Relevant bibliographies by topics / Probabilistic neural network (PNN) algorithm

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  2. Dissertations / Theses
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  4. Conference papers

Academic literature on the topic 'Probabilistic neural network (PNN) algorithm'

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Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Probabilistic neural network (PNN) algorithm"

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Alweshah, Mohammed, Mustafa Alessa, Saleh Alkhalaileh, Sofian Kassaymeh, and Bilal Abu-Salih. "Hybrid Aquila optimizer for efficient classification with probabilistic neural networks." Multiagent and Grid Systems 20, no. 1 (2024): 41–68. http://dx.doi.org/10.3233/mgs-230065.

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The model of a probabilistic neural network (PNN) is commonly utilized for classification and pattern recognition issues in data mining. An approach frequently used to enhance its effectiveness is the adjustment of PNN classifier parameters through the outcomes of metaheuristic optimization strategies. Since PNN employs a limited set of instructions, metaheuristic algorithms provide an efficient way to modify its parameters. In this study, we have employed the Aquila optimizer algorithm (AO), a contemporary algorithm, to modify PNN parameters. We have proposed two methods: Aquila optimizer bas
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Shakya, Subarna. "Probabilistic Neural Network based Managing Algorithm for Building Automation System." December 2021 3, no. 4 (2021): 272–83. http://dx.doi.org/10.36548/jaicn.2021.4.001.

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A building automation system is a centralized intelligent system, which controls the operation of energy, security, water, and safety by the help of hardware and software modules. The general software modules employed for automation process have an algorithm with pre-determined decisions. However, such pre-determined decision algorithms won’t work in a proper manner at all situations like a human brain. Therefore a human biological inspired algorithms are developed in recent days and termed as neural network algorithms. The Probabilistic Neural Network (PNN) is a kind of artificial neural netw
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Wang, Benyou, and Li Gu. "Detection of Network Intrusion Threat Based on the Probabilistic Neural Network Model." Information Technology and Control 48, no. 4 (2019): 618–25. http://dx.doi.org/10.5755/j01.itc.48.4.24036.

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With the popularity of the Internet, people's lives are becoming more and more convenient. However, the network security problems are becoming increasingly serious. This paper, aiming to better protect users’ network security from the internal and external malicious attacks, briefly introduces the probabilistic neural network and principal component analysis method, and combines them for detection of network intrusion data. Simulation analysis of Probabilistic Neural Network (PNN) and Principal Component Analysis-Probabilistic Neural Network (PCA-PNN) are carried out in MATLAB software. The re
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Li, Xin Guang, Min Feng Yao, Li Rui Jian, and Zhen Jiang Li. "The Application of Probabilistic Neural Network in Speech Recognition Based on Partition Clustering." Applied Mechanics and Materials 263-266 (December 2012): 2173–78. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2173.

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A probabilistic neural network (PNN) speech recognition model based on the partition clustering algorithm is proposed in this paper. The most important advantage of PNN is that training is easy and instantaneous. Therefore, PNN is capable of dealing with real time speech recognition. Besides, in order to increase the performance of PNN, the selection of data set is one of the most important issues. In this paper, using the partition clustering algorithm to select data is proposed. The proposed model is tested on two data sets from the field of spoken Arabic numbers, with promising results. The
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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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Li, Li-li, Kun Chen, Jian-min Gao, and Hui Li. "Research on Quality Anomaly Recognition Method Based on Optimized Probabilistic Neural Network." Shock and Vibration 2020 (November 11, 2020): 1–11. http://dx.doi.org/10.1155/2020/6694732.

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Aiming at the problems of the lack of abnormal instances and the lag of quality anomaly discovery in quality database, this paper proposed the method of recognizing quality anomaly from the quality control chart data by probabilistic neural network (PNN) optimized by improved genetic algorithm, which made up deficiencies of SPC control charts in practical application. Principal component analysis (PCA) reduced the dimension and extracted the feature of the original data of a control chart, which reduced the training time of PNN. PNN recognized successfully both single pattern and mixed pattern
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Ji, Xin Xin, and Zhong Min Wang. "Faults Diagnosis of Ball Bearing Based on Probabilistic Neural Network." Applied Mechanics and Materials 543-547 (March 2014): 1149–52. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.1149.

