检索结果-内蒙古大学图书馆

adaptive learning algorithm for Cerebellar model articulation controller

MECHATRONICS 2002年第6期12卷 859-873页

作者： Abdelhameed, MM Pinspon, U Cetinkunt, S Univ Illinois Dept Mech Engn MC 251 Chicago IL 60607 USA Ain Shams Univ Fac Engn Design & Prod Engn Dept Cairo Egypt

Cerebellar model articulation controller (CMAC) was developed two decades ago, yet lacks an adequate learning algorithm. Examining the performance of a CMAC based controller showed that the control system become unstable after a long period of real time runs. A new adaptive learning algorithm is proposed. The resultant controller is applied for the trajectory tracking control of a piezoelectric actuated tool post. The performance of the proposed controller is compared with those of conventional controllers (PI controller and the conventional CMAC based controller). The experimental results showed that performance of the CMAC based controller using the proposed learning algorithm is stable and more effective than that of the conventional controllers. (C) 2002 Elsevier Science Ltd. All rights reserved.

关键词： cerebellar model articulation controller (CMAC) stability neural network adaptive learning algorithm piezoelectric actuated tool post

来源：评论

学校读者我要写书评

暂无评论

adaptive learning algorithm based price prediction model for auction lots-deep clustering based interval quoting

引用

PEERJ COMPUTER SCIENCE 2024年 10卷 e2412-e2412页

作者： Ke, Da Fan, Xianhua Asif, Muhammad Huazhong Univ Sci & Technol Sch Management Wuhan Hubei Peoples R China China Univ Geosci Sch Econ & Management Wuhan Hubei Peoples R China Natl Text Univ Dept Comp Sci Faisalabad Punjab Pakistan

This article addresses the problem of interval pricing for auction items by constructing an auction item price prediction model based on an adaptive learning algorithm. Firstly, considering the confusing class characteristics of auction item prices, a dynamic interclass distance adaptive learning model is developed to identify confusing classes by calculating the differences in prediction values across multiple classifiers for target domain samples. The difference in the predicted values of the target domain samples on multiple classifiers is used to calculate the classification distance, distinguish the confusing classes, and make the similar samples in the target domain more clustered. Secondly, a deep clustering algorithm is constructed, which integrates the temporal characteristics and numerical differences of auction item prices, using DTW-K-medoids based dynamic time warping (DTW) and fuzzy C-means (FCM) algorithms for fine clustering. Finally, the KF-LSTM auction item interval price prediction model is constructed using long short-term memory (LSTM) and dual clustering. Experimental results show that the proposed KF-LSTM model significantly improves the prediction accuracy of auction item prices during fluctuation periods, with an average accuracy rate of 90.23% and an average MAPE of only 5.41%. Additionally, under confidence levels of 80%, 85%, and 90%, the KF-LSTM model achieves an interval coverage rate of over 85% for actual auction item prices, significantly enhancing the accuracy of auction item price predictions. This experiment demonstrates the stability and accuracy of the proposed model when applied to different sets of auction items, providing a valuable reference for research in the auction item price prediction field.

关键词： adaptive learning algorithm Dual clustering LSTM FCM algorithm Interval price prediction

来源：评论

学校读者我要写书评

暂无评论

Next day peak load forecasting using neural network with adaptive learning algorithm based on similarity

引用

ELECTRIC MACHINES AND POWER SYSTEMS 2000年第7期28卷 613-624页

作者： Senjyu, T Sakihara, H Tamaki, Y Uezato, K Univ Ryukyus Fac Engn Okinawa 9030213 Japan

In this article toe propose the adaptive learning algorithm, of neural network with respect to a rapid temperature change of forecasted day. The proposed adaptive learning algorithm is used to shift the learning range of previous year of forecasted day. Therefore, the proposed neural network can be trained by using learning data, including the maximum temperature to be forecasted. The suitability of the proposed approach is illustrated through an application to actual load data of Okinawa Electric Power Company in Japan.

关键词： similar day neural network adaptive learning algorithm annual growth load

来源：评论

学校读者我要写书评

暂无评论

POWER-DEMAND FORECASTING USING A NEURAL-NETWORK WITH AN adaptive learning algorithm

引用

IEE PROCEEDINGS-GENERATION TRANSMISSION AND DISTRIBUTION 1995年第6期142卷 560-568页

作者： DASH, PK LIEW, AC RAMAKRISHNA, G REG ENGN COLL ROUKELA 769008INDIA

An artificial neural network with an adaptive-Kalman-filter-based learning algorithm is presented for forecasting weather-sensitive loads. The proposed model can differentiate between weekday and weekend loads, This neural-network model has been implemented using real load data, The results reveal the efficiency and accuracy of the proposed approach in terms of short learning time, rapid convergence and the adaptive nature of the learning algorithm.

