Performance evaluation of radial basis function networks based on tree seed algorithm

Muneeswaran, V.; Rajasekaran, M. Pallikonda

doi:10.1109/iccpct.2016.7530267

Cited by 26 publications

(4 citation statements)

References 11 publications

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“…At present, research on PV cell identification mainly uses heuristic algorithms for parameter identification, but there is less processing and research on measured data. This paper proposes meta-heuristic algorithms (MhAs) for PV cell parameter identification based on RBF, which de-noises, trains, and predicts through RBF data [22] and then uses MhAs for parameter identification processing [23]. The final results also fully demonstrate that the extraction accuracy after data de-noising and prediction is higher than that of the original data.…”

Section: Introductionmentioning

confidence: 90%

Radial Basis Function Based Meta-Heuristic Algorithms for Parameter Extraction of Photovoltaic Cell

He¹,

Xi²,

Li³

et al. 2023

Processes

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Accurate parameter estimation of photovoltaic (PV) cells is crucial for establishing a reliable cell model. Based on this, a series of studies on PV cells can be conducted more effectively to improve power output; an accurate model is also crucial for the operation and control of PV systems. However, due to the high nonlinearity of the cell and insufficient measured current and voltage data, traditional PV parameter identification methods are difficult to solve this problem. This article proposes a parameter identification method for PV cell models based on the radial basis function (RBF). Firstly, RBF is used to de-noise and predict the data to solve the current problems in the parameter identification field of noise data and insufficient data. Then, eight prominent meta-heuristic algorithms (MhAs) are used to identify the single-diode model (SDM), double-diode model (DDM), and three-diode model (TDM) parameters of PV cells. By comparing the identification accuracy of the three models in two datasets in detail, the final results show that this method can effectively achieve parameter extraction, with advantages such as high extraction accuracy and stability, greatly improving the accuracy and reliability of parameter identification. Especially in the TDM, the I-V data and P-V data obtained from the PV model established through the identified parameters have very high fitting accuracy with the measured I-V and P-V data, reaching 99.58% and 99.65%, respectively. The research can effectively solve the low accuracy problem caused by insufficient data and noise data in the traditional method of identifying PV cells and can greatly improve the accuracy of PV cell modeling. It is of great significance for the operation and control of PV systems.

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Section: Introductionmentioning

confidence: 90%

Radial Basis Function Based Meta-Heuristic Algorithms for Parameter Extraction of Photovoltaic Cell

He¹,

Xi²,

Li³

et al. 2023

Processes

View full text Add to dashboard Cite

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“…This paper deals with the control of both subsystems described by (17), i.e., m = 3 and n = 6. However, due to the complex environment and disturbances, the system 17is difficult to use if the functions f and g i cannot be accurately known.…”

Section: Rnn-based Prediction Modelsmentioning

confidence: 99%

“…As a nonconventional method, the neural network (NN) does not explicitly need an accurate mathematical model [16][17][18][19]. Based on that, some works on NN-based MPC have improved modeling performance for many different nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Recurrent Neural Network-Based Model Predictive Control for Multiple Unmanned Quadrotor Formation Flight

Zhang

Sun

Deng

2019

International Journal of Aerospace Engineering

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This paper presents a dynamical recurrent neural network- (RNN-) based model predictive control (MPC) structure for the formation flight of multiple unmanned quadrotors. A distributed hierarchical control system with the translation subsystem and rotational subsystem is proposed to handle the formation-tracking problem for each quadrotor. The RNN-based MPC is proposed for each subsystem, where the RNN is introduced as the predictive model in MPC. And to improve the modeling accuracy, an adaptive updating law is developed to tune weights online for the RNN. Besides, the adaptive differential evolution (DE) algorithm is utilized to solve the optimization problem for MPC. Furthermore, the closed-loop stability is analyzed; meanwhile, the convergence of the DE algorithm is discussed as well. Finally, some simulation examples are provided to illustrate the validity of the proposed control structure.

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“…Zhao [64] utilized residual vectors and TSA to identify the structural damage. Muneeswaran [36] developed a performance evaluation method for the radial basis function neural network based on TSA. However, TSA is affected by the search trend (ST) which leads to the update falling into local optimum.…”

Section: Introductionmentioning

confidence: 99%

Forecasting short-term electric load using extreme learning machine with improved tree seed algorithm based on Lévy flight

Chen¹,

Przystupa²,

Ye³

et al. 2022

Eksploatacja i Niezawodność – Maintenance and Reliability

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In recent years, forecasting has received increasing attention since it provides an important basis for the effective operation of power systems. In this paper, a hybrid method, composed of kernel principal component analysis (KPCA), tree seed algorithm based on Lévy flight (LTSA) and extreme learning machine (ELM), is proposed for short-term load forecasting. Specifically, the randomly generated weights and biases of ELM have a significant impact on the stability of prediction results. Therefore, in order to solve this problem, LTSA is utilized to obtain the optimal parameters before the prediction process is executed by ELM, which is called LTSA-ELM. Meanwhile, the input data is extracted by KPCA considering the sparseness of the electric load data and used as the input of LTSA-ELM model. The proposed method is tested on the data from European network on intelligent technologies (EUNITE) and experimental results demonstrate the superiority of the proposed approaches compared to the other methods involved in the paper.

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Performance evaluation of radial basis function networks based on tree seed algorithm

Cited by 26 publications

References 11 publications

Radial Basis Function Based Meta-Heuristic Algorithms for Parameter Extraction of Photovoltaic Cell

Radial Basis Function Based Meta-Heuristic Algorithms for Parameter Extraction of Photovoltaic Cell

Recurrent Neural Network-Based Model Predictive Control for Multiple Unmanned Quadrotor Formation Flight

Forecasting short-term electric load using extreme learning machine with improved tree seed algorithm based on Lévy flight

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