Online Regularized and Kernelized Extreme Learning Machines with Forgetting Mechanism

To apply the single hidden-layer feedforward neural networks (SLFN) to identify time-varying system, online regularized extreme learning machine (ELM) with forgetting mechanism (FORELM) and online kernelized ELM with forgetting mechanism (FOKELM) are presented in this paper. The FORELM updates the output weights of SLFN recursively by using Sherman-Morrison formula, and it combines advantages of online sequential ELM with forgetting mechanism (FOS-ELM) and regularized online sequential ELM (ReOS-ELM); that is, it can capture the latest properties of identified system by studying a certain number of the newest samples and also can avoid issue of ill-conditioned matrix inversion by regularization. The FOKELM tackles the problem of matrix expansion of kernel based incremental ELM (KB-IELM) by deleting the oldest sample according to the block matrix inverse formula when samples occur continually. The experimental results show that the proposed FORELM and FOKELM have better stability than FOS-ELM and have higher accuracy than ReOS-ELM in nonstationary environments; moreover, FORELM and FOKELM have time efficiencies superiority over dynamic regression extreme learning machine (DR-ELM) under certain conditions.

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Comparison of Several Extreme Learning Machine Algorithm for Modeling Concrete Compressive Strength

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.548-549.1735 ◽

2014 ◽

Vol 548-549 ◽

pp. 1735-1738 ◽

Cited By ~ 1

Author(s):

Jian Tang ◽

Dong Yan ◽

Li Jie Zhao

Keyword(s):

Compressive Strength ◽

Extreme Learning Machine ◽

Prediction Accuracy ◽

Partial Least Square ◽

Least Square ◽

Extreme Learning Machines ◽

Concrete Compressive Strength ◽

Learning Machines ◽

Learning Machine

Modeling concrete compressive strength is useful to ensure quality of civil engineering. This paper aims to compare several Extreme learning machines (ELMs) based modeling approaches for predicting the concrete compressive strength. Normal ELM algorithm, Partial least square-based extreme learning machines (PLS-ELMs) algorithm and Kernel ELM (KELM) algorithm are used and evaluated. Results indicate that the normal ELMs algorithm has the highest modeling speed, and the KELM has the best prediction accuracy. Every method is validated for modeling concrete compressive strength. The appropriate modeling approach should be selected according different purposes.

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Elliot and Symmetric Elliot Extreme Learning Machines for Gaussian Noisy Industrial Thermal Modelling

Energies ◽

10.3390/en12010090 ◽

2018 ◽

Vol 12 (1) ◽

pp. 90 ◽

Cited By ~ 3

Author(s):

Jose Salmeron ◽

Antonio Ruiz-Celma

Keyword(s):

Extreme Learning Machine ◽

Support Vector ◽

Thermal Processes ◽

Drying Process ◽

Machine Model ◽

Learning Machines ◽

Nearest Neighbours ◽

Thermal Drying ◽

Learning Machine ◽

Hidden Layer

This research proposes an Elliot-based Extreme Learning Machine approach for industrial thermal processes regression. The main contribution of this paper is to propose an Extreme Learning Machine model with Elliot and Symmetric Elliot activation functions that will look for the fittest number of neurons in the hidden layer. The methodological proposal is tested on an industrial thermal drying process. The thermal drying process is relevant in many industrial processes such as the food industry, biofuels production, detergents and dyes in powder production, pharmaceutical industry, reprography applications, textile industries and others. The methodological proposal of this paper outperforms the following techniques: Linear Regression, k-Nearest Neighbours regression, Regression Trees, Random Forest and Support Vector Regression. In addition, all the experiments have been benchmarked using four error measurements (MAE, MSE, MEADE, R 2 ).

