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Relevant bibliographies by topics / Probabilistic fuzzy neural network

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Academic literature on the topic 'Probabilistic fuzzy neural network'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Probabilistic fuzzy neural network"

1

Yerokhin, A. L., and O. V. Zolotukhin. "Fuzzy probabilistic neural network in document classification tasks." Information extraction and processing 2018, no. 46 (2018): 68–71. http://dx.doi.org/10.15407/vidbir2018.46.068.

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Song, Lu-Kai, Guang-Chen Bai, Cheng-Wei Fei, and Rhea P. Liem. "Transient probabilistic design of flexible multibody system using a dynamic fuzzy neural network method with distributed collaborative strategy." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 233, no. 11 (2018): 4077–90. http://dx.doi.org/10.1177/0954410018813213.

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To improve the efficiency and accuracy of transient probabilistic analysis of flexible multibody systems, a dynamic fuzzy neural network method-based distributed collaborative strategy is proposed by integrating extremum response surface method and fuzzy neural network. Distributed collaborative dynamic fuzzy neural network method is mathematically modeled and derived by considering the high nonlinearity, strong coupling, and multicomponent characteristics of a flexible multibody system. The proposed method is demonstrated to perform the transient probabilistic analysis of a two-link flexible

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Wang, Xueyan. "A fuzzy neural network-based automatic fault diagnosis method for permanent magnet synchronous generators." Mathematical Biosciences and Engineering 20, no. 5 (2023): 8933–53. http://dx.doi.org/10.3934/mbe.2023392.

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<abstract> <p>In recent years, automatic fault diagnosis for various machines has been a hot topic in the industry. This paper focuses on permanent magnet synchronous generators and combines fuzzy decision theory with deep learning for this purpose. Thus, a fuzzy neural network-based automatic fault diagnosis method for permanent magnet synchronous generators is proposed in this paper. The particle swarm algorithm optimizes the smoothing factor of the network for the effect of probabilistic neural network classification, as affected by the complexity of the structure and parameters

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Zhai, Suwei, Wenyun Li, Cheng Wang, and Yundi Chu. "A Novel Data-Driven Estimation Method for State-of-Charge Estimation of Li-Ion Batteries." Energies 15, no. 9 (2022): 3115. http://dx.doi.org/10.3390/en15093115.

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With the increasing proportion of Li-ion batteries in energy structures, studies on the estimation of the state of charge (SOC) of Li-ion batteries, which can effectively ensure the safety and stability of Li-ion batteries, have gained much attention. In this paper, a new data-driven method named the probabilistic threshold compensation fuzzy neural network (PTCFNN) is proposed to estimate the SOC of Li-ion batteries. Compared with other traditional methods that need to build complex battery models, the PTCFNN only needs data learning to obtain nonlinear mapping relationships inside Li-ion bat

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Kozhomberdieva, Gulnara I., Dmitry P. Burakov, and Georgii A. Khamchichev. "THE STRUCTURE OF A NEURO-FUZZY NETWORK BASED ON BAYESIAN LOGICAL-PROBABILISTIC MODEL." SOFT MEASUREMENTS AND COMPUTING 12, no. 61 (2022): 52–64. http://dx.doi.org/10.36871/2618-9976.2022.12.004.

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The article presents a multilayer structure of a neurofuzzy network based on the Bayesian logicalprobabilistic model of fuzzy inference, previously proposed, researched and implemented by the authors. A brief description of the Bayesian logicalprobabilistic model is given, an example of setting up a neurofuzzy network for solving a fuzzy inference problem is presented. The example shows which network parameters can be used for its training. According to the authors, the proposed network structure with three parametric layers is comparable to the wellknown Takagi– Sugeno–Kang and Wang–Mendel fu

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D’ALCHÉ-BUC, FLORENCE, VINCENT ANDRÈS, and JEAN-PIERRE NADAL. "RULE EXTRACTION WITH FUZZY NEURAL NETWORK." International Journal of Neural Systems 05, no. 01 (1994): 1–11. http://dx.doi.org/10.1142/s0129065794000025.