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To solve the difficulties of establishing precise mathematical model of ball bearing fault diagnosis, a classification method based on probabilistic neural network (PNN) used for ball bearing fault mode classification is proposed. Firstly, this paper analyzed the basic theory of PNN, and then a mapping relationship between feature vector and fault mode is set up based on PNN. Secondly, selections of ball bearing fault features and practical procedure of neural network setting and training are discussed. Experiments and compared with the algorithm of back propagation neural network (BPNN) prove
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Siregar, Siti Julianita, Ahmadi Irmansyah Lubis, and Erika Fahmi Ginting. "Penerapan Neural Network Dalam Klasifikasi Citra Permainan Batu Kertas Gunting dengan Probabilistic Neural Network." Building of Informatics, Technology and Science (BITS) 3, no. 3 (2021): 420–25. http://dx.doi.org/10.47065/bits.v3i3.1143.

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In this research, an image classification model was developed to distinguish hand objects pointing at rock, paper, and scissors using one of the popular image classification methods, namely the Probabilistic Neural Network. Probabilistic Neural Network is a method in an artificial neural network that is used to classify a category based on the results of calculating the distance between the density function and the probability. PNN has 4 stages of processing, namely Input Layer, Pattern Layer, Summation Layer, and Output Layer. Tests in the study were carried out with a total of 60 testing dat
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Pambudi, Yoyok Dwi Setyo. "PARTICLE SWARM OPTIMIZATION BASED PROBABILISTIC NEURAL NETWORK FOR CLASSIFICATION OF SEVERE ACCIDENT OF NUCLEAR REACTOR." JURNAL TEKNOLOGI REAKTOR NUKLIR TRI DASA MEGA 23, no. 3 (2021): 99. http://dx.doi.org/10.17146/tdm.2021.23.3.6247.

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Due to its danger and complexity, the identification and prediction of major severe accident scenarios from an initiating event of a nuclear power plant remains a challenging task. This paper aims to classify severe accident at the Advanced Power Reactor (APR) 1400, which includes the loss of coolant accidents (LOCA), total loss of feedwater (TLOFW), station blackout (SBO), and steam generator tube rupture (SGTR) using a standard probabilistic neural network (PNN) and Particle Swarm Optimization Based Probabilistic Neural Network (PSO PNN). The algorithm has been implemented in MATLAB. The exp
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Pambudi, Yoyok Dwi Setyo. "PARTICLE SWARM OPTIMIZATION BASED PROBABILISTIC NEURAL NETWORK FOR CLASSIFICATION OF SEVERE ACCIDENT OF NUCLEAR REACTOR." JURNAL TEKNOLOGI REAKTOR NUKLIR TRI DASA MEGA 23, no. 3 (2021): 99. http://dx.doi.org/10.17146/tdm.2021.23.3.6247.

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Due to its danger and complexity, the identification and prediction of major severe accident scenarios from an initiating event of a nuclear power plant remains a challenging task. This paper aims to classify severe accident at the Advanced Power Reactor (APR) 1400, which includes the loss of coolant accidents (LOCA), total loss of feedwater (TLOFW), station blackout (SBO), and steam generator tube rupture (SGTR) using a standard probabilistic neural network (PNN) and Particle Swarm Optimization Based Probabilistic Neural Network (PSO PNN). The algorithm has been implemented in MATLAB. The exp
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Dissertations / Theses on the topic "Probabilistic neural network (PNN) algorithm"

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Schliebs, Stefan. "Heterogeneous probabilistic models for optimisation and modelling of evolving spiking neural networks." AUT University, 2010. http://hdl.handle.net/10292/963.

