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fuzzy logic classifier design for air targets recognition based on HRRP

PHYSICAL COMMUNICATION 2014年第PartC期13卷 205-210页

作者： Liang, Jing Zhu, Fangqi Univ Elect Sci & Technol China Chengdu 611731 SC Peoples R China

The paper describes a fuzzy logic classifier (FLC) to achieve the recognition for air targets. We first acquire the high range resolution profiles (HRRP) of three types of air targets from the measurements. We extract two typical features, namely, the length of the air targets and the difference between adjacent two HRRP based on HRRP from the measurements. Then we design the FLC to synthesize the two characters and identify the type of air targets. Simulation results show that our FLC can achieve the function of target recognition with a high ratio of recognition and also show a robust performance to some extent. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Target recognition HRRP Feature extraction fuzzy logic classifier

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Real-time range estimation in electric vehicles using fuzzy logic classifier

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COMPUTERS & ELECTRICAL ENGINEERING 2020年 83卷 106577-106577页

作者： Ceven, Suleyman Albayrak, Ahmet Bayir, Raif Duzce Univ Duzce Vocat Sch Dept Elect & Automat Technol Duzce Turkey Duzce Univ Fac Technol Dept Comp Engn Duzce Turkey Karabuk Univ Fac Technol Dept Mechatron Engn Karabuk Turkey

Nowadays, many scientists and companies in the automotive sector in the world are undertaking many important studies on electric vehicle technologies. For the electric vehicle to function as desired, the subsystems of the vehicle must be monitored and the parameters related to the vehicle must be kept in the most efficient range. Efficient use of these systems in electric vehicle will increase the vehicle range, as well as ensure the long life of the components used in the vehicle subsystems. Today, problem areas such as calculating the range of electric vehicles and battery state of charge have not yet been sufficiently standardized. The aim of this study is to make a range estimation in electric vehicle with fuzzy logic classifier which has been successfully applied in various problem areas. The fuzzy logic classifier is designed for range estimation, which is one of the most important research areas of electric vehicles today. In the Mamdani type fuzzy logic approach, dynamic vehicle parameters are taken into consideration. The fuzzy logic classifier considers the battery parameters of the vehicle and the power consumed instantly. In the prediction system, the power spent on the vehicle and the battery charge status are selected as inputs. The developed system was evaluated with three different test scenarios on the same track. These tests were conducted with no load (driver only), half load (driver + one person) and fully load (driver + three persons). The fuzzy logic classifier system determines in real-time how far electric vehicle can travel. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Electric vehicles Range estimation fuzzy logic classifier Can communication

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Prediction of Modulus of Rupture and Modulus of Elasticity of Heat Treated Anatolian Chestnut (Castanea Sativa) Wood by fuzzy logic classifier

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DRVNA INDUSTRIJA 2012年第1期63卷 37-43页

作者： Yapici, Fatih Ulucan, Dundar Univ Karabuk Tech Educ Fac Dept Furniture & Decorat Karabuk Turkey

In this study test samples prepared from Anatolian chestnut (Castanea saliva) wood were first exposed to heat treatment at 130, 145, 160, 175, 190 and 205 degrees C for 3, 6, 9 and 12 hours. Then the values of the samples of the modulus of rupture (MOR) and modulus of elasticity (MOE) were determined and evaluated by multiple variance analysis. The aim of this study was to establish the effects of heat treatment on the MOR and MOE values of wood samples by using fuzzy logic classifier Secondly, input and output values and rule base of the fuzzy logic classifier model were built by using the results obtained from the experiment. The developed fuzzy classifier model could predict the MOR and MOE values of test samples at the accuracy levels of 92.64 % and 90.35 %, respectively The model could be especially employed in manufacturing stages of timber industry

关键词： wood heat treatment fuzzy logic classifier modulus of rupture modulus of elasticity

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On size invariance texture image retrieval by fuzzy logic classifier and scattering statistical features

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PATTERN ANALYSIS AND APPLICATIONS 2016年第2期19卷 509-516页

作者： Wang, Juan Zhang, Jiangshe Zhao, Mingming Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Shaanxi Peoples R China

The texture image retrieval plays an important role in everyday life of people. In this paper, a new and efficient image features extraction approach based on scattering transform is proposed for size invariance texture image retrieval. The proposed approach obtains texture information in different directions and scales. And, analysis of size invariance texture image retrieval using fuzzy logic classifier and scattering statistical features is carried out. The different size samples of texture image are randomly generated from the original texture images. Also, average success rate of each size samples is obtained, respectively. The study shows that statistical features can achieve good performance from the sixth feature.

