scholarly journals Adaptive Control of Nonlinear Discrete-Time Systems by Using OS-ELM Neural Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Xiao-Li Li ◽  
Chao Jia ◽  
De-xin Liu ◽  
Da-wei Ding
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Adaptive Control ◽  
Extreme Learning Machine ◽  
Nonlinear Dynamic ◽  
Training Data ◽  
Multiple Model ◽  
Learning Machine ◽  
Hidden Layer ◽  
Time Systems

As a kind of novel feedforward neural network with single hidden layer, ELM (extreme learning machine) neural networks are studied for the identification and control of nonlinear dynamic systems. The property of simple structure and fast convergence of ELM can be shown clearly. In this paper, we are interested in adaptive control of nonlinear dynamic plants by using OS-ELM (online sequential extreme learning machine) neural networks. Based on data scope division, the problem that training process of ELM neural network is sensitive to the initial training data is also solved. According to the output range of the controlled plant, the data corresponding to this range will be used to initialize ELM. Furthermore, due to the drawback of conventional adaptive control, when the OS-ELM neural network is used for adaptive control of the system with jumping parameters, the topological structure of the neural network can be adjusted dynamically by using multiple model switching strategy, and an MMAC (multiple model adaptive control) will be used to improve the control performance. Simulation results are included to complement the theoretical results.

HONNs with Extreme Learning Machine to Handle Incomplete Datasets

2013 ◽  
pp. 276-292
Author(s):  
Shuxiang Xu
Keyword(s):  
Neural Networks ◽  
Missing Data ◽  
Extreme Learning Machine ◽  
Real Life ◽  
Bankruptcy Prediction ◽  
Share Prices ◽  
Fast Learning ◽  
Business Cases ◽  
Learning Machine ◽  
Hidden Layer

An Extreme Learning Machine (ELM) randomly chooses hidden neurons and analytically determines the output weights (Huang, et al., 2005, 2006, 2008). With the ELM algorithm, only the connection weights between hidden layer and output layer are adjusted. The ELM algorithm tends to generalize better at a very fast learning speed: it can learn thousands of times faster than conventionally popular learning algorithms (Huang, et al., 2006). Artificial Neural Networks (ANNs) have been widely used as powerful information processing models and adopted in applications such as bankruptcy prediction, predicting costs, forecasting revenue, forecasting share prices and exchange rates, processing documents, and many more. Higher Order Neural Networks (HONNs) are ANNs in which the net input to a computational neuron is a weighted sum of products of its inputs. Real life data are not usually perfect. They contain wrong, incomplete, or vague data. Hence, it is usual to find missing data in many information sources used. Missing data is a common problem in statistical analysis (Little & Rubin, 1987). This chapter uses the Extreme Learning Machine (ELM) algorithm for HONN models and applies it in several significant business cases, which involve missing datasets. The experimental results demonstrate that HONN models with the ELM algorithm offer significant advantages over standard HONN models, such as faster training, as well as improved generalization abilities.

scholarly journals One-Class Classification with Extreme Learning Machine

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Qian Leng ◽  
Honggang Qi ◽  
Jun Miao ◽  
Wentao Zhu ◽  
Guiping Su
Keyword(s):  
Neural Network ◽  
Extreme Learning Machine ◽  
Classification Problem ◽  
Traditional Learning ◽  
Learning Speed ◽  
One Class Classifier ◽  
Learning Machine ◽  
Hidden Layer ◽  
One Class Classification ◽  
Network Approaches

One-class classification problem has been investigated thoroughly for past decades. Among one of the most effective neural network approaches for one-class classification, autoencoder has been successfully applied for many applications. However, this classifier relies on traditional learning algorithms such as backpropagation to train the network, which is quite time-consuming. To tackle the slow learning speed in autoencoder neural network, we propose a simple and efficient one-class classifier based on extreme learning machine (ELM). The essence of ELM is that the hidden layer need not be tuned and the output weights can be analytically determined, which leads to much faster learning speed. The experimental evaluation conducted on several real-world benchmarks shows that the ELM based one-class classifier can learn hundreds of times faster than autoencoder and it is competitive over a variety of one-class classification methods.

