mean squard error (RMSE) are applied to compare the accuracy of several intelligent machine forecasting algorithms. We prove that the proposed forecasting model has higher prediction accuracy and achieves faster training speed by an empirical analysis. Finally, the proposed combined forecasting algorithm is applied to predict China’s renewable energy terminal power consumption from 2018 to 2030. According to the forecasting results, it is found that China’s renewable energy terminal power consumption shows a gradual growth trend, and will exceeded 3300 billion kWh in 2030, which will represent a renewable energy terminal power ratio of about 38% in 2030.

scholarly journals Klasifikasi Jenis Kendaraan Menggunakan Metode Extreme Learning Machine

2021 ◽  
Vol 2 (4) ◽  
pp. 237-243
Author(s):  
Rispani Himilda ◽  
Ragil Andika Johan
Keyword(s):  
Extreme Learning Machine ◽  
Traffic Accidents ◽  
Feedforward Neural Networks ◽  
Training Data ◽  
Classification Problems ◽  
Road Infrastructure ◽  
The Road ◽  
On The Road ◽  
Learning Machine ◽  
Processing Techniques

The number of vehicles in Indonesia has increased each year, both two-wheeled and four-wheeled vehicles; this is inversely proportional to the development of road infrastructure in Indonesia, which has not experienced much change or improvement. Supposedly, with the increase in the number of vehicles, road infrastructure must also keep pace so that things such as the accumulation of cars on the road do not occur, traffic accidents and congestion become obstacles to carrying out activities. Therefore, it is necessary to make a system to detect and classify vehicles' types in this study using two types of vehicles, namely cars and motorbikes. According to the Indonesian Central Statistics Agency (BPS), it is the highest number. The classification system uses digital image processing techniques, a science to study how an image is formed, processed, and analyzed by a computer to produce information that humans can understand. The method used in this research is the Extreme Learning Machine (ELM), a part of artificial intelligence in feedforward neural networks, where this method can solve regression and classification problems. The data used in this study are 25 images of cars and motorbikes as training data and 15 photos of cars and motorbikes as test data, respectively. The results obtained from this study are a system for classifying two types of vehicles, namely cars and motorbikes, with an accuracy rate of 86.6%.  

scholarly journals DCS-ELM: a novel method for extreme learning machine for regression problems and a new approach for the SFRSCC

2021 ◽  
Vol 7 ◽  
pp. e411
Author(s):  
Osman Altay ◽  
Mustafa Ulas ◽  
Kursat Esat Alyamac
Keyword(s):  
Extreme Learning Machine ◽  
Discrete Time ◽  
High Performance ◽  
Chaotic Systems ◽  
Optimization Methods ◽  
Generalized Solution ◽  
Support Vector ◽  
Classification Problems ◽  
Regression Problems ◽  
Learning Machine

Extreme learning machine (ELM) algorithm is widely used in regression and classification problems due to its advantages such as speed and high-performance rate. Different artificial intelligence-based optimization methods and chaotic systems have been proposed for the development of the ELM. However, a generalized solution method and success rate at the desired level could not be obtained. In this study, a new method is proposed as a result of developing the ELM algorithm used in regression problems with discrete-time chaotic systems. ELM algorithm has been improved by testing five different chaotic maps (Chebyshev, iterative, logistic, piecewise, tent) from chaotic systems. The proposed discrete-time chaotic systems based ELM (DCS-ELM) algorithm has been tested in steel fiber reinforced self-compacting concrete data sets and public four different datasets, and a result of its performance compared with the basic ELM algorithm, linear regression, support vector regression, kernel ELM algorithm and weighted ELM algorithm. It has been observed that it gives a better performance than other algorithms.

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