Wavelet Network: Online Sequential Extreme Learning Machine for Nonlinear Dynamic Systems Identification

Salih, Dhiadeen M.; Noor, Samsul Bahari Mohd; Merhaban, Mohammad Hamiruce; Kamil, Raja

doi:10.1155/2015/184318

Cited by 7 publications

(2 citation statements)

References 22 publications

(39 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The ability to eradicate noise as well as the adaptive nature due to inherent intelligence of the wavelet networks could be used to suppress the adverse effect of the high switching rate of the sliding mode without or no significant interference with the mathematical model of the system. Apart from that others are enhanced accuracy, lowering of computing duration as well as the size of memory [17,25,26].…”

Section: Reason Behind the Combinationmentioning

confidence: 99%

A New Method towards Achieving FES-Induced Movement

Ahmed¹,

Huq²,

Ibrahim³

2018

IJIE

View full text Add to dashboard Cite

Section: Reason Behind the Combinationmentioning

confidence: 99%

A New Method towards Achieving FES-Induced Movement

Ahmed¹,

Huq²,

Ibrahim³

2018

IJIE

View full text Add to dashboard Cite

“…In addition, some new adaptive growth methods of hidden nodes were proposed, including AG-ELM [9] and D-ELM [10]. Apart from optimization constraints of ELM, ELM has a wide range of applications in data classification [11], nonlinear dynamic systems identification [12], pattern recognition [13][14][15], expert diagnosis [16], medical diagnosis [17], modelling permeability prediction [18], expert target recognition [19], human face recognition [20], and prediction interval estimation of electricity markets [21]. However, there are still some problems that need to be studied.…”

Section: Introductionmentioning

confidence: 99%

Theory and Numerical Analysis of Extreme Learning Machine and Its Application for Different Degrees of Defect Recognition of Hoisting Wire Rope

Zhao

Zhang

2018

Shock and Vibration

View full text Add to dashboard Cite

An improved classification approach is proposed to solve the hot research problem of some complex multiclassification samples based on extreme learning machine (ELM). ELM was proposed based on the single-hidden layer feed-forward neural network (SLFNN). ELM is characterized by the easier parameter selection rules, the faster converge speed, the less human intervention, and so on. In order to further improve the classification precision of ELM, an improved generation method of the network structure of ELM is developed by dynamically adjusting the number of hidden nodes. The number change of the hidden nodes can serve as the computational updated step length of the ELM algorithm. In this paper, the improved algorithm can be called the variable step incremental extreme learning machine (VSI-ELM). In order to verify the effect of the hidden layer nodes on the performance of ELM, an open-source machine learning database (University of California, Irvine (UCI)) is provided by the performance test data sets. The regression and classification experiments are used to study the performance of the VSI-ELM model, respectively. The experimental results show that the VSI-ELM algorithm is valid. The classification of different degrees of broken wires is now still a problem in the nondestructive testing of hoisting wire rope. The magnetic flux leakage (MFL) method of wire rope is an efficient nondestructive method which plays an important role in safety evaluation. Identifying the proposed VSI-ELM model is effective and reliable for actually applying data, and it is used to identify the classification problem of different types of samples from MFL signals. The final experimental results show that the VSI-ELM algorithm is of faster classification speed and higher classification accuracy of different broken wires.

show abstract