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This thesis proposes a novel feature selection and classification method employing evolving spiking neural networks (eSNN) and evolutionary algorithms (EA). The method is named the Quantum-inspired Spiking Neural Network (QiSNN) framework. QiSNN represents an integrated wrapper approach. An evolutionary process evolves appropriate feature subsets for a given classification task and simultaneously optimises the neural and learning-related parameters of the network. Unlike other methods, the connection weights of this network are determined by a fast one-pass learning algorithm which dramaticall
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FERREIRA, Aida Araújo. "Comparação de arquiteturas de redes neurais para sistemas de reconheceimento de padrões em narizes artificiais." Universidade Federal de Pernambuco, 2004. https://repositorio.ufpe.br/handle/123456789/2465.

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Made available in DSpace on 2014-06-12T15:58:28Z (GMT). No. of bitstreams: 2 arquivo4572_1.pdf: 1149011 bytes, checksum: 92aae8f6f9b5145bfcecb94d96dbbc0b (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2004<br>Instituto Federal de Educação, Ciência e Tecnologia de Pernambuco<br>Um nariz artificial é um sistema modular composto de duas partes principais: um sistema sensor, formado de elementos que detectam odores e um sistema de reconhecimento de padrões que classifica os odores detectados. Redes neurais artificiais têm sido utilizadas
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Muñoz, Mas Rafael. "Multivariate approaches in species distribution modelling: Application to native fish species in Mediterranean Rivers." Doctoral thesis, Universitat Politècnica de València, 2018. http://hdl.handle.net/10251/76168.

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This dissertation focused in the comprehensive analysis of the capabilities of some non-tested types of Artificial Neural Networks, specifically: the Probabilistic Neural Networks (PNN) and the Multi-Layer Perceptron (MLP) Ensembles. The analysis of the capabilities of these techniques was performed using the native brown trout (Salmo trutta; Linnaeus, 1758), the bermejuela (Achondrostoma arcasii; Robalo, Almada, Levy & Doadrio, 2006) and the redfin barbel (Barbus haasi; Mertens, 1925) as target species. The analyses focused in the predictive capabilities, the interpretability of the models an
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Bai, Hao. "Machine learning assisted probabilistic prediction of long-term fatigue damage and vibration reduction of wind turbine tower using active damping system." Thesis, Normandie, 2021. http://www.theses.fr/2021NORMIR01.

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Cette thèse est consacrée au développement d'un système d'amortissement actif pour la réduction des vibrations du mât d'éoliennes en cas de vent avec rafales et de vent avec turbulence. La présence de vibrations entraîne souvent soit une déflexion ultime au sommet du mât d'éolienne, soit une défaillance due à la fatigue du matériau près du bas du mât d'éolienne. De plus, étant donné la nature aléatoire de l'état du vent, il est indispensable d'examiner ce problème d'un point de vue probabiliste. Dans ce travail, un cadre probabiliste d'analyse de la fatigue est développé et amélioré en utilisa
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Book chapters on the topic "Probabilistic neural network (PNN) algorithm"

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Hafdaoui, Hichem, Cherifa Mehadjebia, and Djamel Benatia. "Using Probabilistic Neural Network (PNN) for Extracting Acoustic Microwaves (BAW) in Piezoelectric Material." In Artificial Intelligence in Renewable Energetic Systems. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73192-6_32.

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Izonin, Ivan, Roman Tkachenko, and Michal Greguš. "I-PNN: An Improved Probabilistic Neural Network for Binary Classification of Imbalanced Medical Data." In Lecture Notes in Computer Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12426-6_12.

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Nuntalid, Nuttapod, Kshitij Dhoble, and Nikola Kasabov. "EEG Classification with BSA Spike Encoding Algorithm and Evolving Probabilistic Spiking Neural Network." In Neural Information Processing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24955-6_54.

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Kusy, Maciej, and Roman Zajdel. "Probabilistic Neural Network Training Procedure with the Use of SARSA Algorithm." In Artificial Intelligence and Soft Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19324-3_5.

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Narendra, V. G., and K. Govardhan Hegde. "Intelligent System to Evaluate the Quality of Orange, Lemon, Sweet Lime and Tomato Using Back-Propagation Neural-Network (BPNN) and Probabilistic Neural Network (PNN)." In Communications in Computer and Information Science. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0108-1_34.

