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Journal articles on the topic 'Probabilistic fuzzy neural network'

Author: Grafiati

Published: 3 June 2025

Last updated: 19 July 2025

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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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2

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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3

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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10

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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Kusumoputro, Benyamin, and Teguh P. Arsyad. "Recognizing Odor Mixtures Using Optimized Fuzzy Neural Network Through Genetic Algorithms." Journal of Advanced Computational Intelligence and Intelligent Informatics 9, no. 3 (2005): 290–96. http://dx.doi.org/10.20965/jaciii.2005.p0290.

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Recognizing odor mixtures is rather difficult in artificial odor recognition system, especially when the number of sensors is limited. Classification is further hampered if the number of unlearned odor mixtures classes is increased. We developed a fuzzy-neuro multilayer perceptron as a pattern classifier and compared its recognition with that of the Probabilistic Neural Network and Back-propagation Neural Network. To enhance the recognition capability of the system, we then optimized fuzzy-neuro multilayer perceptron topology by deleting its weak weight connections using Genetic Algorithms. Ex

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12

Kusy, Maciej. "Fuzzy c-means-based architecture reduction of a probabilistic neural network." Neural Networks 108 (December 2018): 20–32. http://dx.doi.org/10.1016/j.neunet.2018.07.012.

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S.SriDevi, Dr M. Renuga Devi,. "Probabilistic Wind Power Forecasting Using Fuzzy Logic." International Journal of Scientific Research and Management (IJSRM) 5, no. 7 (2017): 6497–500. http://dx.doi.org/10.18535/ijsrm/v5i7.87.

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Wind power forecasting is the need of the era. As wind power generation lays a platform for usage of renewable resources. Employing efficient wind turbines enhances the forecasting of generation in short term. However, power generation differs from conventional thermal due to its unstable nature. Henceforth, accurate wind power forecasting plays in managing the variance in supply and demand of the high energy consumption sectors. This paper deals with classifiers to minimize the errors during wind power forecasting due to some data loss. The proposed paper combines fuzzy logic and neural netwo

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Bao, Jinyan, and Xiangzhi Kong. "Weight Optimization Decision Algorithm in (p,q)-Rung Probabilistic Hesitant Orthopair Fuzzy Environments." Symmetry 15, no. 11 (2023): 2043. http://dx.doi.org/10.3390/sym15112043.

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Aiming at the fuzzification of a decision environment and the challenge of determining the weights associated with the interaction among decision-makers, this study offers an original method for (p,q)-rung probabilistic hesitant orthopair fuzzy multi-objective group decision-making, which is founded on the weight optimization principle. Firstly, the notion of a probabilistic hesitant fuzzy set is expanded to a (p,q)-rung. Secondly, the determination of subjective and objective weights is accomplished through the utilization of the Analytic Network Process (ANP) and the Entropy Method. Accordin

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15

Bodyanskiy, Yevgeniy, Anastasiia Deineko, Iryna Pliss, and Olha Chala. "Adaptive Probabilistic Neuro-Fuzzy System and its Hybrid Learning in Medical Diagnostics Task." Open Bioinformatics Journal 14, no. 1 (2021): 123–29. http://dx.doi.org/10.2174/18750362021140100123.

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Background: The medical diagnostic task in conditions of the limited dataset and overlapping classes is considered. Such limitations happen quite often in real-world tasks. The lack of long training datasets during solving real tasks in the problem of medical diagnostics causes not being able to use the mathematical apparatus of deep learning. Additionally, considering other factors, such as in a dataset, classes can be overlapped in the feature space; also data can be specified in various scales: in the numerical interval, numerical ratios, ordinal (rank), nominal and binary, which does not a

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Giordano, Laura, Valentina Gliozzi, and Daniele Theseider DuprÉ. "A conditional, a fuzzy and a probabilistic interpretation of self-organizing maps." Journal of Logic and Computation 32, no. 2 (2022): 178–205. http://dx.doi.org/10.1093/logcom/exab082.

