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Hardware/Software Implementation of Fuzzy-Neural-Network Self-learning Control methods for Brushless DC Motor Drives

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2016年第1期52卷 414-424页

作者： Rubaai, Ahmed Young, Paul Howard Univ Dept Elect & Comp Engn Washington DC 20059 USA RadiantBlue Technol Inc Chantilly VA USA

This paper presents a study of different fuzzy neural network (FNN) learning control methods for brushless dc (BLDC) motor drives. The FNN combines fuzzy logic (FL) with the learning capabilities of an artificial neural network. The study designs an FNN structure and defines four different training algorithms for the FNN, namely, backpropagation (BP), extended Kalman filter (EKF), genetic (GEN), and particle swarm optimization (PSO). These algorithms are examined in the simple application of pattern matching an input set to an output set and determine the strengths and weaknesses of each algorithm. Tests of each learning algorithm by a pattern matching benchmark are achieved via dSPACE DSP MATLAB/Simulink environment and allows for the capability for adaptive self-tuning of the weights and memberships of the input parameters. Thus, this adds a self-learning capability to the initial fuzzy design for operational adaptively and implements the solution on real hardware using a BLDC motor drive system. The success of the adaptive FNN-controlled BLDC motor drive system is verified by experimental results. Testing results show that the EKF method is the superior method of the four for this specific application. The BP method was also somewhat successful, nearly matching the pattern but not to the accuracy of the EKF. The GEN and PSO methods did not demonstrate success. Demonstrating the proposed self-learning FNN control on real hardware realizes the solution.

关键词： Fuzzy neural network (FNN) industrial drives learning methods MATLAB/Simulink real-time control

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Application of Machine learning methods for Artificial ECG with T-wave alternans

Application of Machine Learning Methods for Artificial ECG w...

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IEEE International Conference on Electronics and Nanotechnology (ELNANO)

作者： Oleksandra Karnaukh Yevgeniy Karplyuk Kyiv Ukraine National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Kyiv Ukraine

ISBN: (数字)9781728197135

ISBN: (纸本)9781728197142

This paper presents the modeling approach artificial electrocardiograms (ECG) with T -wave alternans based on extracted parameters from the T -wave alternas (TWA) database. The developed optimal TWA classification system was evaluated by signals from a mixed database, which consisted of the TWA database and artificial ECG modeled records. F1-score about 95,9 % was received for the Random Forest Classifier (RFC) and Sequential Forward Floating Feature Selection method. Using a wrapper method for feature selection, 14 significant features were selected that associate with TWA.

关键词： T-wave alternans ECG modeling feature extraction machine learning techniques artificial database feature selection Feature extraction Time-weighted average (TWA) Electrocardiography alternan electrochemical grinding MACHINE APPLICATIONS learning methods T wave

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Structure learning methods for Bayesian Networks to Reduce Alarm Floods by Identifying the Root Cause

Structure Learning Methods for Bayesian Networks to Reduce A...

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IEEE International Conference on Emerging Technologies and Factory Automation

作者： Paul Wunderlich Oliver Niggemann inIT-Institut Industrial IT OWL University of Applied Sciences Lemgo Germany

In times of increasing connectivity, complexity and automation safety is also becoming more demanding. As a result of these developments, the number of alarms for the individual operator increases and leads to mental overload. This overload caused by alarm floods is an enormous safety risk. By reducing this risk, it is not only possible to increase the safety for humans and machines, but also to correct the failure at an early stage. This saves money and reduces outage time. In this paper we present an approach using a Bayesian network to identify the root cause of an alarm flood. The root cause is responsible for a sequence of alarms. The causal dependencies between the alarms are represented with a Bayesian network, which serves as a causal model. Based on this causal model the root cause of an alarm flood can be determined using inference. There exist different methods to learn the structure of a Bayesian network. To investigate which method suites the best for the purpose of alarm flood reduction, one algorithm from each method is selected. We evaluated these algorithms with a dataset, which is recorded from a demonstrator of a manufacturing plant in the SmartFactoryOWL.

