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Application Research of Hierarchical adaptive Online Course Teaching System based on Optimization learning algorithm

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Journal of Circuits, Systems and Computers 2025年

作者： Zhuge, Mingshuang Li, Jie Organization Department YouJiang Medical University for Nationalities Baise 533000 China Enrollment and Employment Guidance Service Center YouJiang Medical University for Nationalities Baise 533000 China

Accompanied by the continuous development of computer network technology, each university is eager to establish a set of scientific, reasonable, convenient, efficient and easy-to-use online course evaluation systems. This paper proposes an adaptive online assessment system for course teaching based on the adaptive learning algorithm with the improved hierarchical method. First, this paper combines the Hidden Markov Model and proposes a selection quantity reflecting the probability of items being selected according to the relationship between the exposure rate and the accuracy of the quiz, and puts forward a selection strategy based on the difficulty and differentiation of multilevel stratification, and controlling the exposure rate with the number of item subjects, i.e., the "b-a multilevel hierarchical selection method for controlling the exposure rate". Then the adaptive online assessment system and its database are designed according to the optimized adaptive learning algorithm. The test results found that the b-a multilevel hierarchical question selection method proposed in this paper to control the exposure rate is effective in controlling the exposure rate relative to the maximum information selection, and the number of times the items are exposed is averaged through the computation of the exposure rate, but the exposure rate is not as good as the hierarchical method in the highest differentiation level of the hierarchical division as in the case of the informativeness hierarchical method, and the distribution of the exposure rate of the asymptotic maximum information method is the closest to that of the hierarchical method in terms of the exposure rate. Exposure is only second to the a-stratification method. The maximum response time of the general assessment system is 40s when virtualizing the transaction transmission of 10 users, while the maximum response time of the test system in this paper is 50s when virtualizing the transaction transmission of 50 user

关键词： adaptive learning algorithm database Hidden Markov multilevel hierarchy online assessment

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Intelligent e-learning design for art courses based on adaptive learning algorithms and artificial intelligence

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ENTERTAINMENT COMPUTING 2024年 50卷

作者： Zheng, Wang Anyang Inst Technol Acad Art & Design Anyang 455000 Henan Peoples R China

With the development of digital technology and the popularity of the Internet, e-learning has become an increasingly popular learning method. However, current e-learning systems often lack personalization and intelligence, and cannot meet the different learning needs of students. This paper designed an intelligent e-learning system using adaptive learning algorithm and artificial intelligence technology. The system aims to provide a personalized learning experience to help students efficiently master the knowledge of art courses. We introduce in detail the design and implementation of the intelligent e-learning system, which uses the adaptive learning algorithm to continuously adjust the learning content and learning methods by analyzing the students' learning behaviors and characteristics. The system provide intelligent tutoring and evaluation functions to help students with autonomous learning and feedback. By comparing the experiment with the traditional e-learning system, we find that the intelligent e-learning system has significant advantages in improving the learning effect and learning motivation. After using the intelligent e-learning system, students' knowledge mastery and learning interest have been significantly improved.

关键词： adaptive learning algorithm Artificial intelligence Art course Intelligent e-learning

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Sequential sampling techniques for algorithmic learning theory

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THEORETICAL COMPUTER SCIENCE 2005年第1期348卷 3-14页

作者： Watanabe, O Tokyo Inst Technol Dept Math & Comp Sci Tokyo 1528552 Japan

A sequential sampling algorithm or adaptive sampling algorithm is a sampling algorithm that obtains instances sequentially one by one and determines from these instances whether it has already seen enough number of instances for achieving a given task. In this paper, we present two typical sequential sampling algorithms. By using simple estimation problems for our example, we explain when and how to use such sampling algorithms for designing adaptive learning algorithms. (c) 2005 Elsevier B.V. All rights reserved.

关键词： sequential sampling adaptive sampling adaptive learning algorithm Chernoff bound Hoeffding bound

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The Tag Navigation Recommendation with adaptive learning Method

The Tag Navigation Recommendation with Adaptive Learning Met...

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19th Annual International Conference on Management Science and Engineering (ICMSE)

作者： Jiang Wei Pang Xiu-li Harbin Inst Technol Sch Management Harbin 150001 Peoples R China

ISBN: (纸本)9781467330152;9781467330145

Social Tags are widely used in web 2.0, and they bring the new chance and challenge to the recommender system, which is used to help users deal with information overload and provide personalized services. There are three respects of work done in this paper: firstly, the n-gram based Tag Navigation is presented to provide the assistant support for tag retrieval;secondly, the Average Mutual Information based tag similarity measure is detailed, furthermore this kind of semantic relation is applied to the retrieval intention expansion;thirdly, an approach of ranking based recommendation is presented, and the adaptive learning mechanism is explored. The experiments verify above methods, and result shows the complex features adopted in the recommendation bring improvement by 13.39%.