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This paper deals with the learning of understandable decision rules with connectionist systems. Our approach consists of extracting fuzzy control rules with a new fuzzy neural network. Whereas many other works on this area propose to use combinations of nonlinear neurons to approximate fuzzy operations, we use a fuzzy neuron that computes max-min operations. Thus, this neuron can be interpreted as a possibility estimator, just as sigma-pi neurons can support a probabilistic interpretation. Within this context, possibilistic inferences can be drawn through the multi-layered network, using a dis

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B, Sivaranjani, and Kalaiselvi C. "SOBEL OPERATOR AND PCA FOR NEAREST TARGET OF RETINA IMAGES." ICTACT Journal on Image and Video Processing 11, no. 4 (2021): 2483–91. http://dx.doi.org/10.21917/ijivp.2021.0353.

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In eye, innermost layer is retina. Various important anatomical structures are available in this. Different eye diseases like diabetic retinopathy, glaucoma, etc are indicated by this. For clinical study, patient screening, and diagnosing ocular diseases, physicians are assisted by vascular intersections and blood vessels extraction in retinal images. Retina image’s nearest template are detected using fuzzy neural network (FNN), Probabilistic neural network (PNN) and Adaptive Neuro Fuzzy Inference System (ANFIS) classifier’s ensemble in recent work. However, various factors like low contrast a

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KIM, JAE-HOON, and GIL CHANG KIM. "Fuzzy network model for part-of-speech tagging under small training data." Natural Language Engineering 2, no. 2 (1996): 95–110. http://dx.doi.org/10.1017/s1351324996001258.

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Recently, most part-of-speech tagging approaches, such as rule-based, probabilistic and neural network approaches, have shown very promising results. In this paper, we are particularly interested in probabilistic approaches, which usually require lots of training data to get reliable probabilities. We alleviate such a restriction of probabilistic approaches by introducing a fuzzy network model to provide a method for estimating more reliable parameters of a model under a small amount of training data. Experiments with the Brown corpus show that the performance of the fuzzy network model is muc

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Butusov, A. V., A. V. Kiselev, E. V. Petrunina, R. I. Safronov, V. V. Pesok, and A. E. Pshenichniy. "Algorithms for Monitoring the Effectiveness of Therapeutic and Rehabilitation Procedures Based on Clinical Blood Analysis Indicators in the Medical Decision Support System." Proceedings of the Southwest State University. Series: IT Management, Computer Science, Computer Engineering. Medical Equipment Engineering 13, no. 1 (2023): 170–90. http://dx.doi.org/10.21869/2223-1536-2023-13-1-170-190.

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The purpose of research is development of algorithms for a computer system for monitoring the effectiveness of therapeutic procedures in terms of clinical blood analysis.Methods. A set of algorithms has been developed for a computer system for monitoring the effectiveness of medicinal prescriptions based on the results of a clinical blood test, including an algorithm for analyzing the dynamics of intercellular ratios in a clinical blood test, an algorithm for filling in a database, an algorithm for forming a base of decisive rules, an algorithm for analyzing the sensitivity of a decisive rule.

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Wu, Xinhao, and Qiujun Lu. "Financial asset yield series forecasting based on risk-neutral fuzzy bilinear regression and probabilistic neural network." Journal of Intelligent & Fuzzy Systems 40, no. 6 (2021): 11829–44. http://dx.doi.org/10.3233/jifs-202927.

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Application of quantitative methods for forecasting purposes in financial markets has attracted significant attention from researchers and managers in recent years when conventional time series forecasting models can hardly develop the inherent rules of complex nonlinear dynamic financial systems. In this paper, based on the fuzzy technique integrated with the statistical tools and artificial neural network, a new hybrid forecasting system consisting of three stages is constructed to exhibit effectively improved forecasting accuracy of financial asset price. The sum of squared errors is minimi

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Dissertations / Theses on the topic "Probabilistic fuzzy neural network"

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Kan, Wing Kay. "A probabilistic neural network for associative learning." Thesis, Imperial College London, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.283809.

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Brande, Julia K. Jr. "Computer Network Routing with a Fuzzy Neural Network." Diss., Virginia Tech, 1997. http://hdl.handle.net/10919/29685.

