Relevant bibliographies by topics / Adaptive Fuzzy filter

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Adaptive Fuzzy filter'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Adaptive Fuzzy filter"

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Aessa, Suad Ali, Ekbal Hussain Ali, Salam Waley Shneen, and Layla H. Abood. "Adaptive Fuzzy Filter Technique for Mixed Noise Removing from Sonar Images Underwater." Journal of Fuzzy Systems and Control 2, no. 2 (2024): 45–49. https://doi.org/10.59247/jfsc.v2i2.176.

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Underwater Analysis of acquired images may be affected by low contrast, haze, and other disturbances., caused by scattering and absorption of the light through propagation. An adaptive fuzzy filter for three mixed noise reduction is adopted on underwater sonar images to take out the various noises that either appear in the image when captured or injected into the image when transmitted. Underwater images when captured usually have speckle noise, salt, pepper noise also Gaussian noise. Is suggested in this paper an adaptive fuzzy filter structure that combines the fuzzy filter, sigmoid sliding
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Wang, Yong Jun, Jing Shuo Xu, Rui Hua Song, Yang Gao, and Ya Zhou Di. "Comparison on Three Filter Methods in Self-Alignment for SINS of the Carrier Craft." Advanced Materials Research 591-593 (November 2012): 1793–99. http://dx.doi.org/10.4028/www.scientific.net/amr.591-593.1793.

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Fuzzy adaptive filter and H∞ filter are introduced to solve the problem of low filter performance, which comes from uncertain noise caused by seawave and high frequence vibrancy. First, basic principles of the fuzzy adaptive filter and H∞ filter are formulated. Second, state space model of self-alignment for SINS of the carrier craft is built. Finally, according to each character, a comparison on results that Kalman filter, fuzzy adaptive filter and H∞ filter are applied to alignment for SINS of the carrier craft is made. Simulation results show that although Kalman filter has definite robustn
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RYU, GEUN-TAEK, DAE-SUNG KIM, DAE-YOUNG LEE, SUNG-HWAN HAN, and HYEON-DEOK BAE. "CONVERGENCE IMPROVEMENT OF ADAPTIVE LATTICE ALGORITHM WITH FUZZY BASED ADAPTIVE GAIN." Journal of Circuits, Systems and Computers 09, no. 01n02 (1999): 125–32. http://dx.doi.org/10.1142/s0218126699000116.

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The choice of the adaptive gain is important to the performance of LMS-based adaptive filters. Depending on application areas, the realization structure of the filters is also important. This letter presents an adaptive lattice algorithm which adjusts the adaptive gain of LMS using fuzzy if-then rules determined by matching input and output variables during adaptation procedure. In each lattice filter stage, this filter adjusts the adaptive gain as the output of the fuzzy logic which has two input variables, normalized squared forward prediction error and one step previous adaptive gain. The p
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Xiahua Yang and Peng Seng Toh. "Adaptive fuzzy multilevel median filter." IEEE Transactions on Image Processing 4, no. 5 (1995): 680–82. http://dx.doi.org/10.1109/83.382502.

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García Infante, Juan Carlos, José de J. Medel Juárez, and Juan Carlos Sánchez García. "Neural fuzzy digital filtering: multivariate identifier filters involving multiple inputs and multiple outputs (MIMO)." Ingeniería e Investigación 31, no. 1 (2011): 184–92. http://dx.doi.org/10.15446/ing.investig.v31n1.20569.

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Multivariate identifier filters (multiple inputs and multiple outputs - MIMO) are adaptive digital systems having a loop in accordance with an objective function to adjust matrix parameter convergence to observable reference system dynamics. One way of complying with this condition is to use fuzzy logic inference mechanisms which interpret and select the best matrix parameter from a knowledge base. Such selection mechanisms with neural networks can provide a response from the best operational level for each change in state (Shannon, 1948). This paper considers the MIMO digital filter model usi
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Roy, Amarjit, Joyeeta Singha, and Rabul Hussain Laskar. "Removal of Impulse Noise from Gray Images Using Fuzzy SVM Based Histogram Fuzzy Filter." Journal of Circuits, Systems and Computers 27, no. 09 (2018): 1850139. http://dx.doi.org/10.1142/s0218126618501396.