关键词： learning methods Bayesian Networks Reduce Alarm Floods

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Application of Bayesian Network learning methods in E-learning

Application of Bayesian Network Learning Methods in E-learni...

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2008年国际电子商务、工程及科学领域的分布式计算和应用学术研讨会

作者： Qing Yang~(1,2)Lianfa Zhang~2 Zhufeng Huang~2 Xueping Wang~2 1 Center for Language& Language Education,Central China Normal University,Wuhan,Hubei 430079,P.R.China 2 Department of Computer Science,Central China Normal University,Wuhan,Hubei 430079,P.R.China

E-learning is becoming one of the most important educational *** more and more organizations and institutions are moving towards the e-learning strategy, self-learning model becomes a big challenge. Background knowledge and learning objectives of various groups of students on the network are very ***-learning system,which uses different learning programs for different students,can enhance the efficiency of learning *** a self-learning system,the algorithm dealing with uncertainty factors of Self-learning model is very *** network artifice is a very effective one within various methods dealing with *** this paper,we applied Bayesian network method to self-learning model;designed Bayesian network structure in a self-learning model;assigned the local probability distribution and discussed the way to acquire and propagate related *** practice has proven Bayesian network approach for self-learning model is a very effective method.

关键词： Bayesian Network learning methods self-learning e-learning DAG

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Study on the Connection between learning methods of Elementary Mathematics and Advanced Mathematics

Study on the Connection between Learning Methods of Elementa...

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2018 4th International Conference on Education & Training,Management and Humanities Science(ETMHS 2018)

作者： Bing Tang Chongqing Agricultural Mechanization School

With the continuous reform of college entrance examinations,learning methods have become increasingly *** mathematics has been adjusted in teaching content,and some content overlaps with advanced *** teaching modes and good learning methods can enhance students in the fierce *** core competitiveness of China,the continuous improvement of students' abilities,and the process of advanced mathematics learning methods have a certain degree of *** is its independence that helps students to have their own understanding and use their own advantages to actively study,constantly enhance the ability to analyze things and outline streamlined *** paper is based on a thorough understanding of elementary mathematics teaching,targeted teaching,good transition and convergence of elementary mathematics and advanced mathematics.

关键词： elementary mathematics advanced mathematics learning methods

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Machine learning methods for Assessing Economic Security Indicators in the Life Cycle of On-Board Automation Systems on a Given Platform

Machine Learning Methods for Assessing Economic Security Ind...

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Systems of Signals Generating and Processing in the Field of on Board Communications

作者： S. V. Kozlov E. A. Voronin Federal State Institution «Federal Research Center «Informatics and Management» of the Russian Academy of Sciences Moscow Russia

ISBN: (数字)9781728147727

ISBN: (纸本)9781728147734

A mathematical approach is presented using a unified indicator of assessing economic security in the field of creating on-board complexes of technical equipment as a payload for installation on a given technical platform, based on an assessment of the probability of various kinds of threats and the probability of their successful reflection. Various methods for estimating probabilities are presented, their capabilities and applications are revealed. It is shown that machine learning methods in combination with probabilistic programming are most suitable for the requirements of the digital economy. The analysis of the possibilities of using machine learning technologies and assessing the probability of programming in assessing and predicting economic security in the formation of onboard automation systems based on the rear platform is carried out.

关键词： complexation of onboard equipment on the platform complex process economic security analysis and modeling of random processes Markov chains Bayesian networks probabilistic programming Bayesian analysis Bayesian networks Markov chain Probability Bayes methods automation system Artificial Intelligence Machine learning Tree graphs Platform learning methods Programming

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Machine learning methods for IoT and their Future Applications

Machine learning methods for IoT and their Future Applicatio...

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International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)

作者： Mansi Jindal Jatin Gupta Bharat Bhushan Dept. of CSE HMRITM GGSIPU New Delhi India

ISBN: (纸本)9781728148274

With the advent of rapid developments, large number of heterogeneous devices is able to connect with the help of IOT technology. Although IOT possess very complex architecture because of connectivity of variety of devices and services in the system. In this paper, a brief concept of urban IOT system is presented which are designed to support smart city and advanced communication technologies. Hence a comprehensive survey of architecture, technologies, and computational frameworks is provided for a smart IOT. It also discusses the major vulnerabilities and challenges faced by IOT and also present how machine learning is applied to IOT. Hence smart city is considered as the use case and it explains how various techniques are applied to data in order to extract great results with good efficiency.