关键词： neural network learning (artificial intelligence) weekday loads efficiency Power system planning and layout rapid convergence load forecasting Neural computing techniques short learning time weekend loads adaptive Kalman filters adaptive learning algorithm power-demand forecasting neural nets Power engineering computing power system analysis computing Knowledge engineering techniques weather-sensitive loads forecasting

来源：评论

学校读者我要写书评

暂无评论

An adaptive learning algorithm for controlling a two-degree-of-freedom serial ball-and-socket actuator

引用

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING 2007年第I7期221卷 1001-1006页

作者： Al-Assadi, H. M. A. A. Hamouda, A. M. S. Ismail, N. Aris, I. Univ Putra Malaysia Serdang 43400 Malaysia Univ Qatar Dept Mech & ind Syst Engn Doha Qatar Univ Putra Malaysia Dept Elect & Elect Engn Serdang Malaysia

This article presents the development and implementation of an artificial neural network (ANN) for controlling a new two-degree-of-freedom (2DOF) serial ball-and-socket actuator. The ANN is a well-known algorithm for simulating the ability of the human brain to learn and predict sets of information. In this approach, ANN will learn the control parameters to obtain the angular displacement, angular velocity, and angular acceleration of the end-effector without any prior knowledge of the actuator. The ball-and-socket actuator has been proposed as an alternative actuator to the conventional one-degree-of-freedom (1DOF) revolute actuator. The actuator was fabricated from a ball-and-socket joint powered by two electrohydraulic cylinders. Experimental control data had been collected manually and provided for ANN to learn in off-line mode. The training process was carried out to build control knowledge. Thus, the adaptive learning algorithm adopts any modification in the actuator mechanism and hydraulic power system through updating the control knowledge. The results of implementing the build control knowledge for on-line operation of the ball-and-socket actuator shows a fully compliant actuator end-effector to the desired dynamic behaviour within the workspace.

关键词： ball-and-socket actuator hydraulic power intelligent controller artificial neural network back-propagation serial robot manipulator adaptive learning algorithm

来源：评论

学校读者我要写书评

暂无评论

adaptive learning algorithm for cerebellar model articulation controller neural network based hybrid-type controller (Part I)

American Society of Mechanical Engineers, Dynamic Systems an...

引用

American Society of Mechanical Engineers, Dynamic Systems and Control Division (Publication) DSC 2000年 68卷 201-210页

作者： Abdelhameed, Magdy Mohamed Cetinkunt, Sabri Des. and Prod. Eng. Department Faculty of Engineering Ain Shams University Abasiah Cairo Egypt Mechanical Engineering Department University of Illinois at Chicago 842 W Taylor St. Chicago IL 60607-7022 United States

Cerebellar model articulation controller (CMAC) is a useful neural network learning technique. It was developed two decades ago but yet lacks an adequate learning algorithm, especially when it is used in a hybrid-type controller. This work is intended to introduce a simulation study for examining the performance of a hybrid-type control system based on the conventional learning algorithm of CMAC neural network. This study showed that the control system is unstable. Then a new adaptive learning algorithm of a CMAC based hybrid-type controller is proposed. The main features of the proposed learning algorithm, as well as the effects of the newly introduced parameters of this algorithm have been studied extensively via simulation case studies. The simulation results showed that the proposed learning algorithm is a robust in stabilizing the control system. Also, this proposed learning algorithm preserved all the known advantages of the CMAC neural network. Part II of this work is dedicated to validate the effectiveness of the proposed CMAC learning algorithm experimentally.

关键词： (CMAC) adaptive learning algorithm Hybrid-type control Neural network Piezoelectric actuator Stability Tool post

来源：评论

学校读者我要写书评

暂无评论

RBF Neural Network Based on T-S Fuzzy Model adaptive learning algorithm and Application 8

RBF Neural Network Based on T-S Fuzzy Model Adaptive Learnin...