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Abstract In this paper we establish a link between fuzzy and preferential semantics for description logics and self-organizing maps (SOMs), which have been proposed as possible candidates to explain the psychological mechanisms underlying category generalization. In particular, we show that the input/output behavior of a SOM after training can be described by a fuzzy description logic interpretation as well as by a preferential interpretation, based on a concept-wise multipreference semantics, which takes into account preferences with respect to different concepts and has been recently propose

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Bodyanskiy, Yevgeniy, Anastasiia Deineko, Irina Pliss, and Olha Chala. "Fast Probabilistic Neuro-Fuzzy System for Pattern Classification Task." Information Technology and Management Science 23 (December 15, 2020): 12–16. http://dx.doi.org/10.7250/itms-2020-0002.

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The probabilistic neuro-fuzzy system to solve the image classification-recognition task is proposed. The considered system is a “hybrid” of Specht’s probabilistic neural network and the neuro-fuzzy system of Takagi-Sugeno-Kang. It is designed to solve tasks in case of overlapping classes. Also, it is supposed that the initial data that are fed on the input of the system can be represented in numerical, rank, and nominal (binary) scales. The tuning of the network is implemented with the modified procedure of lazy learning based on the concept “neurons at data points”. Such a learning approach a

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18

kulhare, Rachna, and Divakar Singh. "Intrusion Detection System based on Fuzzy C Means Clusteringand Probabilistic Neural Network." International Journal of Computer Applications 74, no. 2 (2013): 30–33. http://dx.doi.org/10.5120/12860-9725.

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19

Lin, Faa-Jeng, Ming-Shi Huang, Po-Yi Yeh, Han-Chang Tsai, and Chi-Hsuan Kuan. "DSP-Based Probabilistic Fuzzy Neural Network Control for Li-Ion Battery Charger." IEEE Transactions on Power Electronics 27, no. 8 (2012): 3782–94. http://dx.doi.org/10.1109/tpel.2012.2187073.

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20

Cheng, Lilin, Haixiang Zang, Tao Ding, et al. "Ensemble Recurrent Neural Network Based Probabilistic Wind Speed Forecasting Approach." Energies 11, no. 8 (2018): 1958. http://dx.doi.org/10.3390/en11081958.

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Wind energy is a commonly utilized renewable energy source, due to its merits of extensive distribution and rich reserves. However, as wind speed fluctuates violently and uncertainly at all times, wind power integration may affect the security and stability of power system. In this study, we propose an ensemble model for probabilistic wind speed forecasting. It consists of wavelet threshold denoising (WTD), recurrent neural network (RNN) and adaptive neuro fuzzy inference system (ANFIS). Firstly, WTD smooths the wind speed series in order to better capture its variation trend. Secondly, RNNs w

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21

Zhai, Junhai, and Wenxiu Zhao. "Ensemble of multiresolution probabilistic neural network classifiers with fuzzy integral for face recognition." Journal of Intelligent & Fuzzy Systems 31, no. 1 (2016): 405–14. http://dx.doi.org/10.3233/ifs-162153.

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22

Jayawardena, Chandimal, Keigo Watanabe, and Kiyotaka Izumi. "Controlling a robot manipulator with fuzzy voice commands using a probabilistic neural network." Neural Computing and Applications 16, no. 2 (2006): 155–66. http://dx.doi.org/10.1007/s00521-006-0056-8.

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23

Chen, Syuan‐Yi, and Tung‐Sheng Liu. "Intelligent tracking control of a PMLSM using self‐evolving probabilistic fuzzy neural network." IET Electric Power Applications 11, no. 6 (2017): 1043–54. http://dx.doi.org/10.1049/iet-epa.2016.0819.

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24

吴, 新好. "Financial Asset Yield Series Forecasting Based on Risk-Neutral Fuzzy Bilinear Regression and Probabilistic Neural Network." Operations Research and Fuzziology 11, no. 02 (2021): 190–205. http://dx.doi.org/10.12677/orf.2021.112024.

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25

Chandomi Castellanos, Eduardo, and Elías N. Escobar Gómez. "M-ANFIS model to determine the urban travel time with uncertain edges." International Journal of Combinatorial Optimization Problems and Informatics 12, no. 3 (2021): 63–78. https://doi.org/10.61467/2007.1558.2021.v12i3.231.