关键词： personal recommendation Tag Navigation retrieval intention expansion Average Mutual Information adaptive learning algorithm

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Sequential sampling techniques for algorithmic learning theory 11th

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11th International Conference on algorithmic learning Theory (ALT 2000)

作者： Watanabe, O Tokyo Inst Technol Dept Math & Comp Sci Tokyo 1528552 Japan

ISBN: (纸本)9783540412373

关键词： sequential sampling adaptive sampling adaptive learning algorithm Chernoff bound Hoeffding bound

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adaptive Fuzzy Total Sliding-Mode Control of Unknown Nonlinear Systems

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2012年第3期14卷 434-443页

作者： Lin, Chih-Min Hsu, Chun-Fei Chen, Te-Yu Yuan Ze Univ Dept Elect Engn Tao Yuan 320 Taiwan Tamkang Univ Dept Elect Engn New Taipei City 251 Taiwan

This paper proposes the adaptive fuzzy total sliding-mode control systems with integral (I-AFTSMC) and proportional-integral (PI-AFTSMC) learning algorithms for the unknown nonlinear systems. These AFTSMC systems are comprised of a fuzzy total sliding-mode controller and a robust controller. The fuzzy total sliding-mode controller is utilized to approximate an ideal controller and the robust controller is designed to cover the approximation error between the fuzzy total sliding-mode controller and the ideal controller. In these designs the fuzzy rules are on-line tuned by the derived learning algorithm in the sense of Lyapunov function, so that the stability of the system can be guaranteed. The proposed AFTSMC systems are applied to the fault accommodation control of a Van der Pol oscillator and trajectory tracking control of a linear piezoelectric ceramic motor. The simulation result of Van der Pol oscillator and the experimental result of linear piezoelectric ceramic motor demonstrate that the effectiveness of the proposed AFTSMC systems for achieving favorable tracking performance. Comparing to the integral learning algorithm, the proportional-integral learning algorithm can achieve faster convergence of the tracking error;this comparison is also illustrated by the simulations and experiments.

关键词： adaptive control fuzzy system total sliding-mode control adaptive learning algorithm

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Lane-changes prediction based on adaptive fuzzy neural network

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EXPERT SYSTEMS WITH APPLICATIONS 2018年 91卷 452-463页

作者： Tang, Jinjun Liu, Fang Zhang, Wenhui Ke, Ruimin Zou, Yajie Cent S Univ Sch Traff & Transportat Engn Changsha 410075 Hunan Peoples R China Inner Mongolia Agr Univ Sch Energy & Transportat Engn Hohhot 010018 Peoples R China Northeast Forestry Univ Traff Sch Harbin 150040 Heilongjiang Peoples R China Univ Washington Dept Civil & Environm Engn Seattle WA 98195 USA Tongji Univ Minist Educ Key Lab Rd & Traff Engn Shanghai 201804 Peoples R China

Lane changing maneuver is one of the most important driving behaviors. Unreasonable lane changes can cause serious collisions and consequent traffic delays. High precision prediction of lane changing intent is helpful for improving driving safety. In this study, by fusing information from vehicle sensors, a lane changing predictor based on adaptive Fuzzy Neural Network (AFFN) is proposed to predict steering angles. The prediction model includes two parts: fuzzy neural network based on Takagi-Sugeno fuzzy inference, in which an improved Least Squares Estimator (LSE) is adopt to optimize parameters;adaptive learning algorithm to update membership functions and rule base. Experiments are conducted in the driving simulator under scenarios with different speed levels of lead vehicle: 60 km/h, 80 km/h and 100 km/h. Prediction results show that the proposed method is able to accurately follow steering angle patterns. Furthermore, comparison of prediction performance with several machine learning methods further verifies the learning ability of the AFNN. Finally, a sensibility analysis indicates heading angles and acceleration of vehicle are also important factors for predicting lane changing behavior. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Lane changes Fuzzy neural network Steering prediction Driving simulation adaptive learning algorithm

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Generalized adaptive Bayesian Relevance Feedback for image retrieval in the Orthogonal Polynomials Transform domain

