Modified online sequential extreme learning machine algorithm using model predictive control approach

Wibawa, Ig. Prasetya Dwi; Machbub, Carmadi; Rohman, Arief Syaichu; Hidayat, Egi

doi:10.1016/j.iswa.2023.200191

Cited by 4 publications

(1 citation statement)

References 91 publications

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“…g is the activation function. In this paper's case, in order to improve prediction accuracy, it is not only need to know whether the input signal has an important impact on the result after weighting and offsetting, but also need to identify whether the impact is positive or negative and how much they are, so comparing to previous works, a new form of activation function (Hyperbolic tangent function) was chosen in this paper [44][45][46][47][48][49]:…”

Section: Elmmentioning

confidence: 99%

A novel data-driven method for fuel-consumption prediction based on fast converged kernel extreme learning machine

Wang

2023

Meas. Sci. Technol.

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An accurate fuel consumption prediction is significant to fuel-economy-oriented optimization, which can improve vehicle fuel economy. Based on the data-driven prediction framework, this paper proposes a Real-time Fuel Consumption Prediction Model (RFCPM) using Kernel Extreme Learning Machine (KELM) which is optimized by Fast Converged Grey Wolf Algorithm (FCGWA). A new steering function of FCGWA and a new activation function for KELM are presented to ensure the fast converge speed and higher accuracy performance. First, the characteristic variables of RFCPM are selected by reference to the model-based fuel consumption prediction method. Second, a KELM is adopted to predict fuel consumption. Third, FCGWA is adopted to select the best parameters of KELM using k-fold cross-validation method. Finally, the best model is chosen through real-vehicle test. Test results are compared with the original ELM and the wildly used WNN. The experiment shows that the proposed method significantly outperforms the original ELM and WNN in terms of both prediction accuracy and training time.

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Section: Elmmentioning

confidence: 99%

A novel data-driven method for fuel-consumption prediction based on fast converged kernel extreme learning machine

Wang

2023

Meas. Sci. Technol.

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Parallel ensemble of a randomization-based online sequential neural network for classification problems using a frequency criterion

Gelvez-Almeida,

Barrientos,

Vilches-Ponce

et al. 2024

Sci Rep

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Randomization-based neural networks have gained wide acceptance in the scientific community owing to the simplicity of their algorithm and generalization capabilities. Random vector functional link (RVFL) networks and their variants are a class of randomization-based neural networks. RVFL networks have shown promising results in classification, regression, and clustering problems. For real-world applications, learning algorithms that can train with new samples over previous results are necessary because of to the constant generation of problems related to large-scale datasets. Various online sequential algorithms, commonly involving an initial learning phase followed by a sequential learning phase, have been proposed to address this issue. This paper presents a training algorithm based on multiple online sequential random vector functional link (OS-RVFL) networks for large-scale databases using a shared memory architecture. The training dataset is distributed among p OS-RVFL networks, which are trained in parallel using p threads. Subsequently, the test dataset samples are classified using each trained OS-RVFL network. Finally, a frequency criterion is applied to the results obtained from each OS-RVFL network to determine the final classification. Additionally, an equation was derived to reasonably predict the total training time of the proposed algorithm based on the learning time in the initial phase and the time scaling factor compared to the sequential learning phase. The results demonstrate a drastic reduction in training time because of data distribution and an improvement in accuracy because of the adoption of the frequency criterion.

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Case Study: Jakarta AQI Classification Using Extreme Learning Machine Method with Oversampling Technique

Sukiman,

Kallista,

Dwi Wibawa

et al. 2023

2023 IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob)

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Modified online sequential extreme learning machine algorithm using model predictive control approach

Cited by 4 publications

References 91 publications

A novel data-driven method for fuel-consumption prediction based on fast converged kernel extreme learning machine

A novel data-driven method for fuel-consumption prediction based on fast converged kernel extreme learning machine

Parallel ensemble of a randomization-based online sequential neural network for classification problems using a frequency criterion

Case Study: Jakarta AQI Classification Using Extreme Learning Machine Method with Oversampling Technique

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