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Xu, Jian-jun, Chao Liu, and Quan Zhou. "Study on Steam Turbine Fault Diagnosis of Fish-Swarm Optimized Probabilistic Neural Network Algorithm." In Lecture Notes in Electrical Engineering. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19706-2_9.

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S., Nivetha, and Ananthi Sheshasaayee. "An Element Gathering Optimization-Based Probabilistic Multi-model Neural Network Classification Algorithm for Stock Market Prediction." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2839-8_30.

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Morita, Shunpei, Hiroto Iguchi, and Tetsuya Hoya. "A Class Incremental Learning Algorithm for a Compact-Sized Probabilistic Neural Network and Its Empirical Comparison with Multilayered Perceptron Neural Networks." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-47634-1_22.

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Liu, Zheng, and Hao Wang. "Research on Process Diagnosis of Severe Accidents Based on Deep Learning and Probabilistic Safety Analysis." In Springer Proceedings in Physics. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1023-6_54.

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AbstractSevere accident process diagnosis provides data basis for severe accident prognosis, positive and negative effect evaluation of Severe Accident Management Guidelines (SAMGs), especially to quickly diagnose Plant Damage State (PDS) for operators in the main control room or personnel in the Technical Support Center (TSC) based on historic data of the limited number of instruments during the operation transition from Emergency Operation Procedures (EOPs) to SAMGs. This diagnosis methodology is based on tens of thousands of simulations of severe accidents using the integrated analysis prog
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Tsuji, Toshio, Nan Bu, and Osamu Fukuda. "A Recurrent Probabilistic Neural Network for EMG Pattern Recognition." In Pattern Recognition Technologies and Applications. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-807-9.ch017.

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In the field of pattern recognition, probabilistic neural networks (PNNs) have been proven as an important classifier. For pattern recognition of EMG signals, the characteristics usually used are: (1) amplitude, (2) frequency, and (3) space. However, significant temporal characteristic exists in the transient and non-stationary EMG signals, which cannot be considered by traditional PNNs. In this article, a recurrent PNN, called recurrent log-linearized Gaussian mixture network (R-LLGMN), is introduced for EMG pattern recognition, with the emphasis on utilizing temporal characteristics. The str
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Conference papers on the topic "Probabilistic neural network (PNN) algorithm"

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S, Sabitha Rani B., Malu G, and Elizabeth Sherly. "Kidney Stone Detection from CT images using Probabilistic Neural Network(PNN) and Watershed Algorithm." In 2023 International Conference on Advances in Intelligent Computing and Applications (AICAPS). IEEE, 2023. http://dx.doi.org/10.1109/aicaps57044.2023.10074562.

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Baek, Dae Seong, Chengjun Li, Jung Soo Nam, et al. "A Study on Condition Monitoring and Diagnosis of Injection Molding Process Using Probabilistic Neural Network Method." In ASME 2014 International Manufacturing Science and Engineering Conference collocated with the JSME 2014 International Conference on Materials and Processing and the 42nd North American Manufacturing Research Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/msec2014-4058.

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The objective of this research is the development of condition diagnosis model for injection molding process based on wavelet packet decomposition (WPD), feature extraction from cavity pressure, nozzle pressure and screw position signals and probability neural network (PNN) method. The node energies from the WPD of cavity and nozzle pressure signals are identified. In addition, five (5), seven (7) and two (2) critical features are extracted from the cavity pressure, nozzle pressure and screw position signals via the new feature extraction algorithm. The node energies and critical features are
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Sa'adah, Siti, and Melati Suci Pratiwi. "Classification of Customer Actions on Digital Money Transactions on PaySim Mobile Money Simulator using Probabilistic Neural Network (PNN) Algorithm." In 2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI). IEEE, 2020. http://dx.doi.org/10.1109/isriti51436.2020.9315344.

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Loboda, Igor, and Sergiy Yepifanov. "On the Selection of an Optimal Pattern Recognition Technique for Gas Turbine Diagnosis." In ASME Turbo Expo 2013: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/gt2013-95198.