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In this paper, we propose a method to solve the shortest path problem using imprecise variables. The model calculates values for each edge of the network utilize base a fuzzy logic scheme and an adaptive architecture with neural networks. For the uncertainty, the effect of three variables for each street considers the state of the streets, traffic zones, and rainfall (intensity of rain) with an adaptable neural networks architecture. The membership functions experimentally calculate, getting times closer to the real. The model evaluates the uncertainty for each of the network's edges (streets)

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Taha, Mohammed Hasan, Dheyaa Mohammed Sahab, and Waleed Jumana. "DEVELOPMENT OF BREAST CANCER DIAGNOSIS SYSTEM BASED ON FUZZY LOGIC AND PROBABILISTIC NEURAL NETWORK." Eastern-European Journal of Enterprise Technologies 4, no. 9 (106) (2020): 6–13. https://doi.org/10.15587/1729-4061.2020.202820.

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Breast cancer is one of the most common kinds of cancers that infect females in the whole world. It has happened when the cells in breast tissues start to grow in an uncontrollable way. Because it leads to death, early detection and diagnosis is a very important task to save the patient's life. Due to the restriction of human observers, computer plays a significant role in detecting early cancer signs. The proposed system uses a multi-resolution analysis and a top-hat operation for detecting the suspicious regions in a mammogram image. The discrete wavelet transform feature analysis is uti

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Shang, Xiao Jing. "The Identification of Neurons Research." Advanced Materials Research 756-759 (September 2013): 2813–18. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.2813.

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In view of the present medical neurons characteristic cognition and human brain plan in the neurons of the limitation of recognition, this paper puts forward the neurons identification method. First the L - Measure software to neuron geometry feature extraction, and then to extract high dimensional feature through the principal component analysis dimension reduction processing. Combined classifier with pyramidal neurons, general Ken wild neurons, motor neuron, sensory neurons, double neurons, level 3 neurons and multistage neurons 7 kinds of neurons are classified. Experimental results prove t

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Kawaji, Shigeyasu, and Yuehui Chen. "Evolving Neurofuzzy System by Hybrid Soft Computing Approaches for System Identification." Journal of Advanced Computational Intelligence and Intelligent Informatics 5, no. 4 (2001): 220–28. http://dx.doi.org/10.20965/jaciii.2001.p0220.

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This paper studies optimizing neurofuzzy system using a hybrid approach of a modified probabilistic incremental program evolution algorithm (MPIPE), random search algorithm, and evolutionary programming (EP). Neurofuzzy system is a combination of fuzzy system and neural network. The performance of a neurofuzzy system depends largely on selection of fuzzy membership functions, partition of input space and fuzzy rules. Two neurofuzzy models, additive and direct, are proposed in which neurofuzzy system calculation is based on tree structural representation. Without prior knowledge of the plant, p

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Palomino, Lizeth Vargas, Valder Steffen, and Roberto Mendes Finzi Neto. "Probabilistic Neural Network and Fuzzy Cluster Analysis Methods Applied to Impedance-Based SHM for Damage Classification." Shock and Vibration 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/401942.

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Impedance-based structural health monitoring technique is performed by measuring the variation of the electromechanical impedance of the structure caused by the presence of damage. The impedance signals are collected from patches of piezoelectric material bonded on the surface of the structure (or embedded). Through these piezoceramic sensor-actuators, the electromechanical impedance, which is directly related to the mechanical impedance of the structure, is obtained. Based on the variation of the impedance signals, the presence of damage can be detected. A particular damage metric is used to

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Kuk, Kristijan, Aleksandar Stanojević, Petar Čisar, et al. "Applications of Fuzzy Logic and Probabilistic Neural Networks in E-Service for Malware Detection." Axioms 13, no. 9 (2024): 624. http://dx.doi.org/10.3390/axioms13090624.