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Peramalan Indeks Harga Saham Gabungan (IHSG) dengan Metode Extreme Learning Machine (ELM)

JMT : Jurnal Matematika dan Terapan ◽

10.21009/jmt.1.1.4 ◽

2017 ◽

Vol 1 (1) ◽

pp. 22-32

Author(s):

Afifah Arifianty ◽

Mulyono Mulyono ◽

Med Irzal

Keyword(s):

Neural Networks ◽

Extreme Learning Machine ◽

Feedforward Neural Networks ◽

Learning Machine ◽

Hidden Layer

Abstrak Indeks Harga Saham Gabungan (IHSG) merupakan suatu nilai untuk mengukur kinerja seluruh saham. IHSG mencerminkan perkembangan pasar secara keseluruhan. Jika IHSG mengalami kenaikan dari hari kemarin maka dapat disimpulkan beberapa saham yang berada pada bursa efek mengalami kenaikan. Oleh karena itu, peramalan harga akan sangat bermanfaat untuk para investor, sehingga mereka dapat mengetahui prospek investasi saham di masa datang. Ada banyak metode untuk peramalan. Tetapi, metode-metode yang telah ada sebelumnya membutuhkan waktu komputasi yang relatif lebih lama. Metode Jaringan Syaraf Tiruan(JST) dikhawatirkan akan semakin ditinggalkan karena diperlukan waktu yang lama dalam pengambilan keputusan. Untuk mengatasi masalah, Huang (2004) menemukan sebuah metode pembelajaran dalam JST bernama Extreme Learning Machine (ELM). ELM merupakan jaringan syaraf tiruan feedforward dengan satu hidden layer atau lebih dikenal dengan istilah Single hidden Layer Feedforward neural Networks(SLFNs) (Sun et al, 2008). Pada metode ini, faktor yang digunakan dalam peramalan hanya faktor data masa lalu, bukan disebabkan faktor lain seperti politik, ekonomi dan lain-lain. Kata kunci: Indeks Harga Saham Gabungan, Peramalan, Jaringan Syaraf Tiruan, Extreme Learning Machine.

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Spatial extreme learning machines: An application on prediction of disease counts

Statistical Methods in Medical Research ◽

10.1177/0962280218767985 ◽

2018 ◽

Vol 28 (9) ◽

pp. 2583-2594

Author(s):

Marcos O Prates

Keyword(s):

Learning Community ◽

Extreme Learning Machine ◽

Spatial Modeling ◽

Real Data ◽

Extreme Learning Machines ◽

Missing Information ◽

Learning Machines ◽

Uncertainty Prediction ◽

Learning Machine ◽

Many Sources

Extreme learning machines have gained a lot of attention by the machine learning community because of its interesting properties and computational advantages. With the increase in collection of information nowadays, many sources of data have missing information making statistical analysis harder or unfeasible. In this paper, we present a new model, coined spatial extreme learning machine, that combine spatial modeling with extreme learning machines keeping the nice properties of both methodologies and making it very flexible and robust. As explained throughout the text, the spatial extreme learning machines have many advantages in comparison with the traditional extreme learning machines. By a simulation study and a real data analysis we present how the spatial extreme learning machine can be used to improve imputation of missing data and uncertainty prediction estimation.

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Toward deep neural networks: Mirror extreme learning machines for pattern classification

Filomat ◽

10.2298/fil2015985l ◽

2020 ◽

Vol 34 (15) ◽

pp. 4985-4996

Author(s):

Bolin Liao ◽

Chuan Ma ◽

Meiling Liao ◽

Shuai Li ◽

Zhiguan Huang

Keyword(s):

Neural Network ◽

Extreme Learning Machine ◽

Pattern Classification ◽

Simple Structure ◽

Layer Structure ◽

Feed Forward Neural Network ◽

Learning Machines ◽

S Parameter ◽

Learning Machine ◽

Hidden Layer

In this paper, a novel type of feed-forward neural network with a simple structure is proposed and investigated for pattern classification. Because the novel type of forward neural network?s parameter setting is mirrored with those of the Extreme Learning Machine (ELM), it is termed the mirror extreme learning machine (MELM). For the MELM, the input weights are determined by the pseudoinverse method analytically, while the output weights are generated randomly, which are completely different from the conventional ELM. Besides, a growing method is adopted to obtain the optimal hidden-layer structure. Finally, to evaluate the performance of the proposed MELM, abundant comparative experiments based on different real-world classification datasets are performed. Experimental results validate the high classification accuracy and good generalization performance of the proposed neural network with a simple structure in pattern classification.