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Impulse noise is an image noise that degrades the quality of the image drastically. In this paper, k-means clustering has been incorporated with fuzzy-support vector machine (FSVM) classifier for classification of noisy and non-noisy pixels in removal of impulse noise from gray images. Here, local binary pattern (LBP) has been incorporated with previously used feature vector prediction error of the processing pixel, absolute difference between median value and processing pixel, median pixel, pixel under operation and mean value around the processing kernel. In this work, [Formula: see text]-me
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Do, Cong Hung, and Huei-Yung Lin. "Incorporating neuro-fuzzy with extended Kalman filter for simultaneous localization and mapping." International Journal of Advanced Robotic Systems 16, no. 5 (2019): 172988141987464. http://dx.doi.org/10.1177/1729881419874645.

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Extended Kalman filter is well-known as a popular solution to the simultaneous localization and mapping problem for mobile robot platforms or vehicles. In this article, the development of a neuro-fuzzy-based adaptive extended Kalman filter technique is presented. The objective is to estimate the proper values of the R matrix at each step. We design an adaptive neuro-fuzzy extended Kalman filter to minimize the difference between the actual and theoretical covariance matrices of the innovation consequence. The parameters of the adaptive neuro-fuzzy extended Kalman filter is then trained offline
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Pulipaka, Krishna Prasad, K. Sathish Kumar, K. Geethali Apoorva, Rohith Rao, and K. Radha Krishna. "Noise Removal from Images Using Adaptive Neuro/Network-Fuzzy Interface Systems." International Journal of Engineering and Applied Technologies 22 (December 13, 2022): 15–32. http://dx.doi.org/10.56431/p-t615v7.

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Any Information signal is best desirable without any external noise/ disturbances. Noise in any signal is the undesirable quantity present which deteriorates the signal's quality, thus compromising the information. Any signal, be it an image signal (2-D) or else a video signal (3-D) in the field of communication, if not always but most number of times prone to noise. In this paper, we would be dealing with removing types of noise on an image, using various filter techniques such as vector median filter, vector directional filter. Using the image processing tools in MATLAB, we could achieve thi
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Salehi, Hadi, Javad Vahidi, and Homayun Motameni. "A Robust Hybrid Filter Based on Evolutionary Intelligence and Fuzzy Evaluation." International Journal of Image and Graphics 18, no. 04 (2018): 1850023. http://dx.doi.org/10.1142/s0219467818500237.

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In this paper, a novel denoising method based on wavelet, extended adaptive Wiener filter and the bilateral filter is proposed for digital images. Production of mode is accomplished by the genetic algorithm. The proposed extended adaptive Wiener filter has been developed from the adaptive Wiener filter. First, the genetic algorithm suggest some hybrid models. The attributes of images, including peak signal to noise ratio, signal to noise ratio and image quality assessment are studied. Then, in order to evaluate the model, the values of attributes are sent to the Fuzzy deduction system. Simulat
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Arnal, Josep, and Luis Súcar. "Hybrid Filter Based on Fuzzy Techniques for Mixed Noise Reduction in Color Images." Applied Sciences 10, no. 1 (2019): 243. http://dx.doi.org/10.3390/app10010243.

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To decrease contamination from a mixed combination of impulse and Gaussian noise on color digital images, a novel hybrid filter is proposed. The new technique is composed of two stages. A filter based on a fuzzy metric is used for the reduction of impulse noise at the first stage. At the second stage, to remove Gaussian noise, a fuzzy peer group method is applied on the image generated from the previous stage. The performance of the introduced algorithm was evaluated on standard test images employing widely used objective quality metrics. The new approach can efficiently reduce both impulse an
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Dissertations / Theses on the topic "Adaptive Fuzzy filter"

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Kumar, Jukanti Ajay, and Dharmana B. P. Naidu. "Digital Video Stabilization using SIFT Feature Matching and Adaptive Fuzzy Filter." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4063.