关键词： Scattering transform Scattering statistical features fuzzy logic classifier Image retrieval Size invariance

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Intelligent Recommendation System for E-Learning using Membership Optimized fuzzy logic classifier 3

Intelligent Recommendation System for E-Learning using Membe...

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3rd IEEE-Pune-Section International Conference (PuneCon)

作者： Das, Anupam Al Akour, Mohammad Cotton Univ Gauhati Assam India Al Sharqyat Univ Ibra Oman

ISBN: (纸本)9781728196008

Technology-enhanced learning provides various communication and information technologies for learning and teaching. The teachers also feel comfortable with the Open Educational Resources repositories that learn the existing learning materials when a new course is developed. Yet, this exists as a non-trivial problem because the learning environments should be flexible for the students to learn on the basis of their situations and characteristics. Hence, the main aim of this paper is to provide personalized dynamic and continuous recommendations for online learning systems. This paper plans to implement the novel recommendation system for online learning using intelligent techniques. The main steps of the proposed model are (a) Data collection, (b) Feature extraction, and (c) classification. Initially, the data are collected locally from the Ekhool learning application. Then the feature extraction techniques, such as t-Distributed Stochastic Neighbour Embedding (t-SNE) and Principle Component Analysis (PCA) are used for selecting the most relevant features. Further, the classifier termed as fuzzy logic classifier is adopted as the recommendation system, where the improvement is made in the membership limits by optimizing it with the Rider Optimization Algorithm (ROA). The superiority of the proposed method is proved by the performance analysis in terms of various performance measures.

关键词： E-learning Recommendation system t-Distributed Stochastic Neighbour Embedding Principle Component Analysis fuzzy logic classifier Rider Optimization Algorithm Membership Optimization

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Self Organising fuzzy logic classifier for Predicting Type-2 Diabetes Mellitus using ACO-ANN

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2020年第7期11卷 348-353页

作者： Patil, Ratna Tamane, Sharvari Patil, Kanishk Noida Inst Engn & Technol Comp Engn Dept Greater Noida Uttar Pradesh India Jawaharlal Nehru Coll Engn Informat Technol Aurangabad Maharashtra India JJ Hosp Mumbai Maharashtra India

In today's digital world, a dataset with large number of attributes has a curse of dimensionality where the computation time grows exponentially with the number of dimensions. To overcome the problem of computation time and space, appropriate method of feature selection can be developed using metaheuristic approaches. The aim of this work is to investigate the use of ant colony optimization with the help of neural network to select near optimal feature subset and integrate it with the self-organizing fuzzy logic classifier for improving the recognition rate. The proposed fuzzy classifier derives prototype from the collected data through an offline training process and uses it to develop a fuzzy inference system for classification. Once trained, it can continuously learn from streaming data and later adapts the changing facts by updating the system structure recursively. The developed model is not based on predefined parameters used in the data generation model but is derived from the empirically observed data.

关键词： Ant colony optimization feature selection fuzzy logic classifier self organizing type-2 diabetes mellitus

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Validation and reconstruction of flow meter data in the Barcelona water distribution network

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CONTROL ENGINEERING PRACTICE 2010年第6期18卷 640-651页

作者： Quevedo, J. Puig, V. Cembrano, G. Blanch, J. Aguilar, J. Saporta, D. Benito, G. Hedo, M. Molina, A. Tech Univ Catalonia UPC Dept Automat Control Rambla St Nebridi 10 Terrassa 08222 Spain CSIC Ind Robot Inst Barcelona 08028 Spain CNRS LAAS F-31077 Toulouse France AGBAR Barcelona Water Co Barcelona 08009 Spain Adasa Sistemas Lhospitalet De Llobregat 08908 Spain

This paper presents a signal analysis methodology to validate (detect) and reconstruct the missing and false data of a large set of flow meters in the telecontrol system of a water distribution network. The proposed methodology is based on two time-scale forecasting models: a daily model based on a ARIMA time series, while the 10-min model is based on distributing the daily flow using a 10-min demand pattern. The demand patterns have been determined using two methods: correlation analysis and an unsupervised fuzzy logic classification, named LAMDA algorithm. Finally, the proposed methodology has been applied to the Barcelona water distribution network, providing very good results. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Water distribution network Telecontrol system Flow meter Fault detection Sensor failure fuzzy logic classifier

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fuzzy logic model for predicting peanut maturity

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TRANSACTIONS OF THE ASAE 2000年第2期43卷 483-490页