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The key point in the process of agent-based management in e-service for malware detection (according to accuracy criteria) is a decision-making process. To determine the optimal e-service for malware detection, two concepts were investigated: Fuzzy Logic (FL) and Probabilistic Neural Networks (PNN). In this study, three evolutionary variants of fuzzy partitioning, including regular, hierarchical fuzzy partitioning, and k-means, were used to automatically process the design of the fuzzy partition. Also, this study demonstrates the application of a feature selection method to reduce the dimensio

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CHEETHAM, WILLIAM, SIMON SHIU, and ROSINA O. WEBER. "Soft case-based reasoning." Knowledge Engineering Review 20, no. 3 (2005): 267–69. http://dx.doi.org/10.1017/s0269888906000579.

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The aim of this commentary is to discuss the contribution of soft computing—a consortium of fuzzy logic, neural network theory, evolutionary computing, and probabilistic reasoning—to the development of case-based reasoning (CBR) systems. We will describe how soft computing has been used in case representation, retrieval, adaptation, reuse, and case-base maintenance, and then present a brief summary of six CBR applications that use soft computing techniques.

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Mohammed Hasan, Taha, Sahab Dheyaa Mohammed, and Jumana Waleed. "Development of breast cancer diagnosis system based on fuzzy logic and probabilistic neural network." Eastern-European Journal of Enterprise Technologies 4, no. 9 (106) (2020): 6–13. http://dx.doi.org/10.15587/1729-4061.2020.202820.

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Xu, Jing, Zhongbin Wang, Chao Tan, and Xinhua Liu. "A State Recognition Approach for Complex Equipment Based on a Fuzzy Probabilistic Neural Network." Algorithms 9, no. 2 (2016): 34. http://dx.doi.org/10.3390/a9020034.

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Lin, Faa-Jeng, Su-Ying Lu, Jo-Yu Chao, and Jin-Kuan Chang. "Intelligent PV Power Smoothing Control Using Probabilistic Fuzzy Neural Network with Asymmetric Membership Function." International Journal of Photoenergy 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/8387909.

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An intelligent PV power smoothing control using probabilistic fuzzy neural network with asymmetric membership function (PFNN-AMF) is proposed in this study. First, a photovoltaic (PV) power plant with a battery energy storage system (BESS) is introduced. The BESS consisted of a bidirectional DC/AC 3-phase inverter and LiFePO4 batteries. Then, the difference of the actual PV power and smoothed power is supplied by the BESS. Moreover, the network structure of the PFNN-AMF and its online learning algorithms are described in detail. Furthermore, the three-phase output currents of the PV power plan

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E, Manohar, and D. Shalini Punithavathani. "Online Products Recommendation System using Genetic Kernel Fuzzy C-Means and Probabilistic Neural Network." International Journal of Business Intelligence and Data Mining 1, no. 1 (2018): 1. http://dx.doi.org/10.1504/ijbidm.2018.10011146.

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Manohar, E., and D. Shalini Punithavathani. "Online products recommendation system using genetic kernel fuzzy C-means and probabilistic neural network." International Journal of Business Intelligence and Data Mining 17, no. 1 (2020): 32. http://dx.doi.org/10.1504/ijbidm.2020.108029.

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Tan, Kuang-Hsiung, Faa-Jeng Lin, Cheng-Ming Shih, and Che-Nan Kuo. "Intelligent Control of Microgrid With Virtual Inertia Using Recurrent Probabilistic Wavelet Fuzzy Neural Network." IEEE Transactions on Power Electronics 35, no. 7 (2020): 7451–64. http://dx.doi.org/10.1109/tpel.2019.2954740.

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Moradifar, Amir, Asghar Akbari Foroud, and Khalil Gorgani Firouzjah. "Comprehensive identification of multiple harmonic sources using fuzzy logic and adjusted probabilistic neural network." Neural Computing and Applications 31, S1 (2017): 543–56. http://dx.doi.org/10.1007/s00521-017-3022-8.

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Bardak, F. Kebire, M. Nuri Seyman, and Feyzullah Temurtaş. "Hybrid Classification Model for Emotion Prediction from EEG Signals: A Comparative Study." JUCS - Journal of Universal Computer Science 29, no. (12) (2023): 1424–38. https://doi.org/10.3897/jucs.99542.