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Context: Video stabilization techniques have gained popularity for their permit to obtain high quality video footage even in non-optimal conditions. There have been significant works done on video stabilization by developing different algorithms. Most of the stabilization software displays the missing image areas in stabilized video. In the last few years hand-held video cameras have continued to grow in popularity, allowing everyone to easily produce personal video footage. Furthermore, with online video sharing resources being used by a rapidly increasing number of users, a large proportion
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Ferrari, Rafael 1977. "Equalização de canais de comunicação digital baseada em filtros fuzzy." [s.n.], 2005. http://repositorio.unicamp.br/jspui/handle/REPOSIP/261780.

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Orientador: João Marcos Travassos Romano<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação<br>Made available in DSpace on 2018-08-05T07:17:28Z (GMT). No. of bitstreams: 1 Ferrari_Rafael_M.pdf: 3590603 bytes, checksum: 36e018a11a02c47baafbf4b3682e9f1d (MD5) Previous issue date: 2005<br>Resumo: Esta tese objetiva o estudo da utilização de fuzzy na equalização supervisionada e não-supervisionada de canais de comunicação digital. O trabalho se divide em basicamente duas partes. Na primeira, é feita uma revisão das técnicas de equaliza
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Hsieh, Yu-Chen, and 謝雨辰. "Improved Adaptive Fuzzy Multilevel Median Filter." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/16718205799958302185.

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碩士<br>國立臺灣科技大學<br>資訊管理系<br>90<br>In this thesis, an improved adaptive fuzzy multilevel median filter method (IAFMMF) is presented. Due to employing a true thin-line (TTL) feature checking technique, surprisingly our proposed method has about 44% execution-time improvement ratio when compared to the AFMMF. Under three real images with impulsive noise, experimental results also show that under the same noise cancellation capability, our proposed IAFMMF has a better PSNR improvement and better TTL feature preserving capability when compared to the other seven well-known deno
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Chang, Fu-I., and 張復詒. "An Innovative Fuzzy Adaptive Fading Kalman Filter for GPS Navigation." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/76754239283236996074.

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碩士<br>國立臺灣海洋大學<br>通訊與導航工程系<br>95<br>The extended Kalman Filter (EKF) is an important method for eliminating stochastic errors of dynamic position in the Global Positioning System (GPS). One of the adaptive methods is called the Adaptive Fading Kalman filter (AFKF), which employs suboptimal multiple fading factors for limiting the length of memory in an EKF. A scaling factor α has been proposed for increasing the fading factors so as to improve the tracking capability. Traditional approach for selecting the scaling factor α heavily relies on personal experience or computer simulation. In order
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Wang, Sheng-Hung, and 王聖鋐. "Navigation System Design using Adaptive Fuzzy Strong Tracking Kalman Filter." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/61139809486545522443.

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碩士<br>國立臺灣海洋大學<br>通訊與導航工程系<br>94<br>英文摘要 While employed in the Global Positioning System (GPS) receiver as the navigational state estimator, the extended Kalman filter (EKF) provides optimal solution in terms of minimum mean square error. To obtain good estimation solutions using the EKF approach, the designers are required to have good knowledge on both dynamic process and measurement models, in addition to the assumption that both the process and measurement are corrupted by zero-mean Gaussian white noises. In various circumstances where there are uncertainties in the system model and noise
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Wu, Jia-Hong, and 吳佳鴻. "Error-Trimmed Median Filter Based on the Adaptive Fuzzy Linear Regression." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/21128395365470004795.

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碩士<br>國立中正大學<br>資訊工程學系<br>85<br>Nonlinear filter plays an important role in the field of image processing due to their ability to perform noise cancellation effectively. The median filter is one of the best known nonlinear filters. In median filtering, a pixel is replaced with the median within a window even if the pixel is not corrupted. Some filters based on a detection-estimation strategy are proposed to solve this problem. Even though the performance of the detection
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Chang, Yuan-tain, and 張園田. "An Adaptive Hybrid Filter Based on Fuzzy System for Speech Enhancement." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/55038312936888767465.

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碩士<br>朝陽科技大學<br>資訊工程系碩士班<br>98<br>n this paper an adaptive hybrid filter, based on fuzzy inference system, is proposed for speech signal enhancement. As we know, noise causes the drastic reduction of the accuracy ratio of the speech recognition system. In this paper an effective voice enhancement scheme is proposed to filter the additive noise. In the hybrid filter spectral subtraction, wavelet filtering and wiener filtering are cooperated to filter the signal and complementary to each other. And, the input voice signal is classified into non-stationary and stationary type. The former signal i
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Tai, Chen-Wu(Jacky), and 戴承武. "Impulse Noise Removal of Corrupted Images Using Adaptive Median Fuzzy Weighted Recursive Filter." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/83713505700438170310.