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Study on optimization of rake head density of suction hopper dredger based on bat algorithm and extreme learning machine

MATEC Web of Conferences ◽

10.1051/matecconf/202030904018 ◽

2020 ◽

Vol 309 ◽

pp. 04018

Author(s):

Guangjie Hao ◽

Menghong Yu ◽

Zhen Su

Keyword(s):

Extreme Learning Machine ◽

Prediction Models ◽

Prediction Method ◽

Bat Algorithm ◽

Yangtze Estuary ◽

Extreme Learning Machines ◽

Learning Machines ◽

Density Prediction ◽

Learning Machine ◽

Selection Of

The dredging output of suction dredger mainly comes from the suction density of the rake head. Accurate prediction of suction density is of great significance to improve the dredging output of suction dredger. In order to overcome the shortcomings of low accuracy and poor real-time performance of the current inhalation density prediction methods, a bat algorithm is proposed to optimize the inhalation density prediction method of extreme learning machine. The bat algorithms for optimizing extreme learning machines prediction model is constructed based on the measured construction data of “Xinhaifeng” Yangtze Estuary, and compared with other prediction models. Finally, the bat algorithms for optimizing extreme learning machines model is used to build the output simulator of inhalation density. Compared with the actual construction, the selection of control parameters is analyzed when the output of inhalation density is the best. Experients show that bat algorithms for optimizing extreme learning machines prediction has high accuracy and good stability, and can provide scientific and effective reference for yield prediction and construction guidance.

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Klasifikasi Keadaan Mata Berdasarkan sinyal EEG menggunakan Extreme Learning Machines

Indonesian Journal on Computing (Indo-JC) ◽

10.21108/indojc.2016.1.2.105 ◽

2016 ◽

Vol 1 (2) ◽

pp. 97 ◽

Cited By ~ 1

Author(s):

Ersa Christian Prakoso ◽

Untari Novia Wisesty ◽

Jondri .

Keyword(s):

Extreme Learning Machine ◽

Extreme Learning Machines ◽

White Space ◽

Font Size ◽

Stroke Width ◽

Learning Machines ◽

Normal Line ◽

Learning Machine

Electroencephalography atau sinyal EEG adalah salah satu biosignal yang marak menjadi topik penelitian saat ini. Sinyal EEG memiliki banyak manfaat seperti pendeteksian epilepsi, gangguan tidur, atau input dalam aplikasi komputer. Salah satu input yang dapat dideteksi berdasarkan sinyal EEG adalah keadaan mata. Namun untuk digunakan sebagai input dalam aplikasi diperlukan klasifikasi dengan performansi yang memadai. Oleh karena itu penulis akan dilakukan penelitian dimana salah satu metode pembelajaran Jaringan Syaraf Tiruan yaitu Extreme Learning Machine (ELM) akan diimplementasikan untuk mengklasifikasikan kondisi mata berdasarkan sinyal EEG. Dataset yang digunakan untuk melatih dan menguji model adalah dataset eye-state yang didonasikan oleh Oliver Roesler digabung dengan dataset yang berasal dari website repository Universitas of California, IrvineI (UCI) . Terdapat 7 corpus yang terdiri dari perekaman EEG yang dilakukan kepada 4 orang berbeda, lalu ditambahkan 1 corpus yang merupakan penggabungan seluruh corpus lain. Dari hasil pengujian yang dilakukan disimpulkan bahwa ELM dapat digunakan untuk klasifikasi keadaan mata dengan akurasi mencapai 97,95% dengan waktu latih hanya 0,81 detik jika masing-masing data digunakan secara terpisah, sedangkan penggabungan keseluruhan dataset hanya mencapai akurasi 78,94% dengan waktu latih 5,71 detik.<br style="font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; orphans: 2; text-align: -webkit-auto; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px;" />

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Lithofacies Recognition Based on Extreme Learning Machine

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.241-244.1762 ◽

2012 ◽

Vol 241-244 ◽

pp. 1762-1767 ◽

Cited By ~ 1

Author(s):