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The growing usage of computer networks is requiring improvements in network technologies and management techniques so users will receive high quality service. As more individuals transmit data through a computer network, the quality of service received by the users begins to degrade. A major aspect of computer networks that is vital to quality of service is data routing. A more effective method for routing data through a computer network can assist with the new problems being encountered with today's growing networks. Effective routing algorithms use various techniques to determine the most

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Elhewy, Ahmed. "Probabilistic analysis of composite structures using artificial neural network." Thesis, University of Newcastle Upon Tyne, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.413045.

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James, Keith. "Online adaptive fuzzy neural network automotive engine control." Thesis, Loughborough University, 2011. https://dspace.lboro.ac.uk/2134/9089.

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Automotive manufacturers are investing in research and development for hybridization and more modern advanced combustion strategies. These new powertrain systems can offer the higher efficiency required to meet future emission legislation, but come at the cost of significantly increased complexity. The addition of new systems to modernise an engine increases the degrees of freedom of the control problem and the number of control variables. Advanced combustion strategies also display interlinked behaviour between control variables. This type of behaviour requires a more orchestrated multi-input

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Myers, Catherine E. "Learning with delayed reinforcement in an exploratory probabilistic logic neural network." Thesis, Imperial College London, 1990. http://hdl.handle.net/10044/1/46462.

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Wang, Ziqing. "Fuzzy neural network for edge detection and Hopfield network for edge enhancement." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0005/MQ42458.pdf.

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Hudgins, Billy E. "Implementation of fuzzy inference systems using neural network techniques." Thesis, Monterey, California. Naval Postgraduate School, 1992. http://hdl.handle.net/10945/23919.

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Campbell, Jonathan G. "Fuzzy logic and neural network techniques in data analysis." Thesis, University of Ulster, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.342530.

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Kuan, Chi-Hsuan, and 官啟玄. "Development of TSK-Type Probabilistic Fuzzy Neural Network Control for LiFePO4 Battery Storage System." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/27852200149425491942.

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碩士<br>國立中央大學<br>電機工程研究所<br>100<br>A digital signal processor (DSP)-based TSK-Type probabilistic fuzzy neural network (TSKPFNN) is proposed in this thesis to control a 4 LiFePO battery storage system. The storage system includes 4 LiFePO battery module with battery management system (BMS) and bidirectional power flow three-phase AC-DC converter. Moreover, the designed storage system adopts active and reactive power control for grid connection. Furthermore, to improve the transient of command variation, a TSKPFNN controller is proposed to replace the traditional proportional-integral (PI) contro

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Γεωργίου, Βασίλειος. "Στατιστική και υπολογιστική νοημοσύνη". Thesis, 2008. http://nemertes.lis.upatras.gr/jspui/handle/10889/2840.

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Η παρούσα διατριβή ασχολείται με τη μελέτη και την ανάπτυξη μοντέλων ταξινόμησης τα οποία βασίζονται στα Πιθανοτικά Νευρωνικά Δίκτυα (ΠΝΔ). Τα προτεινόμενα μοντέλα αναπτύχθηκαν ενσωματώνοντας στατιστικές μεθόδους αλλά και μεθόδους από διάφορα πεδία της Υπολογιστικής Νοημοσύνης (ΥΝ). Συγκεκριμένα, χρησιμοποιήθηκαν οι Διαφοροεξελικτικοί αλγόριθμοι βελτιστοποίησης και η Βελτιστοποίηση με Σμήνος Σωματιδίων (ΒΣΣ) για την αναζήτηση βέλτιστων τιμών των παραμέτρων των ΠΝΔ. Επιπλέον, ενσωματώθηκε η τεχνική bagging για την ανάπτυξη συστάδας μοντέλων ταξινόμησης. Μια άλλη προσέγγιση ήταν η ανάπτυξη ενός

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Books on the topic "Probabilistic fuzzy neural network"

1

Lim, Chee Peng. Probabilistic fuzzy ARTMAP: An autonomous neural network architecture for Bayesian probability estimation. University of Sheffield, Dept. of Automatic Control & Systems Engineering, 1995.

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2

European Congress on Intelligent Techniques and Soft Computing (5th 1997 Aachen, Germany). EUFIT '97: 5th European Congress on Intelligent Techniques and Soft Computing : Aachen, Germany , September 8-11, 1997, proceedings. Verlag Mainz [for the] ELITE-Foundation, 1997.