作者： Shahin, MA Verma, BP Tollner, EW Univ Georgia Driftmier Engn Ctr Athens GA 30602 USA Univ Georgia Dept Biol & Agr Engn Athens GA 30602 USA Canadian Grain Commiss Winnipeg MB Canada

Peanut quality and yield are impacted by harvest timing. The most commonly used tool for determining harvest timing is-the hull-scrape chart giving association of kernel maturity to color of mesocarp. The hull-scrape technique is tedious, time-consuming, and labor-intensive. Wider use of maturity evaluations would be greatly facilitated by a quicker and easier test. While testing for maturity, the NMR signals from peanuts and days after planting exhibit a nonlinear relationship with the maturity class of kernels. Therefore, linear classification techniques such as linear discriminant analysis (LDA) may not achieve "good" classification results. This article describes the development of a fuzzy model to predict peanut maturity based on NMR-signal (FIDPK) and days after planting (DAP). Compared to the hull-scrape method, the fuzzy model predictions were 45%, 63%, and 73% accurate when maturity was classified in 6 classes, 5 classes and 3 classes, respectively. The respective accuracies from LDA, using the same data, were 42%, 56% and 70%. Data from 346 kernels were used for performance evaluation of both the fuzzy and LDA models. The fuzzy model improved maturity prediction compared to LDA. These results are encouraging, however;fuzzy model should be further evaluated with new data.

关键词： peanut maturity NMR fuzzy logic classifier

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Single-channel surface electromyography (sEMG) based control of a multi-functional prosthetic hand

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INSTRUMENTATION SCIENCE & TECHNOLOGY 2021年第4期49卷 428-444页

作者： Prakash, Alok Sharma, Shiru Banaras Hindu Univ Indian Inst Technol Sch Biomed Engn Varanasi 221005 Uttar Pradesh India

Patients suffering from upper limb amputation experience a significant reduction in their ability to perform various tasks. The currently available myoelectric prostheses seek to reinstate the lost abilities of the amputees. However, most users abandon their devices due to high cost, large size, heavy weight, complexity, and limited functional control. This paper presents a multifunctional prosthetic hand that can perform six hand activities deploying a single-channel surface electromyography (sEMG) sensor. EMG signals for fifteen subjects (five amputees and ten intact) were acquired for six contraction levels of forearm muscles using the designed sensor. These levels were further classified to recognize six predefined hand gestures by a fuzzy logic classifier. The proposed system showed an excellent success (> 95%) and other performance parameters above 96%. The intended classification-based control scheme was further realized in real-time to achieve six grip patterns for the developed prosthetic prototype. The hand was tested on five subjects (two amputees and three intact) showed a percentage of success above 91% for accomplishing the dexterous grasping operations. The proposed approach is modest, efficient, and provides a low-cost solution to amputees with multiple degrees of freedom with a single channel EMG system.

关键词： Single-channel surface electromyography (sEMG) prosthetic hand fuzzy logic classifier degrees of freedom grip patterns

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A novel algorithm for ventricular arrhythmia classification using a fuzzy logic approach

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AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2016年第4期39卷 903-912页

作者： Nong Weixin Guangxi Guigang Peoples Hosp Zhongshan Rd Guigang Guangxi Peoples R China

In the present study, it has been shown that an unnecessary implantable cardioverter-defibrillator (ICD) shock is often delivered to patients with an ambiguous ECG rhythm in the overlap zone between ventricular tachycardia (VT) and ventricular fibrillation (VF);these shocks significantly increase mortality. Therefore, accurate classification of the arrhythmia into VT, organized VF (OVF) or disorganized VF (DVF) is crucial to assist ICDs to deliver appropriate therapy. A classification algorithm using a fuzzy logic classifier was developed for accurately classifying the arrhythmias into VT, OVF or DVF. Compared with other studies, our method aims to combine ten ECG detectors that are calculated in the time domain and the frequency domain in addition to different levels of complexity for detecting subtle structure differences between VT, OVF and DVF. The classification in the overlap zone between VT and VF is refined by this study to avoid ambiguous identification. The present method was trained and tested using public ECG signal databases. A two-level classification was performed to first detect VT with an accuracy of 92.6 %, and then the discrimination between OVF and DVF was detected with an accuracy of 84.5 %. The validation results indicate that the proposed method has superior performance in identifying the organization level between the three types of arrhythmias (VT, OVF and DVF) and is promising for improving the appropriate therapy choice and decreasing the possibility of sudden cardiac death.

关键词： Ventricular arrhythmia fuzzy logic classifier Feature extraction Pattern classification

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