Multiple Linear Regression Prediction and Wavelet Neural Network Based Intelligent Online Tuning Control Method

In order to explore how to realize the cost prediction and sharing ability of higher education, this paper proposes an analysis of the cost prediction and sharing ability of higher education based on artificial neural network. This method explores how to realize the cost prediction of higher education through the key technical problems and solutions of information recommendation based on artificial neural network. The research shows that the ability of college education cost prediction and sharing based on artificial neural network is 61% higher than that of traditional cost prediction and sharing. With the help of BP neural network algorithm, the accuracy of cost prediction can be maintained above 90%. It is proved that the artificial neural network algorithm can effectively improve the cost forecasting, financial system, and cost management system of colleges and universities.

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“…e complexity of the neural network is determined by the number of original layers and the number of hidden units in each layer [13].…”

Section: Determination Of Network Structurementioning

confidence: 99%

Prediction of Higher Education Cost and Analysis of Sharing Ability Based on Artificial Neural Network

Xia

Yue

Niu

et al. 2022

Security and Communication Networks

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“…An intelligent PID controller that utilized the estimation of nonlinear system and PID control together was proposed. [9][10][11] The adaptive controller was proposed using time-varying parameter adaptation of the vehicle dynamics model. 12,13 These studies assumed that the unknown disturbances were not significant.…”

Section: Introductionmentioning

confidence: 99%

Model-reference adaptive sliding mode control of longitudinal speed tracking for autonomous vehicles

Lee

Seo

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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This paper presents a longitudinal speed control algorithm using a model-reference adaptive sliding mode control (ASMC) scheme for an autonomous vehicle in various driving environments using only wheel speed sensors. The proposed algorithm could control the vehicle’s speed not using parameter estimators but using an adaptation technique. The parameter adaptation laws were designed to compensate for the changes in the environmental disturbances and model uncertainties. Moreover, the upper bound of unknown disturbances, that were not compensated by the adaptation algorithm, was estimated using radial basis function neural network (RBFNN). The sliding mode controller updated the upper bound from the RBFNN and obtained robustness without knowing the bound in advance. Adaptive equivalent control input was also defined to compensate for zero-throttle acceleration varying with speed. This input could enhance the mode switch smoothly between throttle and brake control. We conducted computer simulations and vehicle tests under various driving environments to evaluate the performance of the proposed algorithm. In the simulation result, the average tracking error of the proposed algorithm was 0.718 kph, and the maximum change rate of the error due to the disturbances was 11%. The improvements were 55% and 68%, respectively, compared to the PID control. The average error in the vehicle test result was 0.414 kph, which was improved by 48% compared to the PID control in the test track. The results demonstrate that the proposed algorithm ensures desirable tracking performance under environmental disturbances and model uncertainties.

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A novel bagged tree ensemble regression method with multiple correlation coefficients to predict the train body vibrations using rail inspection data

Peng

Zheng²,

Zhong³

et al. 2023

Mechanical Systems and Signal Processing

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Multiple Linear Regression Prediction and Wavelet Neural Network Based Intelligent Online Tuning Control Method

Cited by 8 publications

References 27 publications

Prediction of Higher Education Cost and Analysis of Sharing Ability Based on Artificial Neural Network

Prediction of Higher Education Cost and Analysis of Sharing Ability Based on Artificial Neural Network

Model-reference adaptive sliding mode control of longitudinal speed tracking for autonomous vehicles

A novel bagged tree ensemble regression method with multiple correlation coefficients to predict the train body vibrations using rail inspection data

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