Respiratory Motion Prediction with Random Vector Functional Link (RVFL) Based Neural Networks

Rasheed, Asad; Adebisi, Abdulyekeen T.; Veluvolu, Kalyana C.

doi:10.1088/1742-6596/1626/1/012022

Cited by 6 publications

(8 citation statements)

References 11 publications

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“…Moreover, in [49], several scientific questions were addressed, such as the relationship between the performance of the RVFL model and the rank of the input dataset, the sensitivity of this relationship with the different types of activation function, and the number of hidden nodes, respectively and many more. Rasheed et al [50] implemented standard RVFL with different activation functions into the respiratory motion prediction and compared RVFL with direct and without direct links. The authors find out that the results with hardlim activation function are better than results with sigmoid, sine, tribas, radbas and sign functions, and direct links prevent RVFL from overfitting.…”

Section: Empirical Evaluation Of Rvfl For Classification and Regressi...mentioning

confidence: 99%

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: Empirical Evaluation Of Rvfl For Classification and Regressi...mentioning

confidence: 99%

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

“…An independently developed method, the single hidden layer neural network with random weights (RWSLFN), was reported in [29], differing from RVFL by excluding the direct links. Research has shown that the direct links significantly enhances RVFL's performance, especially in time series forecasting [27,[30][31][32][33]. In [16,31,33], the authors clearly demonstrated that the presence of the direct links in RVFL help to regularize the randomization and reduce the model complexity.…”

Section: Introductionmentioning

confidence: 99%

“…Research has shown that the direct links significantly enhances RVFL's performance, especially in time series forecasting [27,[30][31][32][33]. In [16,31,33], the authors clearly demonstrated that the presence of the direct links in RVFL help to regularize the randomization and reduce the model complexity.…”

Section: Introductionmentioning

confidence: 99%

Fast and Accurate Short-Term Load Forecasting with a Hybrid Model

Shin,

Rasheed,

Kil-Heum

et al. 2024

Electronics

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Short-term electric load forecasting (STLF) plays a pivotal role in modern power system management, bolstering forecasting accuracy and efficiency. This enhancement assists power utilities in formulating robust operational strategies, consequently fostering economic and social advantages within the systems. Existing methods employed for STLF either exhibit poor forecasting performance or require longer computational time. To address these challenges, this paper introduces a hybrid learning approach comprising variational mode decomposition (VMD) and random vector functional link network (RVFL). The RVFL network, serving as a universal approximator, showcases remarkable accuracy and fast computation, owing to the randomly generated weights connecting input and hidden layers. Additionally, the direct links between hidden and output layers, combined with the availability of a closed-form solution for parameter computation, further contribute to its efficiency. The effectiveness of the proposed VMD-RVFL was assessed using electric load datasets obtained from the Australian Energy Market Operator (AEMO). Moreover, the effectiveness of the proposed method is demonstrated by comparing it with existing benchmark forecasting methods using two performance indices such as root mean square error (RMSE) and mean absolute percentage error (MAPE). As a result, our proposed method requires less computational time and yielded accurate and robust prediction performance when compared with existing methods.

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“…On the other hand, model-free methods, alternatively, do not formulate any specific model for motion characteristics, as an alternative to forecast the location by taking into account the preceding observations. Existing model-free approaches include support vector regression (SVR) [10,11], relevance vector machines (RVM) [12,13], extreme learning machine (ELM) [14][15][16][17][18], random vector functional link (RVFL) [19], convolution neural network (CNN) [20], long short-term memory (LSTM) [21][22][23], and forecasting random convolution node (fRCN) [24]. A recent comprehensive evaluation of existing approaches [7] reveals that model-free approaches predict respiratory motion with greater robustness and less prediction error than model-based counterparts, as a result, yielding improved prediction performance, especially at larger prediction lengths.…”

Section: Introductionmentioning

confidence: 99%

“…This architectural choice enhances its overall generalization capabilities and significantly accelerates the learning speed. The effectiveness of these direct links within the RVFL network has been showcased across various domains, including time-series prediction [37,42], function approximation [43], as well as classification and regression tasks [19,[44][45][46]. In our prior studies [19], RVFL was employed on respiratory motion for multi-step ahead prediction.…”

Section: Introductionmentioning

confidence: 99%

Respiratory Motion Prediction with Empirical Mode Decomposition-Based Random Vector Functional Link

Rasheed,

Veluvolu

2024

Mathematics

Self Cite

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The precise prediction of tumor motion for radiotherapy has proven challenging due to the non-stationary nature of respiration-induced motion, frequently accompanied by unpredictable irregularities. Despite the availability of numerous prediction methods for respiratory motion prediction, the prediction errors they generate often suffer from large prediction horizons, intra-trace variabilities, and irregularities. To overcome these challenges, we have employed a hybrid method, which combines empirical mode decomposition (EMD) and random vector functional link (RVFL), referred to as EMD-RVFL. In the initial stage, EMD is used to decompose respiratory motion into interpretable intrinsic mode functions (IMFs) and residue. Subsequently, the RVFL network is trained for each obtained IMF and residue. Finally, the prediction results of all the IMFs and residue are summed up to obtain the final predicted output. We validated this proposed method on the benchmark datasets of 304 respiratory motion traces obtained from 31 patients for various prediction lengths, which are equivalent to the latencies of radiotherapy systems. In direct comparison with existing prediction techniques, our hybrid architecture consistently delivers a robust and highly accurate prediction performance. This proof-of-concept study indicates that the proposed approach is feasible and has the potential to improve the accuracy and effectiveness of radiotherapy treatment.

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Respiratory Motion Prediction with Random Vector Functional Link (RVFL) Based Neural Networks

Cited by 6 publications

References 11 publications

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

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

Fast and Accurate Short-Term Load Forecasting with a Hybrid Model

Respiratory Motion Prediction with Empirical Mode Decomposition-Based Random Vector Functional Link

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