Ya Juan Tian ◽

Hua Xian Pan ◽

Xuan Chao Liu ◽

Guo Jian Cheng

Keyword(s):

Extreme Learning Machine ◽

Learning Algorithm ◽

Selection Process ◽

Feedforward Neural Networks ◽

Parameter Selection ◽

Classification Model ◽

Support Vector ◽

New Learning ◽

Learning Machine ◽

Hidden Layer

To overcome the problem of lower training speed and difficulty parameter selection in traditional support vector machine (SVM), a method based on extreme learning machine (ELM) for lithofacies recognition is presented in this paper. ELM is a new learning algorithm with single-hidden layer feedforward neural networks (SLFNN). Not only it can simplify the parameter selection process, but also improve the training speed of the network learning. By determining the optimal parameters, the lithofacies classification model is established, and the classification result of ELM is also compared to traditional SVM. The experimental results show that, ELM with less number of neurons has similar classification accuracy compared to SVM, and it is easier to select the parameters which significantly reduce the training speed. The feasibility of ELM for lithofacies recognition and the availability of the algorithm are verified and validated

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Evolutionary Voting-Based Extreme Learning Machines

Mathematical Problems in Engineering ◽

10.1155/2014/808292 ◽

2014 ◽

Vol 2014 ◽

pp. 1-7 ◽

Cited By ~ 3

Author(s):

Nan Liu ◽

Jiuwen Cao ◽

Zhiping Lin ◽

Pin Pin Pek ◽

Zhi Xiong Koh ◽

...

Keyword(s):

Genetic Algorithm ◽

Decision Making ◽

Extreme Learning Machine ◽

Real World ◽

Classification Accuracy ◽

Majority Voting ◽

Extreme Learning Machines ◽

Learning Machines ◽

Learning Machine ◽

Elm Classifier

Voting-based extreme learning machine (V-ELM) was proposed to improve learning efficiency where majority voting was employed. V-ELM assumes that all individual classifiers contribute equally to the decision ensemble. However, in many real-world scenarios, this assumption does not work well. In this paper, we aim to enhance V-ELM by introducing weights to distinguish the importance of each individual ELM classifier in decision making. Genetic algorithm is used for optimizing these weights. This evolutionary V-ELM is named as EV-ELM. Results on several benchmark databases show that EV-ELM achieves the highest classification accuracy compared with V-ELM and ELM.

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Cholesky Factorization Based Online Sequential Extreme Learning Machines with Persistent Regularization and Forgetting Factor

Symmetry ◽

10.3390/sym11060801 ◽

2019 ◽

Vol 11 (6) ◽

pp. 801

Author(s):

Xinran Zhou ◽

Xiaoyan Kui

Keyword(s):

Extreme Learning Machine ◽

Cholesky Factorization ◽

Extreme Learning Machines ◽

Forgetting Factor ◽

Learning Machines ◽

Chunk Size ◽

Ill Posed ◽

Regression Problems ◽

Simulation Results ◽

Learning Machine

The online sequential extreme learning machine with persistent regularization and forgetting factor (OSELM-PRFF) can avoid potential singularities or ill-posed problems of online sequential regularized extreme learning machines with forgetting factors (FR-OSELM), and is particularly suitable for modelling in non-stationary environments. However, existing algorithms for OSELM-PRFF are time-consuming or unstable in certain paradigms or parameters setups. This paper presents a novel algorithm for OSELM-PRFF, named “Cholesky factorization based” OSELM-PRFF (CF-OSELM-PRFF), which recurrently constructs an equation for extreme learning machine and efficiently solves the equation via Cholesky factorization during every cycle. CF-OSELM-PRFF deals with timeliness of samples by forgetting factor, and the regularization term in its cost function works persistently. CF-OSELM-PRFF can learn data one-by-one or chunk-by-chunk with a fixed or varying chunk size. Detailed performance comparisons between CF-OSELM-PRFF and relevant approaches are carried out on several regression problems. The numerical simulation results show that CF-OSELM-PRFF demonstrates higher computational efficiency than its counterparts, and can yield stable predictions.

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