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碩士<br>國立臺灣科技大學<br>自動化及控制研究所<br>101<br>Nowadays, human’s computing technology is progressed from vacuum tube to transmitter and semiconductor. The quantity and quality of computing technology is faster and diverse. Lots of applications depending on computers, especially for mechanical visual technology, are used to replace older films. Since storage devices and CMOS are recently cheaper than before, images can be saved as high-quality digital data. In real world, when an image is transformed from one place to another by a digital technology, the image may be corrupted due to imposed noises like
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Amara, Chandrasekhar. "Adaptive hysteresis based fuzzy controlled shunt active power filter for mitigation of harmonics." Thesis, 2013. http://ethesis.nitrkl.ac.in/4645/1/209EE2157.pdf.

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Active filters are widely employed in distribution system to reduce the harmonics produced by non-linear loads result in voltage distortion and leads to various power quality problems. In this work the simulation study of a Adaptive hysteresis based fuzzy logic controlled shunt active power filter capable of reducing the total harmonic distortion is presented. The advantage of fuzzy control is that it is based on a linguistic description and does not require a mathematical model of the system and it can adapt its gain according to the changes in load. The instantaneous p-q theory is used for c
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Hsiao, Wei-Chieh, and 蕭維頡. "Application of Kalman Filter and Adaptive-Network-Based Fuzzy Inference System in Indoor Localization." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/11884914828064826829.

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碩士<br>東海大學<br>工業工程與經營資訊學系<br>102<br>Location-based services are widely integrated in our lives, such as inventory management, personal tracking or healthcare. With increasing applications of wireless localization, accuracy and stability of location estimation have become more critical. However, indoor localization suffers from multipath interference that affects traditional algorithm based on received signal strength indicator (RSSI). In this research, an indoor localization algorithm which combined Kalman filter and adaptive-network-based fuzzy inference system (ANFIS) was proposed. This loca
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Book chapters on the topic "Adaptive Fuzzy filter"

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Zhihong, Man, Seng Kah Phooi, and H. R. Wu. "Designing a Fuzzy Gain Lyapunov Adaptive Filter Algorithm." In Fuzzy Logic. Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1806-2_24.

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Yao, Chih-Chia, and Ming-Hsun Tsai. "Adaptive Fuzzy Filter for Speech Enhancement." In Computational Science and Its Applications – ICCSA 2010. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12179-1_42.

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Sharma, Teena, and Nishchal K. Verma. "Adaptive Interval Type-2 Fuzzy Filter." In Artificial Intelligent Algorithms for Image Dehazing and Non-Uniform Illumination Enhancement. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2011-8_6.

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Lee, Chang-Shing, and Yau-Hwang Kuo. "Adaptive Fuzzy Filter and Its Application to Image Enhancement." In Fuzzy Techniques in Image Processing. Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1847-5_6.

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Xu, Qing, Liang Ma, Weifang Nie, Peng Li, Jiawan Zhang, and Jizhou Sun. "Adaptive Fuzzy Weighted Average Filter for Synthesized Image." In Computational Science and Its Applications – ICCSA 2005. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11424857_32.

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Mélange, Tom, Vladimir Zlokolica, Stefan Schulte, et al. "A New Fuzzy Motion and Detail Adaptive Video Filter." In Advanced Concepts for Intelligent Vision Systems. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74607-2_58.

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Duan, Zhuohua, Zixing Cai, and Jinxia Yu. "Fuzzy Adaptive Particle Filter Algorithm for Mobile Robot Fault Diagnosis." In Neural Information Processing. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11893295_78.

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Farbiz, Farzam, Mohammad Bagher Menhaj, and Seyed Ahmad Motamedi. "An Adaptive C-Average Fuzzy Control Filter for Image Enhancement." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48774-3_20.

