Non linear system identification using kernel based exponentially extended random vector functional link network

Chakravorti, Tatiana; Satyanarayana, Penke

doi:10.1016/j.asoc.2020.106117

Cited by 24 publications

(8 citation statements)

References 48 publications

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“…Manually determin-ing the optimal range of hidden nodes and the optimal activation function is challenging. Chakraavorti et al [28] proposed kernel exponentially extended random vector functional link network (KERVFLN) for non-linear system identification. Here, the kernel function has been used to increase the stability of standard RVFL, and the inputs are extended using a trigonometric function that improves the generalization performance of the KERVFLN model.…”

Section: Kernelized Rvfl Modelsmentioning

confidence: 99%

“…Then, a grid search is conducted to select the hyperparameters, number of hidden nodes, and regularization strength, according to the forecasting errors on the validation set. Choosing the optimal activation function and number of hidden neurons is also an challenging job so some researchers adopt incremental learning techniques [78] and kernel trick [28] to avoid these issues. Table 1 shows the list of activation functions used in the literature.…”

Section: Hyper-parameters Optimization For Single-layer Rvflmentioning

confidence: 99%

“…After that, the RVFL model got the attention of researchers from diverse domains due to its simple architecture, fast training speed, and universal approximation capability. The shallow RVFL model has been employed successfully in several domains, i.e., forecasting [25,26,27], non-linear system identification [28], function approximation [22], classification and regression problem [29,30,31], etc.…”

Section: Introductionmentioning

confidence: 99%

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Random vector functional link network: recent developments, applications, and future directions

Malik¹,

Gao²,

Ganaie³

et al. 2022

Preprint

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Neural networks have been successfully employed in various domain such as classification, regression and clustering, etc. Generally, the back propagation (BP) based iterative approaches are used to train the neural networks, however, it results in the issues of local minima, sensitivity to learning rate and slow convergence. To overcome these issues, randomization based neural networks such as random vector functional link (RVFL) network have been proposed. RVFL model has several characteristics such as fast training speed, simple architecture, and universal approximation capability, that make it a viable randomized neural network. This article presents the comprehensive review of the development of RVFL model, which can serve as the extensive summary for the beginners as well as practitioners. We discuss the shallow RVFL, ensemble RVFL, deep RVFL and ensemble deep RVFL models. The variations, improvements and applications of RVFL models are discussed in detail. Moreover, we discuss the different hyperparameter optimization techniques followed in the literature to improve the generalization performance of the RVFL model. Finally, we give potential future research directions/opportunities that can inspire the researchers to improve the RVFL architecture further.

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Section: Kernelized Rvfl Modelsmentioning

confidence: 99%

Section: Hyper-parameters Optimization For Single-layer Rvflmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Random vector functional link network: recent developments, applications, and future directions

Malik¹,

Gao²,

Ganaie³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…System identification is theory of establishing the mathematical models of dynamical systems by measuring the system inputs and outputs 1‐3 . System modeling and parameter estimation are the basis of all the control problems, 4‐9 so it is significant to build an appropriate model for system prediction and control 10‐15 .…”

Section: Introductionmentioning

confidence: 99%

Identification of the nonlinear systems based on the kernel functions

Ding

Yang

2021

Intl J Robust & Nonlinear

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Constructing an appropriate membership function is significant in fuzzy logic control. Based on the multi‐model control theory, this article constructs a novel kernel function which can implement the fuzzification and defuzzification processes and reflect the dynamic quality of the nonlinear systems accurately. Then we focus on the identification problems of the nonlinear systems based on the kernel functions. Applying the hierarchical identification principle, we present the hierarchical stochastic gradient algorithm for the nonlinear systems. Meanwhile, the one‐dimensional search methods are proposed to solve the problem of determining the optimal step sizes. In order to improve the parameter estimation accuracy, we propose the hierarchical multi‐innovation forgetting factor stochastic gradient algorithm by introducing the forgetting factor and using the multi‐innovation identification theory. The simulation example is provided to test the proposed algorithms from the aspects of parameter estimation accuracy and prediction performance.

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“…Using these AI approaches, the outflow from a barrage situated at the downstream end of a river can be predicted with more precision. Different researchers have been used the intelligent machine learning and soft computing techniques to handle the complex nonlinearity phenomenon in water resources and other various domains [4][5][6][7][8][9][10][11][12][13][14][15][16][17]. The ANN techniques are applied successfully in numerous interdisciplinary domain [15,[18][19].…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Network and Genetic Algorithm Based Artificial Neural Network Models for River Flow Prediction

Yadav¹,

Prasad²,

Mojjada³

et al. 2020

RIA

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In hydrology and water resource engineering, water flow forecasting is of great importance for getting the information about the river engineering, dam structure design and waterrelated inflow demand management. In order to prevent flooding on the downstream side of the river during the rainy season, sufficient outflow from a barrage should be maintained. It is very difficult to predict the desired water flow using physically-based models and conventional regression-based methods due to the nonlinear and fuzzy nature of hydrological activity and scarcity of relevant data. These traditional methods are incapable to handle the complex non-linearity and non-stationarity process of water flow. Thus, the aim of this study is to develop intelligent hybrid artificial intelligence model, namely genetic algorithm based Artificial Neural Network (GA-ANN) for monthly Water Flow prediction in the Mahanadi river system. All parameters associated with the artificial neural network (ANN) model are optimized simultaneous automatically using Genetic Algorithm (GA) for prediction of the Water flow. Twenty years monthly data from Mahanadi river in India has collected for the development of this GA-ANN model. The hydro-climatical parameters like Rainfall, Water Level, Sediment yield and Temperature are used for the development of the ANN prediction model of Water Flow at Tikarapara gauging station which is extreme last downstream station in Mahanadi River basin, India. The performances of the GA-ANN model were compared with Artificial Neural Network (ANN) model for checking the estimation capability of the model. The obtained results revealed that the proposed novel GA-ANN model is capable to predict river flow with satisfactory performances and provided better results than the ANN model. This modelling approach can be potentially used for prediction of water flow discharge in the river system where measurement of water flow is unavailable.

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Non linear system identification using kernel based exponentially extended random vector functional link network

Cited by 24 publications

References 48 publications

Random vector functional link network: recent developments, applications, and future directions

Random vector functional link network: recent developments, applications, and future directions

Identification of the nonlinear systems based on the kernel functions

Application of Artificial Neural Network and Genetic Algorithm Based Artificial Neural Network Models for River Flow Prediction

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