scholarly journals Neural Networks with Improved Extreme Learning Machine for Demand Prediction of Bike-sharing

2021 ◽  
Author(s):  
Fan Wu ◽  
Si Hong ◽  
Wei Zhao ◽  
Xiaoyan Wang ◽  
Xun Shao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Extreme Learning Machine ◽  
User Satisfaction ◽  
Back Propagation ◽  
Feedforward Neural Networks ◽  
Demand Prediction ◽  
Bike Sharing ◽  
Learning Machine ◽  
Hidden Layer ◽  
The Cost

AbstractAccurate demand prediction of bike-sharing is an important prerequisite to reducing the cost of scheduling and improving the user satisfaction. However, it is a challenging issue due to stochasticity and non-linearity in bike-sharing systems. In this paper, a model called pseudo-double hidden layer feedforward neural networks is proposed to approximately predict actual demands of bike-sharing. Specifically, to overcome limitations in traditional back-propagation learning process, an algorithm, an extreme learning machine with improved particle swarm optimization, is designed to construct learning rules in neural networks. The performance is verified by comparing with other learning algorithms on the dataset of Streeter Dr bike-sharing station in Chicago.

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.

AN IMPROVEMENT OF EXTREME LEARNING MACHINE FOR COMPACT SINGLE-HIDDEN-LAYER FEEDFORWARD NEURAL NETWORKS

2008 ◽  
Vol 18 (05) ◽  
pp. 433-441 ◽  
Author(s):  
HIEU TRUNG HUYNH ◽  
YONGGWAN WON ◽  
JUNG-JA KIM
Keyword(s):  
Neural Networks ◽  
Extreme Learning Machine ◽  
High Speed ◽  
Learning Algorithm ◽  
Differential Evolution Algorithm ◽  
Feedforward Neural Networks ◽  
Analytical Determination ◽  
Data Sets ◽  
Learning Machine ◽  
Hidden Layer

Recently, a novel learning algorithm called extreme learning machine (ELM) was proposed for efficiently training single-hidden-layer feedforward neural networks (SLFNs). It was much faster than the traditional gradient-descent-based learning algorithms due to the analytical determination of output weights with the random choice of input weights and hidden layer biases. However, this algorithm often requires a large number of hidden units and thus slowly responds to new observations. Evolutionary extreme learning machine (E-ELM) was proposed to overcome this problem; it used the differential evolution algorithm to select the input weights and hidden layer biases. However, this algorithm required much time for searching optimal parameters with iterative processes and was not suitable for data sets with a large number of input features. In this paper, a new approach for training SLFNs is proposed, in which the input weights and biases of hidden units are determined based on a fast regularized least-squares scheme. Experimental results for many real applications with both small and large number of input features show that our proposed approach can achieve good generalization performance with much more compact networks and extremely high speed for both learning and testing.

scholarly journals Extreme Learning Machines on High Dimensional and Large Data Applications: A Survey

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Jiuwen Cao ◽  
Zhiping Lin
Keyword(s):  
Linear System ◽  
Video Processing ◽  
Large Data ◽  
Feedforward Neural Networks ◽  
High Dimensional ◽  
Recent Developments ◽  
Learning Machine ◽  
Hidden Layer ◽  
Hidden Neurons ◽  
The Cost

Extreme learning machine (ELM) has been developed for single hidden layer feedforward neural networks (SLFNs). In ELM algorithm, the connections between the input layer and the hidden neurons are randomly assigned and remain unchanged during the learning process. The output connections are then tuned via minimizing the cost function through a linear system. The computational burden of ELM has been significantly reduced as the only cost is solving a linear system. The low computational complexity attracted a great deal of attention from the research community, especially for high dimensional and large data applications. This paper provides an up-to-date survey on the recent developments of ELM and its applications in high dimensional and large data. Comprehensive reviews on image processing, video processing, medical signal processing, and other popular large data applications with ELM are presented in the paper.

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 Online Regularized and Kernelized Extreme Learning Machines with Forgetting Mechanism

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Xinran Zhou ◽  
Zijian Liu ◽  
Congxu Zhu
Keyword(s):  
Extreme Learning Machine ◽  
Feedforward Neural Networks ◽  
Matrix Inversion ◽  
Time Varying ◽  
Extreme Learning Machines ◽  
Learning Machines ◽  
Matrix Expansion ◽  
Learning Machine ◽  
Hidden Layer ◽  
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.

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.

Lithofacies Recognition Based on Extreme Learning Machine

2012 ◽  
Vol 241-244 ◽  
pp. 1762-1767 ◽  
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

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.

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