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European Congress on Fuzzy and Intelligent Technologies. (1st 1993 Aachen, Germany). EUFIT '93: First European congress on fuzzy and intelligent technologies : September 7-10, 1993, Eurogress Aachen, Germany : Proceedings. Verlag der Augustinus Buchhandlung for the ELITE-Foundation, 1993.

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4

1937-, Chen C. H., ed. Fuzzy logic and neural network handbook. McGraw-Hill, 1996.

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Campbell, Jonathan George. Fuzzy logic and neural network network techniques in data analysis. The Author], 2000.

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6

Badiru, Adedeji Bodunde. Fuzzy Engineering Expert Systems with Neural Network Applications. John Wiley & Sons, Ltd., 2002.

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Y, Cheung John, ed. Fuzzy engineering expert systems with neural network applications. J. Wiley, 2002.

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1950-, Irwin G. W., Warwick K, Hunt K. J. 1963-, and Institution of Electrical Engineers, eds. Neural network applications in control. Institution of Electrical Engineers, 1995.

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Hudgins, Billy E. Implementation of fuzzy inference systems using neural network techniques. Naval Postgraduate School, 1992.

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Cios, Krzysztof J. Self-growing neural network architecture using crisp and fuzzy entropy. National Aeronautics and Space Administration, 1992.

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Book chapters on the topic "Probabilistic fuzzy neural network"

1

Bodyanskiy, Ye, Ye Gorshkov, V. Kolodyazhniy, and J. Wernstedt. "A learning probabilistic neural network with fuzzy inference." In Artificial Neural Nets and Genetic Algorithms. Springer Vienna, 2003. http://dx.doi.org/10.1007/978-3-7091-0646-4_3.

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Qader, Karwan, and Mo Adda. "Fault Classification System for Computer Networks Using Fuzzy Probabilistic Neural Network Classifier (FPNNC)." In Engineering Applications of Neural Networks. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11071-4_21.

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3

Pietrzykowski, Zbigniew. "Probabilistic-Fuzzy Method of Ship Manoeuvre Safety Assessment in a Restricted Area." In Neural Networks and Soft Computing. Physica-Verlag HD, 2003. http://dx.doi.org/10.1007/978-3-7908-1902-1_139.

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Gauff, Ashton, and Humberto Munoz Barona. "Probabilistic Fuzzy Neural Networks and Interval Arithmetic Techniques for Forecasting Equities." In Studies in Systems, Decision and Control. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40814-5_13.

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Delgosha, Farshid, and Mohammad B. Menhaj. "Fuzzy Probabilistic Neural Networks: A Practical Approach to the Implementation of Baysian Classifier." In Computational Intelligence. Theory and Applications. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45493-4_12.

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Zhang, Xiu, Xin Zhang, and Wei Wang. "Fuzzy Neural Network." In Intelligent Information Processing with Matlab. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6449-9_4.

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Gorse, Denise. "Associative Reinforcement Training Using Probabilistic RAM Nets." In Neural Network Dynamics. Springer London, 1992. http://dx.doi.org/10.1007/978-1-4471-2001-8_2.

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Manslow, J., M. Brown, and M. Nixon. "On the Probabilistic Interpretation of Area Based Fuzzy Land Cover Mixing Proportions." In Artificial Neuronal Networks. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-57030-8_6.

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Land, Walker H., and J. David Schaffer. "Bayesian Probabilistic Neural Network (BPNN)." In The Art and Science of Machine Intelligence. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18496-4_7.

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Teixeira, Marcelo Andrade, and Gerson Zaverucha. "A Partitioning Method for Fuzzy Probabilistic Predictors." In Neural Information Processing. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30499-9_143.

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Conference papers on the topic "Probabilistic fuzzy neural network"

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Zhang, Zhaozhao, Lei Yang, and Yingqin Zhu. "Structure Design of Adaptive Probabilistic Modular Neural Network." In 2024 4th International Conference on Artificial Intelligence, Robotics, and Communication (ICAIRC). IEEE, 2024. https://doi.org/10.1109/icairc64177.2024.10900304.