引用

IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

作者： Zheng, Bowen Zhong, Mengchun Mao, Baoquan Army Armored Force Acad Beijing 100072 Peoples R China

ISBN: (纸本)9781538681787

Aiming at the problem that the inference rules are large and the parameters are difficult to identify in multidimensional fuzzy reasoning, a TS-type fuzzy RBF neural network model is constructed based on the TS-type fuzzy inference system and the RBF neural network function equivalence principle, and a TS fuzzy model based on TS fuzzy model is proposed The adaptive learning algorithm of RBF neural network can not only dynamically adjust the number of hidden nodes in TS-type fuzzy RBF network, but also adaptively change the data center value of the network. Finally, the effectiveness of the algorithm is verified by MATLAB software simulation. The feasibility shows that the TS-type fuzzy RBF network can quickly approximate any multivariable nonlinear function and has better adaptive ability and generalization ability.

关键词： T-S fuzzy model RBF neural network adaptive learning algorithm MATLAB simulation

来源：评论

学校读者我要写书评

暂无评论

Simulation of E-learning in English personalized learning recommendation system based on Markov chain algorithm and adaptive learning algorithm

引用

ENTERTAINMENT COMPUTING 2024年 51卷

作者： Liu, Ruonan Jilin Univ Finance & Econ Dept Publ Foreign Language Teaching & Res Changchun 130000 Jilin Peoples R China

The research topic of this article is the English personalized learning recommendation module based on Markov chain algorithm and adaptive learning algorithm. Personalized learning recommendation systems have been widely applied in the field of education. Firstly, this article uses Markov chain algorithm to analyze user learning behavior data, abstract user learning behavior as a process of state transition, predict their future behavior based on their past behavior, better understand their learning needs and interests, and provide more personalized learning recommendations for them. Subsequently, combined with adaptive learning algorithms, recommendation strategies and content are dynamically adjusted based on the user's learning goals and level, providing more accurate and effective learning recommendations according to their personalized needs and learning progress. By comparing with traditional recommendation systems, evaluate the accuracy and personalization of the recommendation module to better meet the learning needs of users.

关键词： Markov chain algorithm adaptive learning algorithm Personalized recommendation in English learning system

来源：评论

学校读者我要写书评

暂无评论

31Design of Intelligent Financial System Based on adaptive learning algorithm: Intelligent Optimization of High Frequency Trading System

引用

INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGIES AND SYSTEMS APPROACH 2024年第1期17卷

作者： Zhang, Zhaozhe Ahmad, Shahbaz Henan Tech Inst Zhengzhou Henan Peoples R China Natl Text Univ Faisalabad Pakistan

The high-frequency trading system in the financial domain has long been a focal point of investigation. This study posits an intelligent financial system design framework predicated on a cross-adaptive self-entropy projection clustering model, aimed at enhancing the efficacy of high-frequency trading systems. A composite distribution model of financial data is formulated to derive sequences of financial data activities. And cross-adaptive learning algorithm is employed to ascertain the interrelated attributes of financial data. Following this, the support vector machine algorithm is applied for the classification processing of these interrelated features, yielding a set of financial data feature vectors, which are then fed into the gray correlation-based information feature extraction model. Through extensive empirical evaluations with authentic trading data, the proposed intelligent financial system design framework exhibits commendable performance, furnishing a viable solution for the intelligent optimization of high-frequency trading systems.

关键词： High-Frequency Trading Intelligent Financial System adaptive learning algorithm Cross-adaptive Self-Entropy Projection Clustering Model

来源：评论

学校读者我要写书评

暂无评论

adaptive learning algorithms to incorporate additional functional constraints into neural networks

引用

NEUROCOMPUTING 2000年第1-4期35卷 73-90页

作者： Jeong, SY Lee, SY Korea Adv Inst Sci & Technol Dept Elect Engn Yusong Gu Taejon 305701 South Korea Korea Adv Inst Sci & Technol Computat & Neural Syst Lab Brain Sci Res Ctr Yusong Gu Taejon 305701 South Korea

In this paper, adaptive learning algorithms to obtain better generalization performance are proposed. We specifically designed cost terms for the additional functionality based on the first- and second-order derivatives of neural activation at hidden layers. In the course of training, these additional cost functions penalize the input-to-output mapping sensitivity and high-frequency components in training data. A gradient-descent method results in hybrid learning rules to combine the error back-propagation, Hebbian rules, and the simple weight decay rules. However, additional computational requirements to the standard error back-propagation algorithm are almost negligible. Theoretical justifications and simulation results are given to verify the effectiveness of the proposed learning algorithms. (C) 2000 Elsevier Science B.V. All rights reserved.

关键词： adaptive learning algorithm mapping sensitivity curvature smoothing time-series prediction

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：