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Ding, Mingli, and Qi Wang. "An Integrated Navigation System of NGIMU/ GPS Using a Fuzzy Logic Adaptive Kalman Filter." In Fuzzy Systems and Knowledge Discovery. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11539506_100.

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Perez, Meir, David M. Rubin, Tshilidzi Marwala, Lesley E. Scott, Jonathan Featherston, and Wendy Stevens. "The Fuzzy Gene Filter: An Adaptive Fuzzy Inference System for Expression Array Feature Selection." In Trends in Applied Intelligent Systems. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13033-5_7.

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Conference papers on the topic "Adaptive Fuzzy filter"

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Kumar, Ravinder, Sudhansu Kumar Mishra, and Karan Veer. "Hybrid renewable energy sources integrated shunt active power filter with adaptive fuzzy logic." In 2025 International Conference on Power Electronics Converters for Transportation and Energy Applications (PECTEA). IEEE, 2025. https://doi.org/10.1109/pectea61788.2025.11076224.

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Wang, Jianxiong, Congzhe Zhang, and Haobo Liang. "Design of Photoelectric Sensor with Adaptive Filter Based on Fuzzy RBF Neural Network." In 2025 IEEE 5th International Conference on Electronic Technology, Communication and Information (ICETCI). IEEE, 2025. https://doi.org/10.1109/icetci64844.2025.11083985.

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M, Jeba Jenitha, Kani Jesintha D, and Mahalakshmi P. "Noise Adaptive Fuzzy Switching Median Filters for Removing Gaussian Noise." In The International Conference on scientific innovations in Science, Technology, and Management. International Journal of Advanced Trends in Engineering and Management, 2023. http://dx.doi.org/10.59544/ozsc7243/ngcesi23p113.

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Recently, in all image processing systems, image restoration plays a major role and it forms the major part of image processing systems. Medical images such as brain Magnetic Resonance Imaging (MRI), ultrasound images of liver and kidney, retinal images and images of uterus images are often affected by various types of noises such as Gaussian noise and salt and pepper noise. All image restoration techniques attempts to remove various types of noises. This paper deals with various filters namely Mean Filter, Averaging Filter, Median Filter, Adaptive Median Filter, Adaptive Weighted Median Filte
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Kam, H. S., and W. H. Tan. "Impulse Detection Adaptive Fuzzy (IDAF) Filter." In 2009 International Conference on Computer Technology and Development. IEEE, 2009. http://dx.doi.org/10.1109/icctd.2009.157.

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Chung, I.-Ling, Fuh-Hsin Hwang, Cheng-Yuan Chang, and Chang-Min Chou. "Efficient adaptive filter design to the active noise control system." In 2009 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2009. http://dx.doi.org/10.1109/fuzzy.2009.5277339.

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Nick, Theresa, Jurgen Gotze, and Werner John. "Fuzzy-Adaptive Kaiman Filter for RFID localization." In 2012 Ubiquitous Positioning Indoor Navigation and Location Based Service (UPINLBS). IEEE, 2012. http://dx.doi.org/10.1109/upinlbs.2012.6409751.

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Osinenko, Pavel, Mike Geissler, and Thomas Herlitzius. "Adaptive unscented Kaiman filter with a fuzzy supervisor for electrified drive train tractors." In 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2014. http://dx.doi.org/10.1109/fuzz-ieee.2014.6891532.

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Ding, Yu, Qidan Zhu, Zhuoyi Xing, and Lei Li. "An Adaptive-Fuzzy Filter Algorithm for Vision Preprocessing." In 2006 IEEE International Conference on Robotics and Biomimetics. IEEE, 2006. http://dx.doi.org/10.1109/robio.2006.340264.

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El Madbouly, E. E., A. E. Abdalla, and Gh M. El Banby. "Fuzzy adaptive Kalman filter for multi-sensor system." In 2009 International Conference on Networking and Media Convergence (ICNM). IEEE, 2009. http://dx.doi.org/10.1109/icnm.2009.4907206.

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Mohamad, Auday A. H., and Mohammed Osman. "Adaptive median filter background subtractions technique using fuzzy logic." In 2013 International Conference on Computing, Electrical and Electronics Engineering (ICCEEE). IEEE, 2013. http://dx.doi.org/10.1109/icceee.2013.6633917.

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