A novel design of Gaussian Wavelet Neural Networks for nonlinear Falkner-Skan systems in fluid dynamics

Ilyas, Hira; Raja, Muhammad Asif Zahoor; Ahmad, Iftikhar; Shoaib, Mohammed

doi:10.1016/j.cjph.2021.05.012

Cited by 36 publications

(7 citation statements)

References 68 publications

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“…The stochastic soft computing platform has been extensively used by the research community for addressing different applications of paramount interest arising in a broad domain of applied science and engineering. A few examples exploiting the strength of AI methods as reliable and effective solution approaches include online learning, scheduling via multi-objective optimization [41], nonlinear Falkner-Skan systems [42], the berth scheduling problem at marine container terminals [43], the entropy generation model [44], density estimation [45], the vehicle routing problem [46], nonlinear Lane-Emden multipantograph delay differential systems [47], the identification of differences between bacterial and viral meningitis [48], the Bouc-Wen hysteresis model for piezostage actuators [49], data classification [50], and the parameter estimation of power signals [51]. All these illustrations prove the worth of heuristics and meta-heuristics methodologies.…”

Section: Solution Methodologymentioning

confidence: 99%

Intelligent Backpropagation Networks with Bayesian Regularization for Mathematical Models of Environmental Economic Systems

et al. 2021

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The research community of environmental economics has had a growing interest for the exploration of artificial intelligence (AI)-based systems to provide enriched efficiencies and strengthened human knacks in daily live maneuvers, business stratagems, and society evolution. In this investigation, AI-based intelligent backpropagation networks of Bayesian regularization (IBNs-BR) were exploited for the numerical treatment of mathematical models representing environmental economic systems (EESs). The governing relations of EESs were presented in the form of differential models representing their fundamental compartments or indicators for economic and environmental parameters. The reference datasets of EESs were assembled using the Adams numerical solver for different EES scenarios and were used as targets of IBNs-BR to find the approximate solutions. Comparative studies based on convergence curves on the mean square error (MSE) and absolute deviation from the reference results were used to verify the correctness of IBNs-BR for solving EESs, i.e., MSE of around 10−9 to 10−10 and absolute error close to 10−5 to 10−7. The endorsement of results was further validated through performance evaluation by means of error histogram analysis, the regression index, and the mean squared deviation-based figure of merit for each EES scenario.

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Section: Solution Methodologymentioning

confidence: 99%

Intelligent Backpropagation Networks with Bayesian Regularization for Mathematical Models of Environmental Economic Systems

et al. 2021

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“…The timefrequency window can expand the research scope in the time domain and frequency domain at the same time, which is convenient for better problem discovery. This property makes it applicable to nonlinear sciences such as differential equations, pattern recognition, and computer vision [9][10][11].…”

Section: Wnnmentioning

confidence: 99%

Analysis of Wavelet Neural Network Algorithm in Inverter Fault Diagnosis

2022

IJOMAM

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The frequency conversion speed regulation system powered by inverter is widely used in various fields. It is particularly important that the inverter that is most prone to failure can quickly and accurately locate the fault location and analyze the cause of the fault. The Wavelet Neural Network (WNN) is used as the entry point, and the fault diagnosis of the inverter is studied. Firstly, the Back Propagation (BP) algorithm commonly used in WNN is analyzed. A variable learning rate factor is introduced, and a variable learning rate variable weighted recursive least squares algorithm is proposed. Then, the relevant parameters are set. The inverter fault diagnosis simulation experiment is designed. Experiments show that the improved Learning Rate and Variable Weight Recursive Least Squares (LWRLS) algorithm completes fitting 1200-1300 steps faster than the BP algorithm in terms of operation speed. In terms of network error, the error value of the WNN of the BP algorithm can only reach 104. The error of the WNN of the LWRLS algorithm reaches 106. Therefore, when the algorithm is improved, the network error of WNN is reduced a lot. In the fault diagnosis accuracy, the average accuracy of the BP algorithm is 98.37%, and the standard deviation is 1.2571. The average accuracy of the LWRLS algorithm is 99.56% with a standard deviation of 0.3920. The fault recognition rate after the improved algorithm is significantly better, and the stability is better. Theoretical analysis and simulation results show that the improved LWRLS algorithm in WNN has the advantages of fast operation speed, small network error and high diagnostic accuracy in the inverter fault diagnosis method. The proposed improved WNN algorithm provides a reference for the application of inverter fault diagnosis.

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“…People with an adventurous spirit will fear nothing in entrepreneurship. Even if they encounter difficulties, they will take the initiative to accept challenges with confidence and go forward bravely (Ilyas et al, 2021 ).…”

Section: College Students' Entrepreneurial Psychologymentioning

confidence: 99%

The Effect of Law Students in Entrepreneurial Psychology Under the Artificial Intelligence Technology

Zhang

2021

Front. Psychol.

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With the increasingly serious employment situation in China, the government and schools encourage college students to start businesses to alleviate employment pressure. College student's successful entrepreneurship depends on national preferential policies, social support, and, most importantly, their healthy and solid psychological quality and entrepreneurial psychological quality. The purpose is to understand the entrepreneurial psychology of college students and study the entrepreneurial psychological effect. Firstly, the four aspects of entrepreneurial psychology are summarized, including entrepreneurial awareness, entrepreneurial volition, entrepreneurial ability, and entrepreneurial personality. Secondly, the research status of college students' entrepreneurial psychology is reviewed, and the existing problems are pointed out. Thirdly, the combined model of wavelet transform and Neural Network (NN) is proposed, and the feasibility of the proposed model is evaluated through the analysis of college students' entrepreneurial psychology. The wavelet NN is used in experimental design to predict college students' entrepreneurial psychology, and the predicted results are compared with the actual value. From the perspective of the prediction results of entrepreneurial psychology, the combination of wavelet algorithm and neural network is more accurate for entrepreneurial psychology prediction and evaluation results of law students. Overall, the difference between the predicted value and the actual value is within 0.3 points, which is relatively stable. According to the analysis of single-factor results, the scores of students of different majors in the four dimensions of entrepreneurial psychology are all higher than 3.5, but there is no significant difference among the four dimensions (P > 0.05), indicating that the major has no significant impact on entrepreneurial psychology; law students with different educational backgrounds have significant differences in entrepreneurial psychology (P < 0.05), among which students with a master's degree have the strongest entrepreneurial will, while doctoral students have the lowest entrepreneurial will; in terms of entrepreneurial psychological capital, men's self-efficacy is higher than women's, and the difference is significant (P < 0.05). The difference between males and females in the scores of entrepreneurial psychological factors' four aspects is not very obvious. In terms of entrepreneurial psychological capital, males' self-efficacy is significantly higher than females' (P < 0.05). Artificial Intelligence (AI) technology has great application prospects in the prediction and evaluation of college students' entrepreneurial psychology, and college students' entrepreneurial psychology is highly correlated with gender and education.

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A novel design of Gaussian Wavelet Neural Networks for nonlinear Falkner-Skan systems in fluid dynamics

Cited by 36 publications

References 68 publications

Intelligent Backpropagation Networks with Bayesian Regularization for Mathematical Models of Environmental Economic Systems

Intelligent Backpropagation Networks with Bayesian Regularization for Mathematical Models of Environmental Economic Systems

Analysis of Wavelet Neural Network Algorithm in Inverter Fault Diagnosis

The Effect of Law Students in Entrepreneurial Psychology Under the Artificial Intelligence Technology

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