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SIGNAL PROCESSING 2012年第12期92卷 3062-3067页

作者： Kalpana, J. Krishnamoorthy, R. JR Polytech Coll Tiruchirappalli 620012 Tamil Nadu India Anna Univ Technol Tiruchirappalli India

In this paper, we propose a generalized Bayesian Relevance Feedback (RF) algorithm for image retrieval systems with enhanced adaptability to the users' requirements. The adaptability of the algorithm is owing to the different weights that are given to the current and the prior learning. This algorithm is implemented in an image retrieval system which learns in the integer-arithmetic Orthogonal Polynomials Transform (OPT) domain. With the transform's partial coefficients of the image being the features extracted, a mixture of Gaussians is used to represent the image. The image retrieval system is trained on the COIL-100 database. Experimental evidence illustrates the clear benefits of this introduction of adaptability into RF algorithm which can account for both positive and negative feedback. The superiority of the proposed algorithm in terms of increased recall and reduced number of feedback iterations when compared to the already existing Bayesian RF implementations is demonstrated. (C) 2012 Elsevier B.V. All rights reserved.

关键词： adaptive learning algorithm Bayesian Relevance Feedback Content based image retrieval (CBIR) Gaussian Mixture model Orthogonal Polynomials Transform

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Dynamic pruning-based Bayesian support vector regression for reliability analysis

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2024年 244卷

作者： Yu, Shui Ren, Yuyao Wu, Xiao Guo, Peng Li, Yun Univ Elect Sci & Technol China Shenzhen Inst Adv Study Ind Artificial Intelligence Ctr Shenzhen 518110 Peoples R China Southwest Jiaotong Univ Sch Mech Engn Chengdu 610031 Peoples R China i4AI Ltd London WC1N 3AX England

adaptive surrogate-based reliability analysis methods have garnered significant attention due to their potential to enhance computational efficiency in accurately estimating failure probability. However, the candidate sample pool remains constant for most surrogate-based reliability methods, and traversing the candidate sample pool one by one will reduce the efficiency of surrogate modeling. More importantly, maintaining a static sample pool may lead to the inclusion of samples that contribute minimally to the construction of the surrogate modeling, thereby impacting the accuracy and efficiency of the reliability analysis. Accordingly, this paper leverages the robust performance of Bayesian support vector regression to propose a dynamic pruning strategy for the candidate sample pool to estimate failure probability efficiently. A dynamic pruning strategy is presented to streamline the process further, iteratively reducing the candidate sample pool. An adaptive learning algorithm is then introduced, integrating the U function and the sparsity of training samples. This is complemented by a formulated convergence condition, contributing to an ideal surrogate model. The proposed approaches showcase superior efficiency and accuracy through illustrations using well-known benchmark problems and complex reliability analysis problems involving small failure probability and high-dimensional limit state function.

关键词： Reliability analysis Bayesian support vector regression adaptive learning algorithm Dynamic pruning strategy

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Identification of Hammerstein Systems with Random Fourier Features and Kernel Risk Sensitive Loss

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NEURAL PROCESSING LETTERS 2023年第7期55卷 9041-9063页

作者： Zheng, Yunfei Wang, Shiyuan Chen, Badong Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Peoples R China

Identification of Hammerstein systems with polynomial features and mean square error loss has received a lot of attention due to their simplicity in calculation and solid theoretical foundation. However, when the prior information of nonlinear subblock of a Hammerstein system is unknown or some outliers are involved, the performance of related methods may degenerate seriously. The main reason is that the used polynomial just has finite approximation capability to an unknown nonlinear function, and mean square error loss is sensitive to outliers. In this paper, a new identification method based on random Fourier features and kernel risk sensitive loss is therefore proposed. Since the linear combination of random Fourier features can well approximate any continuous nonlinear function, it is expected to be more powerful to characterize the nonlinear behavior of Hammerstein systems. Moreover, since the kernel risk sensitive loss is a similarity measure that is insensitive to outliers, it is expected to have excellent robustness. Based on the mean square convergence analysis, a sufficient condition to ensure the convergence and some theoretical values regarding the steady-state excess mean square error of the proposed method are also provided. Simulation results on the tasks of Hammerstein system identification and electroencephalogram noise removal show that the new method can outperform other popular and competitive methods in terms of accuracy and robustness.

关键词： Hammerstein system identification Random Fourier features Kernel risk sensitive loss adaptive learning algorithm Electroencephalogram noise removal

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