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Bodyanskiy, Yevgeniy, Anastasiia Deineko, Iryna Pliss, Olha Chala, and Anna Nortsova. "Matrix Fuzzy-Probabilistic Neural Network in Image Recognition Task." In 2020 IEEE Third International Conference on Data Stream Mining & Processing (DSMP). IEEE, 2020. http://dx.doi.org/10.1109/dsmp47368.2020.9204236.

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Liu, Zhi, Yun Zhang, and Han-xiong Li. "Uncertainty Modeling Design with a Probabilistic Fuzzy Neural Network." In 2007 IEEE International Conference on Control and Automation. IEEE, 2007. http://dx.doi.org/10.1109/icca.2007.4376483.

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Bodyanskiy, Yevgeniy, Anastasiya Deineko, Iryna Pliss, and Olha Chala. "Evolving Fuzzy-Probabilistic Neural Network and Its Online Learning." In 2020 10th International Conference on Advanced Computer Information Technologies (ACIT). IEEE, 2020. http://dx.doi.org/10.1109/acit49673.2020.9208904.

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Faa-Jeng Lin and Kuang-Hsiung Tan. "Squirrel-cage induction generator system using probabilistic fuzzy neural network for wind power applications." In 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2015. http://dx.doi.org/10.1109/fuzz-ieee.2015.7337836.

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Lim, C. P. "Probabilistic Fuzzy ARTMAP: an autonomous neural network architecture for Bayesian probability estimation." In 4th International Conference on Artificial Neural Networks. IEE, 1995. http://dx.doi.org/10.1049/cp:19950545.

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Asgary, R., and K. Mohammadi. "Using fuzzy probabilistic neural network for fault detection in MEMS." In 5th International Conference on Intelligent Systems Design and Applications (ISDA'05). IEEE, 2005. http://dx.doi.org/10.1109/isda.2005.96.

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Kozhomberdieva, Gulnara, Dmitry Burakov, and Georgii Khamchichev. "Neural Network Interpretation of Bayesian Logical-Probabilistic Fuzzy Inference Model." In International Symposium on Automation, Information and Computing. SCITEPRESS - Science and Technology Publications, 2022. http://dx.doi.org/10.5220/0011901700003612.

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Bodyanskiy, Yevgeniy, Olha Chala, Iryna Pliss, and Anastasiia Deineko. "Adaptive Probabilistic Neural Network With Fuzzy Inference And Its Online Learning." In 2020 IEEE 15th International Conference on Computer Sciences and Information Technologies (CSIT). IEEE, 2020. http://dx.doi.org/10.1109/csit49958.2020.9322052.

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Varlamova, Lyudmila, Timur Nabiev, Anna Gubkina, Galina Kienko, and Nadira Tashpulatova. "Traffic control model based on multilayer adaptive fuzzy probabilistic neural network." In 2021 ASIA-PACIFIC CONFERENCE ON APPLIED MATHEMATICS AND STATISTICS. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0090154.

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Reports on the topic "Probabilistic fuzzy neural network"

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Karakowski, Joseph A., and Hai H. Phu. A Fuzzy Hypercube Artificial Neural Network Classifier. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada354805.

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Clark, G. A., M. E. Glinsky, K. R. S. Devi, J. H. Robinson, P. K. Z. Cheng, and G. E. Ford. Automatic event picking in pre-stack migrated gathers using a probabilistic neural network. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/394450.

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Huang, Z., J. Shimeld, and M. Williamson. Application of computer neural network, and fuzzy set logic to petroleum geology, offshore eastern Canada. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1994. http://dx.doi.org/10.4095/194121.

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Rajagopalan, A., G. Washington, G. Rizzoni, and Y. Guezennec. Development of Fuzzy Logic and Neural Network Control and Advanced Emissions Modeling for Parallel Hybrid Vehicles. Office of Scientific and Technical Information (OSTI), 2003. http://dx.doi.org/10.2172/15006009.

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Rose-Pehrsson, Susan, Sean J. Hart, Mark H. Hammond, Daniel T. Gottuk, and Mark T. Wright. Real-Time Probabilistic Neural Network Performance and Optimization for Fire Detection and Nuisance Alarm Rejection: Test Series 2 Results. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada383627.

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