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This paper introduces a novel hybrid algorithm for emotion classification based on electroencephalogram (EEG) signals. The proposed hybrid model consists of two layers: the first layer includes three parallel adaptive neuro-fuzzy inference systems (ANFIS), and the second layer called the adaptive network comprises various models such as radial basis function neural network (RBFNN), probabilistic neural network (PNN), and ANFIS. It is examined that the feature distribution graphs of the dataset, which includes three emotion classes: positive, negative, and neutral, and selected the most appropr

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Zhang, Zhengjin, Guilin Huang, Yong Zhang, et al. "Research on PMF Model Based on BP Neural Network Ensemble Learning Bagging and Fuzzy Clustering." Complexity 2021 (July 21, 2021): 1–9. http://dx.doi.org/10.1155/2021/9985894.

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Probability matrix factorization model can be used to solve the problem of high-dimensional sparsity of user and rating data in the recommender systems. However, most of the existing methods use the user to model the item rating, ignoring the relationship between the user and the item, so the accuracy of user-item rating prediction is still low. Therefore, this paper proposes a probabilistic matrix factorization model based on BP neural network ensemble learning, bagging, and fuzzy clustering. Firstly, the membership function of fuzzy clustering and the selection of cluster center are used to

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Zhang, Zhengjin, Qilin Wu, Yong Zhang, and Li Liu. "Movie recommendation model based on probabilistic matrix decomposition using hybrid AdaBoost integration." PeerJ Computer Science 9 (April 21, 2023): e1338. http://dx.doi.org/10.7717/peerj-cs.1338.

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In recent years, recommendation systems have already played a significant role in major streaming video platforms.The probabilistic matrix factorization (PMF) model has advantages in addressing high-dimension problems and rating data sparsity in the recommendation system. However, in practical application, PMF has poor generalization ability and low prediction accuracy. For this reason, this article proposes the Hybrid AdaBoost Ensemble Method. Firstly, we use the membership function and the cluster center selection in fuzzy clustering to calculate the scoring matrix of the user-items. Secondl

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Bardak, F. Kebire, M. Nuri Seyman, and Feyzullah Temurtaş. "Hybrid Classification Model for Emotion Prediction from EEG Signals: A Comparative Study." JUCS - Journal of Universal Computer Science 29, no. 12 (2023): 1424–38. http://dx.doi.org/10.3897/jucs.99542.

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This paper introduces a novel hybrid algorithm for emotion classification based on electroencephalogram (EEG) signals. The proposed hybrid model consists of two layers: the first layer includes three parallel adaptive neuro-fuzzy inference systems (ANFIS), and the second layer called the adaptive network comprises various models such as radial basis function neural network (RBFNN), probabilistic neural network (PNN), and ANFIS. It is examined that the feature distribution graphs of the dataset, which includes three emotion classes: positive, negative, and neutral, and selected the most appropr

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Abrahart, R. J., and L. See. "Multi-model data fusion for river flow forecasting: an evaluation of six alternative methods based on two contrasting catchments." Hydrology and Earth System Sciences 6, no. 4 (2002): 655–70. http://dx.doi.org/10.5194/hess-6-655-2002.

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Abstract. This paper evaluates six published data fusion strategies for hydrological forecasting based on two contrasting catchments: the River Ouse and the Upper River Wye. The input level and discharge estimates for each river comprised a mixed set of single model forecasts. Data fusion was performed using: arithmetic-averaging, a probabilistic method in which the best model from the last time step is used to generate the current forecast, two different neural network operations and two different soft computing methodologies. The results from this investigation are compared and contrasted us

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ZHAI, JUNHAI, HONGYU XU, and YAN LI. "FUSION OF EXTREME LEARNING MACHINE WITH FUZZY INTEGRAL." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 21, supp02 (2013): 23–34. http://dx.doi.org/10.1142/s0218488513400138.

