ELM with guaranteed performance for online approximation of dynamical systems

Grzeidak, Emerson; Vargas, José A. R.; Alfaro, Sadek Crisóstomo Absi

doi:10.1007/s11071-017-3966-3

Cited by 20 publications

(5 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ELM is a new type of fast deep learning algorithm proposed in 2004 [14]. ELM is a typical single hidden layer feedforward neural network structure (Figure 1), ELM algorithm can adaptively set the number of hidden nodes, and random assignment between the input layer and the hidden layer connection weight and partial.…”

Section: Extreme Learning Machine Modelmentioning

confidence: 99%

Application of grey relational analysis and extreme learning machine method for predicting silicon content of molten iron in blast furnace

Chen

Kong

Wang

et al. 2018

Ironmaking & Steelmaking

View full text Add to dashboard Cite

Controlling the molten iron temperature plays an important role in the iron and steelmaking industry. The change of silicon content is adopted to reflect the temperature, however , the prediction of silicon content has been one of the hot and difficult problems. In this paper, a new model based on gray relational analysis (GRA) and extreme learning machine (ELM) is developed. Firstly, the GRA is used to get the high correlation indexes with the silicon content. Then the relevant indicators are taken as input and the silicon content is taken as output. The ELM model is constructed and the model is trained. Based on this, the silicon content is predicted. The results show that the hit rate reaches 87%(the error is less than 0.10). Compared with the traditional backpropagation or radial basis function neural network , this model has higher hit rate and faster running speed.

show abstract

Section: Extreme Learning Machine Modelmentioning

confidence: 99%

Application of grey relational analysis and extreme learning machine method for predicting silicon content of molten iron in blast furnace

Chen

Kong

Wang

et al. 2018

Ironmaking & Steelmaking

View full text Add to dashboard Cite

show abstract

“…However, OS-ELM considers that the value of the new and old training samples is equal, and the equal weight of the training sample fails to highlight the role of the new training sample [3,14]. Moreover, as soon as the new training samples are obtained, ELM updates the weights of the network.…”

Section: Literature Review 21 Review Of Os-elmmentioning

confidence: 99%

Fuzzy Multi-Objective Optimization Algorithms for Solving Multi-Mode Automated Guided Vehicles by Considering Machine Break Time and Artificial Neural Network

Nabovati¹,

Haleh²,

Vahdani³

2018

NNW

View full text Add to dashboard Cite

Abstract:As an improved algorithm of standard extreme learning machine, online sequential extreme learning machine achieves excellent classification and regression performance. However, online sequential extreme learning machine gives the same weight to the old and new training samples, and fails to highlight the importance of the new training samples. At the same time, the algorithm updates the network weights after obtaining the new training samples. This network weight updating mode lacks flexibility and increases unnecessary computation. This paper proposes an adaptive online sequential extreme learning machine with an effective sample updating mechanism. The new and old samples are given different weights. The effect of new training samples on the algorithm is further enhanced, which can further improve the regression prediction ability of extreme learning machine. At the same time, an improved artificial bee colony algorithm is proposed and used to optimize the parameters of the adaptive online sequential extreme learning machine. The stability and convergence property of proposed prediction method are proved. The actual collected short-term wind speed time series is used as the research object and verify the prediction perfromance of the proposed method. Multi step prediction simulation of short-term wind speed is performed out. Compared with other prediction methods, the simulation results show that the proposed approach has higher prediction accuracy and reliability performance, meanwhile improve the performance indicators.

show abstract

“…Chaotic and hyperchaotic systems have been used in most diverse contexts, including nonlinear identification [27]- [29], observation and control [30], [31], economy [32], [33], welding [34], [35], and secure communication [11], [21], [36], [37], [39]- [42]. In particular, chaos-based cryptography is a very active research topic in the literature, which is motivated by the pseudo-random behavior observed in chaotic systems.…”

Section: Introductionmentioning

confidence: 99%

Secure Communication Based on Hyperchaotic Underactuated Projective Synchronization

Gularte¹,

Alves²,

Vargas³

et al. 2021

IEEE Access

View full text Add to dashboard Cite

This paper proposes a scheme for secure telecommunication based on the projective synchronization of hyperchaotic systems. The design of the synchronizer considers the presence of disturbances to increase the robustness of the method. The main advantage of the proposed approach lies in that only two control inputs are required to synchronize the master and slave systems. Hence, the control structure is simple, which, in turn, simplifies the applications. Based on Lyapunov theory, the proposed approach ensures the finite-time convergence of the synchronization error to a small neighborhood of the origin, even in the presence of disturbances in all states. Simulations to both show the advantages and applications of the proposed approach were also accomplished to validate the theory.

show abstract

ELM with guaranteed performance for online approximation of dynamical systems

Cited by 20 publications

References 32 publications

Application of grey relational analysis and extreme learning machine method for predicting silicon content of molten iron in blast furnace

Application of grey relational analysis and extreme learning machine method for predicting silicon content of molten iron in blast furnace

Fuzzy Multi-Objective Optimization Algorithms for Solving Multi-Mode Automated Guided Vehicles by Considering Machine Break Time and Artificial Neural Network

Secure Communication Based on Hyperchaotic Underactuated Projective Synchronization

Contact Info

Product

Resources

About