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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Akanksha, A. Somani Kokate, R.D. Abhilasha, Mishra Maharashtra Institute of Technology Department of Electronics & Computer Engineering Aurangabad India College of Engineering Department of Instrumentation and Control Engineering Pune India

ISBN: (纸本)9798350304480

Tool condition monitoring is vital for enhancing productivity, reducing costs, and improving product quality in manufacturing industries. Existing approaches, such as threshold-based methods, sensor-based methods, and machine learning-based techniques, have limitations in terms of accuracy, adaptability, interpretability, and computational costs. This research proposes a tool condition monitoring system based on the Self-Organizing Map (SOM) algorithm and the hebbian learning algorithm. The system is implemented on a Field-Programmable Gate Array (FPGA) platform for real-time processing in manufacturing environments. A dataset is acquired using sensors, pre-processed, and divided into training and testing sets. The SOM-Hebb classifier is designed with appropriate parameters, and the tool condition monitoring algorithm involves projection histograms, discrete Fourier transform (DFT), and the SOM-Hebb classifier. The FPGA implementation achieves high-speed processing and meets timing constraints. Simulation results demonstrate the system's functionality and accuracy, with a classification accuracy of 99.22%. The system exhibits a processing time of 35 clock cycles. This research provides a promising approach for efficient and accurate tool condition monitoring in manufacturing industries. © 2023 IEEE.

关键词： Field-Programmable Gate Array (FPGA) hebbian learning algorithm Real-time processing Self-Organizing Map (SOM) Tool condition monitoring

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A New Mathematical Modelling Approach for Viticulture and Winemaking Using Fuzzy Cognitive Maps

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IFAC-PapersOnLine 2015年第24期48卷 15-20页

作者： Peter, Groumpos P. Antigoni, Anninou P. Vasileios, Groumpos P. Laboratory of Automation and Robotics Department of Electrical and Computer Engineering University of Patras Rio26500 Greece Master Stud. Agricultural University of Athens Athens Greece

The aim of this paper is to present a new approach in modelling wine quality and quantity using Fuzzy Cognitive Maps trained by non linear hebbian learning algorithm. The methodology described extracts the knowledge from the experts and exploits their experience on wine production. Two case studies with data from real vineyards were examined. The results of this study show that software tools using Fuzzy Cognitive Maps could be explored further and problems that arise during the wine production could be prevented by an efficient decision support system. © 2015, IFFCA (Internaitional Fedreation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Fuzzy rules Artificial intelligence Breweries Cognitive systems Decision support systems Wine Case studies Fuzzy cognitive map hebbian learning algorithm New approaches viticulture Wine production Wine quality winemaking

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Adaptive Neuro Fuzzy Based Dynamic Simulation of Induction Motor Drives

Adaptive Neuro Fuzzy Based Dynamic Simulation of Induction M...

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IEEE International Conference on Fuzzy Systems (FUZZ)

作者： Menghal, P. M. Laxmi, A. Jaya Mil Coll Elect & Mech Engn Fac Degree Engn Secunderabad 500015 Andhra Pradesh India JNTU EEE Dept Anantapur 515002 Andhra Pradesh India Jawaharlal Nehru Technol Univ Coll Engn Dept EEE Hyderabad 500085 Andhra Pradesh India

ISBN: (纸本)9781479900206

In the industrial sector especially in the field of electric drives & control, induction motors play a vital role. Without proper controlling of the speed, it is virtually impossible to achieve the desired task for a specific application. Basically AC motors, such as Induction Motors are of Squirrel-Cage type. They are simple, reliable, low cost and virtually maintenance-free electrical drives. Based on the inability of conventional control methods like PI, PID controllers to work under wide range of operation, artificial intelligent based controllers are widely used in the industry like ANN, Fuzzy controller, ANFIS, expert system, genetic algorithm. The main problem with the conventional fuzzy controllers is that the parameters associated with the membership functions and the rules depend broadly on the intuition of the experts. To overcome this problem, Adaptive Neuro-Fuzzy controller is proposed in this paper. The comparison between Conventional PI, Fuzzy Controller and Adaptive neuro fuzzy controller based dynamic performance of induction motor drive has been presented. Adaptive Neuro Fuzzy based control of induction motor will prove to be more reliable than other control methods.