Multiple-Instance Learning via an RBF Kernel-Based Extreme Learning Machine

2017 ◽  
Vol 26 (1) ◽  
pp. 185-195 ◽  
Author(s):  
Jie Wang ◽  
Liangjian Cai ◽  
Xin Zhao
Keyword(s):  
Extreme Learning Machine ◽  
State Of The Art ◽  
Multiple Instance Learning ◽  
Training Data ◽  
Data Sets ◽  
Rbf Kernel ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
Hidden Layer ◽  
Instance Space

AbstractAs we are usually confronted with a large instance space for real-word data sets, it is significant to develop a useful and efficient multiple-instance learning (MIL) algorithm. MIL, where training data are prepared in the form of labeled bags rather than labeled instances, is a variant of supervised learning. This paper presents a novel MIL algorithm for an extreme learning machine called MI-ELM. A radial basis kernel extreme learning machine is adapted to approach the MIL problem using Hausdorff distance to measure the distance between the bags. The clusters in the hidden layer are composed of bags that are randomly generated. Because we do not need to tune the parameters for the hidden layer, MI-ELM can learn very fast. The experimental results on classifications and multiple-instance regression data sets demonstrate that the MI-ELM is useful and efficient as compared to the state-of-the-art algorithms.

OMP-ELM: Orthogonal Matching Pursuit-Based Extreme Learning Machine for Regression

2015 ◽  
Vol 24 (1) ◽  
pp. 135-143 ◽  
Author(s):  
Omer F. Alcin ◽  
Abdulkadir Sengur ◽  
Jiang Qian ◽  
Melih C. Ince
Keyword(s):  
Extreme Learning Machine ◽  
Matching Pursuit ◽  
Computational Cost ◽  
Machine Intelligence ◽  
Orthogonal Matching Pursuit ◽  
Training Data ◽  
Singularity Problem ◽  
Data Set ◽  
Learning Machine ◽  
Hidden Layer

AbstractExtreme learning machine (ELM) is a recent scheme for single hidden layer feed forward networks (SLFNs). It has attracted much interest in the machine intelligence and pattern recognition fields with numerous real-world applications. The ELM structure has several advantages, such as its adaptability to various problems with a rapid learning rate and low computational cost. However, it has shortcomings in the following aspects. First, it suffers from the irrelevant variables in the input data set. Second, choosing the optimal number of neurons in the hidden layer is not well defined. In case the hidden nodes are greater than the training data, the ELM may encounter the singularity problem, and its solution may become unstable. To overcome these limitations, several methods have been proposed within the regularization framework. In this article, we considered a greedy method for sparse approximation of the output weight vector of the ELM network. More specifically, the orthogonal matching pursuit (OMP) algorithm is embedded to the ELM. This new technique is named OMP-ELM. OMP-ELM has several advantages over regularized ELM methods, such as lower complexity and immunity to the singularity problem. Experimental works on nine commonly used regression problems indicate that the investigated OMP-ELM method confirms these advantages. Moreover, OMP-ELM is compared with the ELM method, the regularized ELM scheme, and artificial neural networks.

scholarly journals Fast learning neural network based on texture for Arabic calligraphy identification

2021 ◽  
Vol 21 (3) ◽  
pp. 1794
Author(s):  
Ahmed Kawther Hussein
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Prediction Accuracy ◽  
Input Output ◽  
Fast Learning ◽  
Input And Output ◽  
Learning Machine ◽  
Hidden Layer ◽  
Arabic Calligraphy

