A fast learning algorithm based on extreme learning machine for regular fuzzy neural network

He, Chunmei; Liu, Yaqi; Tong, Yao; Xu, Fanhua; Hu, Yanyun; Zheng, Jinhua

doi:10.3233/jifs-18046

Cited by 6 publications

(5 citation statements)

References 17 publications

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“…A three-layer configuration consisting of an import layer, an implicit layer, and an export layer is adopted by the extreme learning machine, which acts on a stochastic foundation. 40 The joint weights are generated stochastically in the operation from the import layer to the implicit layer, as well as the thresholds of those neurons in the implicit layer. 41 With the structure illustrated in Figure 1, the process goes as follows.…”

Section: Extreme Learning Machinementioning

confidence: 99%

“…It has advantages such as a lower training factor, quicker acquisition, and stronger proficiency in generalization. A three-layer configuration consisting of an import layer, an implicit layer, and an export layer is adopted by the extreme learning machine, which acts on a stochastic foundation . The joint weights are generated stochastically in the operation from the import layer to the implicit layer, as well as the thresholds of those neurons in the implicit layer .…”

Section: Theoretical Analysismentioning

confidence: 99%

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Novel Improved Particle Swarm Optimization-Extreme Learning Machine Algorithm for State of Charge Estimation of Lithium-Ion Batteries

Zhang

Wang

et al. 2022

Ind. Eng. Chem. Res.

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Incisively estimating the state of charge (SOC) of lithium-ion batteries is essential to ensure the safe and stable operation of a battery management system. Neural network methods do not depend on a specific lithium-ion battery model and are able to mirror the lithium-ion battery’s nonlinear relationships, thus receiving widespread attention; however, traditional neural network methods exhibit a long training time and low accuracy in estimating SOC. This paper presents an original algorithm of an improved particle swarm optimization (IPSO) extreme learning machine (ELM) neural network, improving the particle swarm algorithm using nonlinear inertia weights to enhance the global optimization seeking capability of ELM for solving the problem of poor precision of previous battery SOC estimation. The lithium-ion battery voltage and current are the input variables of the model, while SOC is used as the output variable. The results of the experiments revealed that the root-mean-square estimation errors of the proposed IPSO-ELM algorithm for SOC are within 0.31, 0.32, and 0.14% of the root mean square under the hybrid pulse power characteristic (HPPC), the Beijing bus dynamic stress test (BBDST), and the dynamic stress test (DST) operating conditions. Compared with the prediction results of the PSO-ELM and ELM neural networks, the simulation results prove that the SOC optimization method in this paper possesses superior precision and overcomes the shortcomings of traditional neural networks.

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Section: Extreme Learning Machinementioning

confidence: 99%

Section: Theoretical Analysismentioning

confidence: 99%

Novel Improved Particle Swarm Optimization-Extreme Learning Machine Algorithm for State of Charge Estimation of Lithium-Ion Batteries

Zhang

Wang

et al. 2022

Ind. Eng. Chem. Res.

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“…Based on the optimization technique Particle Swarm Optimization (PSO), proposed a model for learning named as PSO-FLN is proposed by M.H.Ali et.al for Fast Learning Network(FLN) [26] that has been experimented with the intrusion detection system dataset KDD99 which outperforms well in all aspect. Using the Extreme Learning Machine (ELM) as a baseline, an algorithm to perform fast learning is applied on the RFNN (Regular Fuzzy Neural Network) is proposed [27] and a new fast learning method (FLM) has been presented for feedforward neural networks [28]. Next, based on the concept of Adaptive Skipping, a new and fast training approach for ANN (Artificial Neural Network) is instituted by presenting the input samples for training randomly [3][5] and based on the fuzzy system [29].…”

Section: Related Studymentioning

confidence: 99%

Multi-Disease Classification Model Using Strassen’s Half of Threshold (SHoT) Training Algorithm in Healthcare Sector

et al. 2021

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In healthcare industry, Neural Network has attained a milestone in solving many real-life classification problems varies from very simple to complex and from linear to non-linear. To improve the training process by reducing the training time, Adaptive Skipping Training algorithm named as Half of Threshold (HOT) has been proposed. To perform the fast classification and also to improve the computational efficiency such as accuracy, error rate, etc., the highlighted characteristics of proposed HOT algorithm has been integrated with Strassen's matrix multiplication algorithm and derived a novel, hybrid and computationally efficient algorithm for training and validating the neural network named as Strassen's Half of Threshold (SHoT) Training Algorithm. The experimental outcome based on the simulation demonstrated that the proposed SHOT algorithm outperforms both BPN and HOT algorithm in terms of training time and its efficiency on various dataset such as such as Hepatitis, SPeCT, Heart, Liver Disorders, Breast Cancer Wisconsin (Diagnostic), Drug Consumption, Cardiotocography, Splice-junction Gene Sequences and Thyroid Disease dataset that are extracted from Machine Learning Dataset Repository of UCI. It can be integrated with any type of supervised training algorithm.

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“…T2FL has shown better performance than T1FL (Deng et al 2020c ; He et al 2019 ; Mohammadzadeh and Kumbasar 2020a ; Shi et al 2020 ; Son et al 2020 ). T2F-NNs are divided into feedforward and recurrent, Mamdani (Linguistic) (Ayala et al 2020 ) and TSK (Tim Oliver Heinz 2017 ) and finally interval and general, in different categories.…”

Section: T2f-nnsmentioning

confidence: 99%

“…In addition to structure, the learning method is also effective in estimation performance of FNNs. Various optimization methods have been applied on the tuning of the both parameters and rules such as particle swarm optimization (Deng et al 2020a ; Kacimi et al 2020 ), quantum-inspired differential evolution (Su and Yang 2011 ; Deng et al xxxx; Deng et al 2020b ), differential evolution (Deng et al 2020c ), extreme learning approach (He et al 2019 ), fractional-order learning rules (Mohammadzadeh and Kumbasar 2020a ), consensus learning (Shi et al 2020 ).…”

Section: Introductionmentioning

confidence: 99%

A review on type-2 fuzzy neural networks for system identification

2021

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In many engineering problems, the systems dynamics are uncertain, and then, the accurate dynamic modeling is required. Type-2 fuzzy neural networks (T2F-NNs) are extensively used in system identification problems, because of their strong estimation capability. In this paper, the application of T2F-NNs is reviewed and classified. First, an introduction to the principles of system identification, including how to extract data from a system, persistency of excitation, preprocessing of information and data, removal of outlier data, and sorting of data to learn the T2F-NNs, is presented. Then, various learning methods for structure and parameters of the T2F-NNs are reviewed and analyzed. A number of different T2F-NNs that have been used to system identification are reviewed, and their disadvantages and advantages are described. Also, their efficiency in different applications is reviewed. Finally, we will look at the horizon ahead in this issue and analyze its challenges.

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A fast learning algorithm based on extreme learning machine for regular fuzzy neural network

Cited by 6 publications

References 17 publications

Novel Improved Particle Swarm Optimization-Extreme Learning Machine Algorithm for State of Charge Estimation of Lithium-Ion Batteries

Novel Improved Particle Swarm Optimization-Extreme Learning Machine Algorithm for State of Charge Estimation of Lithium-Ion Batteries

Multi-Disease Classification Model Using Strassen’s Half of Threshold (SHoT) Training Algorithm in Healthcare Sector

A review on type-2 fuzzy neural networks for system identification

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