Data classification based on fractional order gradient descent with momentum for RBF neural network

Han, Xue; Shao, Zheping; Sun, Hongbo

doi:10.1080/0954898x.2020.1849842

Cited by 8 publications

(5 citation statements)

References 26 publications

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“…L is the truncation order representing the order of truncation and T denotes the discrete sampling period. Letting α as 1 in (19), T as 1 in (22), replacing r(t) with w n (t) and t with t + 1 in (22), the velocity update expression of the FO-PSO is written as [61]:…”

Section: Fractional Order Swarm Optimizationmentioning

confidence: 99%

“…Fractional calculus operators are also exploited to design novel recursive/adaptive algorithms as well as evolutionary/swarm computation heuristics for different optimization tasks involved in engineering and science applications. For example, fractional gradient descent/fractional least mean square algorithm was proposed for various applications including recommender systems [10], channel estimation [11], automatic identification system [12], power system optimization [13], economics [14], radar signal processing [15], system identification [16,17], Hammerstein output error identification [18], wireless sensor network [19], neural network optimization [20][21][22][23][24], chaotic time-series prediction [25,26], oscillator [27], vibration rejection [28], nonlinear AR-MAX identification [29] and parameter estimation of input nonlinear control autoregressive (IN-CAR) systems [29,30].…”

Section: Introduction 1literature Reviewmentioning

confidence: 99%

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Novel Fractional Swarming with Key Term Separation for Input Nonlinear Control Autoregressive Systems

et al. 2022

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In recent decades, fractional order calculus has become an important mathematical tool for effectively solving complex problems through better modeling with the introduction of fractional differential/integral operators; fractional order swarming heuristics are also introduced and applied for better performance in different optimization tasks. This study investigates the nonlinear system identification problem of the input nonlinear control autoregressive (IN-CAR) model through the novel implementation of fractional order particle swarm optimization (FO-PSO) heuristics; further, the key term separation technique (KTST) is introduced in the FO-PSO to solve the over-parameterization issue involved in the parameter estimation of the IN-CAR model. The proposed KTST-based FO-PSO, i.e., KTST-FOPSO accurately estimates the parameters of an unknown IN-CAR system with robust performance in cases of different noise scenarios. The performance of the KTST-FOPSO is investigated exhaustively for different fractional orders as well as in comparison with the standard counterpart. The results of statistical indices through Monte Carlo simulations endorse the reliability and stability of the KTST-FOPSO for IN-CAR identification.

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Section: Fractional Order Swarm Optimizationmentioning

confidence: 99%

Section: Introduction 1literature Reviewmentioning

confidence: 99%

Novel Fractional Swarming with Key Term Separation for Input Nonlinear Control Autoregressive Systems

et al. 2022

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“…The performance of the proposed method is compared with a BP algorithm based on stochastic gradient descent (SGBP), 11 a constrained optimization method based on a BP neural network (CO-BP), 23 an RBF network based on fuzzy c-means clustering (FCRBF), 14 and an optimized RBF network based on fractional order gradient descent with momentum (FOGDM-RBF). 24 In each dataset, all data samples in each dataset are scaled to [−1, 1], the number of kernels in the RBF mapping layer is adjusted manually based on the distribution of the sample space, and the number of BP hidden layers is set to one and two layers; the number of BP hidden layer nodes was set between two and nine, the network learning rate was adjusted iteratively using the simulated annealing algorithm, and the sigmoid kernel parameters were set as a = 1 . 1716 and b = 0 . 6667 . The operating environment of the experiment was an Intel (R) core i7-9700, 3.00 GHz CPU, 8GB RAM, and MATLAB 2013.…”

Section: Experimental Comparison and Analysismentioning

confidence: 99%

Integrated neural network model with pre-RBF kernels

et al. 2021

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To improve the network performance of radial basis function (RBF) and back-propagation (BP) networks on complex nonlinear problems, an integrated neural network model with pre-RBF kernels is proposed. The proposed method is based on the framework of a single optimized BP network and an RBF network. By integrating and connecting the RBF kernel mapping layer and BP neural network, the local features of a sample set can be effectively extracted to improve separability; subsequently, the connected BP network can be used to perform learning and classification in the kernel space. Experiments on an artificial dataset and three benchmark datasets show that the proposed model combines the advantages of RBF and BP networks, as well as improves the performances of the two networks. Finally, the effectiveness of the proposed method is verified.

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“…In this study, several MLP-ANN methods including Levenberg-Marquardt (32), Bayesian Regularization (33), BFGS quasi Newton backpropagation (34) 41), Gradient Descent with Momentum and Gradient Descent (42) were designed based on trialand-error methods with different con gurations. The structure of neural networks was changed by changing the number of hidden layers neurons from 5 to 30 and using 12 different learning algorithms (see Table 2).…”

Section: Model Developmentmentioning

confidence: 99%

Predictive modeling of hospital Length of Stay in COVID-19 patients using Artificial Neural Networks

Orooji

Shanbehzadeh

Kazemi-Arpanahi³

et al. 2021

Preprint

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BackgroundThe current pandemic of coronavirus disease (COVID-19) causes unexpected economic burdens to worldwide health organizations with severe shortages in hospital bed capacity and other related medical resources. Therefore, predicting the length of stay (LOS) is essential to ensure optimal allocating scarce hospital resources and inform evidence-based decision-making. Thus, the purpose of this research is to construct a model for predicting COVID-19 patients' hospital LOS by multiple multilayer perceptron-artificial neural network (MLP-ANN) algorithms. Material and MethodsUsing a single-center registry, the records of 1225 laboratory-confirmed COVID-19 hospitalized cases from February 9, 2020, to December 20, 2020, were analyzed. The correlation coefficient technique was developed to determine the most significant variables as the input of the ANN models. Only variables with a correlation coefficient at the P-value< 0.2 were used in model construction. Ultimately the prediction models were developed based on 12 ANN techniques according to selected variables. ResultsAfter implementing feature selection, a total of 20 variables was determined as the most relevant predictors to build the models. The results indicated that the best performance belongs to a neural network with 20 and 10 neurons in the hidden layer of the Bayesian Regularization classifier for whole and selected features with RMSE of 1.6213 and 2.2332, respectively. ConclusionThe developed model in this study can help in the better calculation of LOS in COVID-19 patients. This model also can be leveraged in hospital bed management and optimized resource utilization.

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Data classification based on fractional order gradient descent with momentum for RBF neural network

Cited by 8 publications

References 26 publications

Novel Fractional Swarming with Key Term Separation for Input Nonlinear Control Autoregressive Systems

Novel Fractional Swarming with Key Term Separation for Input Nonlinear Control Autoregressive Systems

Integrated neural network model with pre-RBF kernels

Predictive modeling of hospital Length of Stay in COVID-19 patients using Artificial Neural Networks

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