<span id="docs-internal-guid-5c723154-7fff-a7b2-3582-b7c2920a9921"><span>Arabic calligraphy is considered a sort of Arabic writing art where letters in Arabic can be written in various curvy or segments styles. The efforts of automating the identification of Arabic calligraphy by using artificial intelligence were less comparing with other languages. Hence, this article proposes using four types of features and a single hidden layer neural network for training on Arabic calligraphy and predicting the type of calligraphy that is used. For neural networks, we compared the case of non-connected input and output layers in extreme learning machine ELM and the case of connected input-output layers in FLN. The prediction accuracy of fast learning machine FLN was superior comparing ELM that showed a variation in the obtained accuracy. </span></span>

Structure Impact Localization Using Emerging Artificial Intelligence Algorithms

2015 ◽  
pp. 103-123
Author(s):  
Qingsong Xu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Learning Algorithm ◽  
Experimental Studies ◽  
Back Propagation ◽  
Damage Localization ◽  
Localization Accuracy ◽  
Fast Learning ◽  
Learning Machine ◽  
Hidden Layer

Extreme learning machine (ELM) is a learning algorithm for single-hidden layer feedforward neural networks. In theory, this algorithm is able to provide good generalization capability at extremely fast learning speed. Comparative studies of benchmark function approximation problems revealed that ELM can learn thousands of times faster than conventional neural network (NN) and can produce good generalization performance in most cases. Unfortunately, the research on damage localization using ELM is limited in the literature. In this chapter, the ELM is extended to the domain of damage localization of plate structures. Its effectiveness in comparison with typical neural networks such as back-propagation neural network (BPNN) and least squares support vector machine (LSSVM) is illustrated through experimental studies. Comparative investigations in terms of learning time and localization accuracy are carried out in detail. It is shown that ELM paves a new way in the domain of plate structure health monitoring. Both advantages and disadvantages of using ELM are discussed.

scholarly journals Fault Diagnosis for Engine Based on Single-Stage Extreme Learning Machine

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Fei Gao ◽  
Jiangang Lv
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Feature Space ◽  
Single Stage ◽  
Training Data ◽  
Learning Machine ◽  
Hidden Layer ◽  
Signal Processing Methods ◽  
Hidden Nodes ◽  
Selection Of

Single-Stage Extreme Learning Machine (SS-ELM) is presented to dispose of the mechanical fault diagnosis in this paper. Based on it, the traditional mapping type of extreme learning machine (ELM) has been changed and the eigenvectors extracted from signal processing methods are directly regarded as outputs of the network’s hidden layer. Then the uncertainty that training data transformed from the input space to the ELM feature space with the ELM mapping and problem of the selection of the hidden nodes are avoided effectively. The experiment results of diesel engine fault diagnosis show good performance of the SS-ELM algorithm.

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

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.

scholarly journals An Effective Turkey Marble Classification System: Convolutional Neural Network with Genetic Algorithm -Wavelet Kernel - Extreme Learning Machine

2021 ◽  
Vol 38 (4) ◽  
pp. 1229-1235
Author(s):  
Derya Avci ◽  
Eser Sert
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Convolutional Neural Network ◽  
Extreme Learning Machine ◽  
Optical Illusions ◽  
Factors Affecting ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
Hidden Layer ◽  
Elm Classifier

Marble is one of the most popular decorative elements. Marble quality varies depending on its vein patterns and color, which are the two most important factors affecting marble quality and class. The manual classification of marbles is likely to lead to various mistakes due to different optical illusions. However, computer vision minimizes these mistakes thanks to artificial intelligence and machine learning. The present study proposes the Convolutional Neural Network- (CNN-) with genetic algorithm- (GA) Wavelet Kernel- (WK-) Extreme Learning Machine (ELM) (CNN–GA-WK-ELM) approach. Using CNN architectures such as AlexNet, VGG-19, SqueezeNet, and ResNet-50, the proposed approach obtained 4 different feature vectors from 10 different marble images. Later, Genetic Algorithm (GA) was used to optimize adjustable parameters, i.e. k, 1, and m, and hidden layer neuron number in Wavelet Kernel (WK) – Extreme Learning Machine (ELM) and to increase the performance of ELM. Finally, 4 different feature vector parameters were optimized and classified using the WK-ELM classifier. The proposed CNN–GA-WK-ELM yielded an accuracy rate of 98.20%, 96.40%, 96.20%, and 95.60% using AlexNet, SequeezeNet, VGG-19, and ResNet-50, respectively.