关键词： Internet of things Machine learning Supervised learning Naive bayes Support vector machines Knn (k nearest neighbors) Internet of things IOT Semi-supervised learning Support Vector Network Artificial Intelligence Machine learning learning methods

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Discussion on learning methods of Improvised Piano Accompaniment

Discussion on Learning Methods of Improvised Piano Accompani...

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作者： Run Li Sichuan University of Culture and Arts

Improvised piano accompaniment is one of the performing methods of piano music art, and it is also a special and independent piano teaching subject, which has received more and more general attention in the music industry. Piano improvisation is a kind of comprehensive art combining performance and creation. It should be conceived and completed in a short time, and create an artistic image and musical atmosphere in an instant, so as to enhance the appeal and tension of accompaniment.

关键词： Impromptu piano accompaniment learning methods Study

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Type of clinical problem is a determinant of physicians' self-selected learning methods in their practice settings

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Journal of Continuing Education in the Health Professions 1998年第2期18卷 107-118页

作者： McClaran, Jacqueline Snell, Linda Franco, Eliane McGill Centre for Continuing Medical Education Faculty of Medicine McGill University Montreal QC Canada McGill Centre for Continuing Medical Education McGill University Montreal QC Canada

While much research has been written about physician-preferred learning methods and self-directed learning, no published study describes the physician's self-selected learning methods specific to problems arising in his practice. Study subjects were 366 primary care physicians asked to describe a recent clinical problem for which they needed more knowledge or skill to solve. For the specific problem of his/her own patient, each physician was asked how he/she gained the specific ability to solve the problem. Fifty-five learning methods were described by the study population and were categorized as three outcome variables: reading and related activity, 47.8% of study subject responses, formal and informal consultation and/or referral (33.3%), and formal continuing medical education (CME) (18.9%). Analysis for risk similarities led to progressive recombination of 64 problem categories to two predictive variables: clinical problems requiring more knowledge and clinical problems requiring more technical or communication skill to solve. Multiple logistic regression was carried out. The final predictive model revealed that of the factors studied, problem type was the major determinant of self-selected learning method in the practice setting. While reading is the most frequently selected method overall, physicians are more likely to select formal CME when the problem requires more technical expertise or communication ability to solve (odds ratio = 2.31, confidence interval = 1.25–4.25). Region and year of graduation added only slightly to the model. Gender, certification, and size of town did not add to the predictive model. CME providers must be aware of physicians' practice-based choices of learning method and incorporate this into their CME activities. © 1998 The Alliance for Continuing Medical Education, the Society for Academic Continuing Medical Education, and the Council on CME, Association for Hospital Medical Education. All right reserved.

关键词： Clinical problem Continuing medical education (CME) learning methods Practice setting

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The post COVID-19 pandemic era: Changes in teaching and learning methods for management educators

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INTERNATIONAL JOURNAL OF MANAGEMENT EDUCATION 2023年第2期21卷

作者： Ratten, Vanessa La Trobe Univ La Trobe Business Sch Melbourne 3083 Australia

The COVID-19 pandemic resulted in significant changes to management education regarding the interaction and use of digital technology as part of the learning experience. This article focuses on how and why these changes occurred and what this means for teaching methods for the post COVID-19 pandemic era. To do this a literature review on COVID-19 management education and future research was conducted. This enabled key alterations in management education because of the COVID-19 crisis including more emphasis on games and simulations, work/life balance and remote learning to be discussed. This includes a stress on emerging technologies such as the metaverse that are shaping a new era for management educators. Implications for management educators suggest that new theory specifically taking into account a crisis and resilience perspective based on the COVID-19 pandemic are needed. This means emphasising the use of new digital technologies that offer a more interactive experience.

关键词： COVID-19 pandemic Curriculum design Education learning methods Management education Metaverse Teaching

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