Data decomposition based fast reduced kernel extreme learning machine for currency exchange rate forecasting and trend analysis

Das, Pragyan Paramita; Bisoi, Ranjeeta; Dash, P.K.

doi:10.1016/j.eswa.2017.10.053

Cited by 41 publications

(13 citation statements)

References 46 publications

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“…The KELM has demonstrated better nonlinear approximation and stability than ELM. 35,36 The KELM model can be established by solving the following optimization problem given by equation (12)…”

Section: Fundamental Theory Of Kelmmentioning

confidence: 99%

“…30,31 However, for the same input data, the output prediction results of ELM are unstable. In this article, an artificial intelligence algorithm called kernel extreme learning machine (KELM) [31][32][33][34][35][36] is proposed for establishing the mathematical model to predict the displacement of a concrete gravity dam. The KELM is an improved algorithm that introduces kernel functions into the ELM algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Temperature effect modeling in structural health monitoring of concrete dams using kernel extreme learning machines

Kang

Liu

2019

Structural Health Monitoring

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Statistical models have been used for dam health monitoring for many years and have achieved some successful applications. In the statistical model, dam structural response is related to external environmental factors such as reservoir water level, temperature, and irreversible time deformation. For concrete dams, the structural response is affected greatly by the ambient temperature. Therefore, in order to establish a more reliable dam health monitoring model, the temperature effect and modeling method should be further studied. This article presents a dam health monitoring model using measured air temperature for temperature effect simulation based on kernel extreme learning machines. The temperature effect is simulated by long-term air temperature data, and the nonlinear relationship is modeled by kernel extreme learning machines, which is an intelligent machine learning technique with high learning speed and good generalization performance. The proposed dam health monitoring model is verified on a real concrete gravity dam with efficient safety monitoring data. Results show that the proposed approach with a variable set recommended for concrete dam behavior prediction is feasible.

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Section: Fundamental Theory Of Kelmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Temperature effect modeling in structural health monitoring of concrete dams using kernel extreme learning machines

Kang

Liu

2019

Structural Health Monitoring

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“…In models that work with large data mass, this approach becomes feasible due to its more straightforward nature of generating the weights using pseudo-inverse [64] concepts. This approach is used in several contexts in science to solve problems related to the pattern classification [65][66][67], time series forecasting [68][69][70], industry issues [71][72][73][74], health area [75][76][77], among others.…”

Section: Neural Network Neural Network Architecture and Training For mentioning

confidence: 99%

Using Resistin, Glucose, Age and BMI and Pruning Fuzzy Neural Network for the Construction of Expert Systems in the Prediction of Breast Cancer

Araújo

Guimarães

Souza

et al. 2019

MAKE

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Research on predictions of breast cancer grows in the scientific community, providing data on studies in patient surveys. Predictive models link areas of medicine and artificial intelligence to collect data and improve disease assessments that affect a large part of the population, such as breast cancer. In this work, we used a hybrid artificial intelligence model based on concepts of neural networks and fuzzy systems to assist in the identification of people with breast cancer through fuzzy rules. The hybrid model can manipulate the data collected in medical examinations and identify patterns between healthy people and people with breast cancer with an acceptable level of accuracy. These intelligent techniques allow the creation of expert systems based on logical rules of the IF/THEN type. To demonstrate the feasibility of applying fuzzy neural networks, binary pattern classification tests were performed where the dimensions of the problem are used for a model, and the answers identify whether or not the patient has cancer. In the tests, experiments were replicated with several characteristics collected in the examinations done by medical specialists. The results of the tests, compared to other models commonly used for this purpose in the literature, confirm that the hybrid model has a tremendous predictive capacity in the prediction of people with breast cancer maintaining acceptable levels of accuracy with good ability to act on false positives and false negatives, assisting the scientific milieu with its forecasts with the significant characteristic of interpretability of breast cancer. In addition to coherent predictions, the fuzzy neural network enables the construction of systems in high level programming languages to build support systems for physicians’ actions during the initial stages of treatment of the disease with the fuzzy rules found, allowing the construction of systems that replicate the knowledge of medical specialists, disseminating it to other professionals..

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“…In recent years, ELM has been used in various applications such as signal processing [18], image processing [19], [20], medical diagnosis [21], market analysis [22], aviation and aerospace [23], forecasting [24] and others [25]. In signal processing, [18] has applied the ELM algorithm to identify two different EEG (electroencephalogram) signals.…”

Section: Related Workmentioning

confidence: 99%

Regularization Activation Function for Extreme Learning Machine

Ismail¹,

Othman²,

Samsudin³

2019

ijacsa

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Extreme Learning Machine (ELM) algorithm based on single hidden layer feedforward neural networks has shown as the best time series prediction technique. Furthermore, the algorithm has a good generalization performance with extremely fast learning speed. However, ELM facing overfitting problem that can affect the model quality due to the implementation using empirical risk minimization scheme. Therefore, this study aims to improve ELM by introducing an Activation Functions Regularization in ELM called RAF-ELM. The experiment has been conducted in two phases. First, investigating the modified RAF-ELM performance using four types of activation functions are: Sigmoid, Sine, Tribas and Hardlim. In this study, input weight and bias for hidden layers are randomly selected, whereas the best neurons number of hidden layer is determined from 5 to 100. This experiment used UCI benchmark datasets. The number of neurons (99) using Sigmoid activation function shown the best performance. The proposed methods has improved the accuracy performance and learning speed up to 0.016205 MAE and processing time 0.007 seconds respectively compared with conventional ELM and has improved up to 0.0354 MSE for accuracy performance compare with state of the art algorithm. The second experiment is to validate the proposed RAF-ELM using 15 regression benchmark dataset. RAF-ELM has been compared with four neural network techniques namely conventional ELM, Back Propagation, Radial Basis Function and Elman. The results show that RAF-ELM technique obtain the best performance compared to other techniques in term of accuracy for various time series data that come from various domain.

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Data decomposition based fast reduced kernel extreme learning machine for currency exchange rate forecasting and trend analysis

Cited by 41 publications

References 46 publications

Temperature effect modeling in structural health monitoring of concrete dams using kernel extreme learning machines

Temperature effect modeling in structural health monitoring of concrete dams using kernel extreme learning machines

Using Resistin, Glucose, Age and BMI and Pruning Fuzzy Neural Network for the Construction of Expert Systems in the Prediction of Breast Cancer

Regularization Activation Function for Extreme Learning Machine

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