scholarly journals Sistem Pendukung Keputusan Peramalan Jumlah Kunjungan Pasien Menggunakan Metode Extreme Learning Machine (Studi Kasus : Poli Gigi Rsu Dr. Wahidin Sudiro Husodo Mojokerto)

2015 ◽  
Vol 1 (1) ◽  
pp. 33
Author(s):  
Delia Putri Fardani ◽  
Eto Wuryanto ◽  
Indah Werdiningsih
Keyword(s):  
Decision Support ◽  
Extreme Learning Machine ◽  
Support System ◽  
Training Data ◽  
Number Of Patients ◽  
Testing Data ◽  
Decision Supporting ◽  
Decision Supporting System ◽  
Learning Machine ◽  
Hidden Layer

Abstrak— Penelitian ini bertujuan merancang dan membangun sistem pendukung keputusan untuk meramalkan jumlah kunjungan pasien RSU Dr. Wahidin Sudiro Husodo Kota Mojokerto dengan menggunakan metode Extreme Learning Machine (ELM). Dengan adanya  sistem pendukung keputusan ini direktur Rumah Sakit dapat meramalkan jumlah kunjungan pasien dan membantu dalam pembuatan kebijakan rumah sakit, mengatur sumber daya manusia dan keuangan, serta mendistribusikan sumber daya material dengan benar khususnya pada poli gigi. Dalam rancang bangun sistem pendukung keputusan ini dilakukan dalam beberapa tahap. Tahap yang pertama, pengumpulan data untuk mengidentifikasi inputan yang dibutuhkan dalam penghitungan metode ELM. Tahap kedua, pengolahan data, data dibagi menjadi data training dan data testing dengan komposisi data training sebanyak 80% (463 data) dari total 579 data dan 20% (116 data) sisanya sebagai data testing yang kemudian di normalisasi. Tahap ketiga, peramalan jumlah kunjungan pasien menggunakan metode ELM. Tahap terakhir, perancangan sistem menggunakan sysflow dan pembangunan sistem berbasis desktop serta evaluasi sistem. Hasil penelitian berupa aplikasi sistem pendukung keputusan untuk meramalkan jumlah kunjungan pasien. Dan melalui uji coba menggunakan 116 data testing berdasarkan fungsi aktivasi sigmoid biner dengan jumlah hidden layer sebanyak 7 unit dan Epoch 500 diperoleh hasil optimal MSE sebesar 0.027 Kata Kunci— Sistem Pendukung Keputusan, Peramalan, Jaringan Syaraf Tiruan, Extreme Learning MachineAbstract— In this research, a decision support system to predict the number of patients visit RSU Dr. Wahidin Sudiro Husodo Kota Mojokerto was designed and developed using Extreme Learning Machine (ELM) method which aims to assist director in making decision for the hospital, managing human and financial resource, as well as distributing material resource properly especially in the Department of Dentistry. The design of this decision support system to predict the number of patients visit with ELM method is divided into several stages. The first stage is to identify the input data collection needed in the calculation method of ELM. The next stage is processing the data; the data is divided into training data and testing data and then normalized, in which training data is 80% (452 data) and testing 579 data 20% (116 data). The third stage is problem solving using ELM. The last stage is the design and development of systems using sysflow and desktop-based system that includes the implementation and evaluation of the system. The result of this research is an application of decision supporting system to predict number of patients. By using 116 testing data based on the binary sigmoid activation function using 7 units of hidden layer and 500 Epoch then Optimal MSE value that was obtained is 0.027. Keywords— Decision Supporting System, Prediction, Artificial Neural Network, Extreme Learning Machine

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