关键词： Adaptive Neuro-Fuzzy controller PI Controller Fuzzy Logic Controller(FLC) Sugeno Fuzzy Controller hebbian learning algorithm

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Adaptive Neuro Fuzzy Based Dynamic Simulation of Induction Motor Drives

Adaptive Neuro Fuzzy Based Dynamic Simulation of Induction M...

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IEEE International Conference on Fuzzy Systems

作者： P. M. Menghal A. Jaya Laxmi Faculty of Degree Engineering Military College of Electronics & Mechanical Engineering Dept. of EEE Jawaharlal Nehru Technological University College of Engineering

ISBN: (纸本)9781479900213

关键词： Adaptive Neuro-Fuzzy controller PI Controller Fuzzy Logic Controller (FLC) Sugeno Fuzzy Controller hebbian learning algorithm

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Radio Frequency (RF) Transient Classification using Sparse Representations over Learned Dictionaries

Radio Frequency (RF) Transient Classification using Sparse R...

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Conference on Wavelets and Sparsity XIV

作者： Moody, Daniela I. Brumby, Steven P. Myers, Kary L. Pawley, Norma H. Los Alamos Natl Lab Los Alamos NM 87545 USA

ISBN: (纸本)9780819487483

Automatic classification of transitory or pulsed radio frequency (RF) signals is of particular interest in persistent surveillance and remote sensing applications. Such transients are often acquired in noisy, cluttered environments, and may be characterized by complex or unknown analytical models, making feature extraction and classification difficult. We propose a fast, adaptive classification approach based on non-analytical dictionaries learned from data. We compare two dictionary learning methods from the image analysis literature, the K-SVD algorithm and hebbian learning, and extend them for use with RF data. Both methods allow us to learn discriminative RF dictionaries directly from data without relying on analytical constraints or additional knowledge about the expected signal characteristics. We then use a pursuit search over the learned dictionaries to generate sparse classification features in order to identify time windows that contain a target pulse. In this paper we compare the two dictionary learning methods and discuss how their performance changes as a function of dictionary training parameters. We demonstrate that learned dictionary techniques are suitable for pulsed RF analysis and present results with varying background clutter and noise levels.

关键词： RF classification sparse representation dictionary learning K-SVD algorithm hebbian learning algorithm

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Prefiltering for LMS based adaptive receivers in DS/CDMA communications

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 1997年第12期E80A卷 2357-2365页

作者： Miyajima, T Yamanaka, K Ibaraki Univ Fac Engn Hitachi Ibaraki 316 Japan

In this paper, three issues concerning the linear adaptive receiver using the LMS algorithm for single-user demodulation in direct-sequence/code-division multiple-access (DS/CDMA) systems are considered. First, the convergence rare of the LMS algorithm in DS/CDMA environment is considered theoretically. Both upper and lower bounds of the eigenvalue spread of the autocorrelation matrix of receiver input signals are derived. It is cleared from the results that the convergence rate of the LMS algorithm becomes slow when the signal power of interferer is large. Second, fast converging technique using a prefilter is considered. The LMS based adaptive receiver using an adaptive prefilter adjusted by a hebbian learning algorithm to decorrelate the input signals is proposed. Computer simulation results show that the proposed receiver provides faster convergence than the LMS based receiver. Third, the complexity reduction of the proposed receiver by prefiltering is considered. As for the reduced complexity receiver, it is shown that the performance degradation is little as compared with the full complexity receiver.

关键词： multiple-access interference suppression fast convergence complexity reduction hebbian learning algorithm KL transform

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