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Efficiency of gas turbine monitoring systems primarily depends on the accuracy of employed algorithms, in particular, pattern recognition techniques to diagnose gas path faults. In investigations many techniques were applied to recognize gas path faults, but recommendations on selecting the best technique for real monitoring systems are still insufficient and often contradictory. In our previous works, three recognition techniques were compared under different conditions of gas turbine diagnosis. The comparative analysis has shown that all these techniques yield practically the same accuracy f
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Abbas, Mohammed A., Watheq J. Al-Mudhafar, Ahmed Alsubaih, Ali Al-Maliki, and Ali Al-Sukaini. "Developing a Novel Machine Learning-Based Petrophysical Rock Typing (PRT) Classification: Applied on Heterogenous Carbonate Reservoirs." In SPE Annual Technical Conference and Exhibition. SPE, 2024. http://dx.doi.org/10.2118/220964-ms.

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Abstract Petrophysical Rock Typing (PRT) from core and well log data is a valuable tool for reservoir discrimination and recoverable reserve estimation in heterogeneous multimodal pore carbonate systems. However, the traditional PRT methods are often ineffective in these systems because of the complex pore geometry and distribution. This work introduces an innovative PRT technique that utilizes cutting-edge machine learning algorithms to categorize petrophysical rock types in a supergiant oil and gas field located in southern Iraq. The study initially employs Ward's hierarchical clustering met
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Loboda, Igor. "Gas Turbine Fault Classification Using Probability Density Estimation." In ASME Turbo Expo 2014: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/gt2014-27265.

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Diagnostics is an important aspect of a condition based maintenance program. To develop an effective gas turbine monitoring system in short time, the recommendations on how to optimally design every system algorithm are required. This paper deals with choosing a proper fault classification technique for gas turbine monitoring systems. To classify gas path faults, different artificial neural networks are typically employed. Among them the Multilayer Perceptron (MLP) is the mostly used. Some comparative studies referred to in the introduction show that the MLP and some other techniques yield pra
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Donat, William, Kihoon Choi, Woosun An, Satnam Singh, and Krishna Pattipati. "Data Visualization, Data Reduction and Classifier Fusion for Intelligent Fault Detection and Diagnosis in Gas Turbine Engines." In ASME Turbo Expo 2007: Power for Land, Sea, and Air. ASMEDC, 2007. http://dx.doi.org/10.1115/gt2007-28343.

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In this paper, we investigate four key issues associated with data-driven approaches for fault classification using the Pratt and Whitney commercial dual-spool turbofan engine data as a test case. The four issues considered here include: (1) Can we characterize, a priori, the difficulty of fault classification via self-organizing maps? (2) Do data reduction techniques improve fault classification performance and enable the implementation of data-driven classification techniques in memory-constrained digital electronic control units (DECUs)?, (3) When does adaptive boosting, an incremental fusi
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Andriani Putri, Intan, Al Rubaiyn, and Agi Robby Rizaldi. "Probabilistic Neural Network (PNN) for Tight Sand Reservoir Characterization." In International Geophysical Conference, Beijing, China, 24-27 April 2018. Society of Exploration Geophysicists and Chinese Petroleum Society, 2018. http://dx.doi.org/10.1190/igc2018-307.

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Iounousse, Jawad, Ahmed Farhi, Ahmed El motassadeq, Hassan Chehouani, and Salah Erraki. "Unsupervised classification of grayscale image using Probabilistic Neural Network (PNN)." In 2012 International Conference on Multimedia Computing and Systems (ICMCS). IEEE, 2012. http://dx.doi.org/10.1109/icmcs.2012.6320161.

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Zaknich, A., C. J. S. deSilva, and Y. Attikiouzel. "A modified probabilistic neural network (PNN) for nonlinear time series analysis." In 1991 IEEE International Joint Conference on Neural Networks. IEEE, 1991. http://dx.doi.org/10.1109/ijcnn.1991.170617.

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