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Extreme learning machine (ELM) is an efficient and practical learning algorithm used for training single hidden layer feed-forward neural networks (SLFNs). ELM can provide good generalization performance at extremely fast learning speed. However, ELM suffers from instability and over-fitting, especially on relatively large datasets. Based on probabilistic SLFNs, an approach of fusion of extreme learning machine (F-ELM) with fuzzy integral is proposed in this paper. The proposed algorithm consists of three stages. Firstly, the bootstrap technique is employed to generate several subsets of origi

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Corne, Simon A., Stephen J. Carver, William E. Kunin, Jack J. Lennon, and Willem W. S. van van Hees. "Predicting Forest Attributes in Southeast Alaska Using Artificial Neural Networks." Forest Science 50, no. 2 (2004): 259–76. http://dx.doi.org/10.1093/forestscience/50.2.259.

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Abstract Artificial neural network (ANN) methods are used to predict forest characteristics. The data source is the Southeast Alaska (SEAK) Grid Inventory, a ground survey compiled by the USDA Forest Service at several thousand sites. The main objective of this article is to predict characteristics at unsurveyed locations between grid sites. A secondary objective is to evaluate the relative performance of different ANNs. Data from the grid sites are used to train six ANNs: multilayer perceptron, fuzzy ARTMAP, probabilistic, generalized regression, radial basis function, and learning vector qua

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Jayanthi, M. G., and Dandinashivara Revanna Shashikumar. "Cucumber disease detection using adaptively regularised kernel-based fuzzy C-means and probabilistic neural network." International Journal of Computational Vision and Robotics 10, no. 5 (2020): 385. http://dx.doi.org/10.1504/ijcvr.2020.10029219.

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Mondal, Ajoy, Susmita Ghosh, and Ashish Ghosh. "Partially Camouflaged Object Tracking using Modified Probabilistic Neural Network and Fuzzy Energy based Active Contour." International Journal of Computer Vision 122, no. 1 (2016): 116–48. http://dx.doi.org/10.1007/s11263-016-0959-5.

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Kuo, Chung-Feng Jeffrey, Chung-Yang Shih, and Chien-Tung Max Hsu. "Pattern-making simulation on embroidery using probabilistic neural network and texture fitting method." Textile Research Journal 81, no. 20 (2011): 2082–94. http://dx.doi.org/10.1177/0040517511414980.

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Embroidery fabric is different from other planar fabrics such as printed fabrics and twill fabrics. Because embroidery fabrics have inherent solid texture patterns, furry edges, voids and thickness shadows, it is very difficult to filter and simulate texture patterns and this is the bottleneck for embroidery automation. Therefore, this paper proposes the texture fitting method. The texture fitting method is a kind of nonfiltered digital image processing method. For embroidery fabrics full of multiple single-connected, single-color and single-texture closed regions, the texture fitting method c

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Kabanda, Gabriel. "Bayesian Network Model for a Zimbabwean Cybersecurity System." Oriental journal of computer science and technology 12, no. 4 (2020): 147–67. http://dx.doi.org/10.13005/ojcst12.04.02.

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The purpose of this research was to develop a structure for a network intrusion detection and prevention system based on the Bayesian Network for use in Cybersecurity. The phenomenal growth in the use of internet-based technologies has resulted in complexities in cybersecurity subjecting organizations to cyberattacks. What is required is a network intrusion detection and prevention system based on the Bayesian Network structure for use in Cybersecurity. Bayesian Networks (BNs) are defined as graphical probabilistic models for multivariate analysis and are directed acyclic graphs that have an a

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Shao, Ke-Yong, Ao Feng, and Ting-Ting Wang. "Fixed-Time Sliding Mode Synchronization of Uncertain Fractional-Order Hyperchaotic Systems by Using a Novel Non-Singleton-Interval Type-2 Probabilistic Fuzzy Neural Network." Fractal and Fractional 7, no. 3 (2023): 247. http://dx.doi.org/10.3390/fractalfract7030247.

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In this study, we proposed a sliding mode control method based on fixed-time sliding mode surface for the synchronization of uncertain fractional-order hyperchaotic systems. In addition, we proposed a novel self-evolving non-singleton-interval type-2 probabilistic fuzzy neural network (SENSIT2PFNN) to estimate the uncertain dynamics of the system. Moreover, an adaptive compensator was designed to eliminate the influences of random uncertainty and fuzzy uncertainty, thereby yielding an asymptotically stable controlled system. Furthermore, an adaptive law was introduced to optimize the consequen

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