Relevant bibliographies by topics / Training systems in artificial neural networks

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Training systems in artificial neural networks'

Author: Grafiati
Published: 5 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Training systems in artificial neural networks"

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Volodymyr, Dudnyk, Sinenko Yuriy, Matsyk Mykhailo, et al. "DEVELOPMENT OF A METHOD FOR TRAINING ARTIFICIAL NEURAL NETWORKS FOR INTELLIGENT DECISION SUPPORT SYSTEMS." Eastern-European Journal of Enterprise Technologies 3, no. 2 (105) (2020): 37–47. https://doi.org/10.15587/1729-4061.2020.203301.

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A method for training artificial neural networks for intelligent decision support systems has been developed. The method provides training not only of the synaptic weights of the artificial neural network, but also the type and parameters of the membership function, architecture and parameters of an individual network node. The architecture of artificial neural networks is trained if it is not possible to ensure the specified quality of functioning of artificial neural networks due to the training of parameters of an artificial neural network. The choice of architecture, type and parameters of
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Didmanidze, I. Sh, G. A. Kakhiani, and D. Z. Didmanidze. "TRAINING OF ARTIFICIAL NEURAL NETWORK." Journal of Numerical and Applied Mathematics, no. 1 (135) (2021): 110–14. http://dx.doi.org/10.17721/2706-9699.2021.1.14.

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The methodology of neural networks is even more often applied in tasks of management and decision-making, including in the sphere of trade and finance. The basis of neural networks is made by nonlinear adaptive systems which proved the efficiency at the solution of problems of forecasting.
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Mahdi, Qasim Abbood, Andrii Shyshatskyi, Oleksandr Symonenko, et al. "Development of a method for training artificial neural networks for intelligent decision support systems." Eastern-European Journal of Enterprise Technologies 1, no. 9(115) (2022): 35–44. http://dx.doi.org/10.15587/1729-4061.2022.251637.

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We developed a method of training artificial neural networks for intelligent decision support systems. A distinctive feature of the proposed method consists in training not only the synaptic weights of an artificial neural network, but also the type and parameters of the membership function. In case of impossibility to ensure a given quality of functioning of artificial neural networks by training the parameters of an artificial neural network, the architecture of artificial neural networks is trained. The choice of architecture, type and parameters of the membership function is based on the c
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Qasim, Abbood Mahdi, Shyshatskyi Andrii, Symonenko Oleksandr, et al. "Development of a method for training artificial neural networks for intelligent decision support systems." Eastern-European Journal of Enterprise Technologies 1, no. 9 (115) (2022): 35–44. https://doi.org/10.15587/1729-4061.2022.251637.

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We developed a method of training artificial neural networks for intelligent decision support systems. A distinctive feature of the proposed method consists in training not only the synaptic weights of an artificial neural network, but also the type and parameters of the membership function. In case of impossibility to ensure a given quality of functioning of artificial neural networks by training the parameters of an artificial neural network, the architecture of artificial neural networks is trained. The choice of architecture, type and parameters of the membership function is based on the c
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Oleg, Sova, Turinskyi Oleksandr, Shyshatskyi Andrii, et al. "DEVELOPMENT OF AN ALGORITHM TO TRAIN ARTIFICIAL NEURAL NETWORKS FOR INTELLIGENT DECISION SUPPORT SYSTEMS." Eastern-European Journal of Enterprise Technologies 1, no. 9 (103) (2020): 46–55. https://doi.org/10.15587/1729-4061.2020.192711.

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The algorithm to train artificial neural networks for intelligent decision support systems has been constructed. A distinctive feature of the proposed algorithm is that it conducts training not only for synaptic weights of an artificial neural network, but also for the type and parameters of membership function. In case of inability to ensure the assigned quality of functioning of artificial neural networks due to training of parameters of artificial neural network, the architecture of artificial neural networks is trained. The choice of the architecture, type and parameters of membership func
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Oleg, Sova, Shyshatskyi Andrii, Zhuravskyi Yurii, et al. "DEVELOPMENT OF A METHODOLOGY FOR TRAINING ARTIFICIAL NEURAL NETWORKS FOR INTELLIGENT DECISION SUPPORT SYSTEMS." Eastern-European Journal of Enterprise Technologies 2, no. 4 (104) (2020): 6–14. https://doi.org/10.15587/1729-4061.2020.199469.

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The method of training artificial neural networks for intelligent decision support systems is developed. A distinctive feature of the proposed method is that it provides training not only of the synaptic weights of the artificial neural network, but also the type and parameters of the membership function. If it is impossible to provide the specified quality of functioning of artificial neural networks due to the learning of the parameters of the artificial neural network, the architecture of artificial neural networks is trained. The choice of architecture, type and parameters of the membershi
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KONOVALOV, S. "FEATURES OF DIAGNOSTIC ARTIFICIAL NEURAL NETWORKS FOR HYBRID EXPERT SYSTEMS." Digital Technologies 26 (2019): 36–46. http://dx.doi.org/10.33243/2313-7010-26-36-46.

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In the proposed article, various methods of constructing an artificial neural network as one of the components of a hybrid expert system for diagnosis were investigated. A review of foreign literature in recent years was conducted, where hybrid expert systems were considered as an integral part of complex technical systems in the field of security. The advantages and disadvantages of artificial neural networks are listed, and the main problems in creating hybrid expert systems for diagnostics are indicated, proving the relevance of further development of artificial neural networks for hybrid e
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Belkin, M. E., and K. V. Shabelnik. "Introduction to Photonic Neural Networks." Nano- i Mikrosistemnaya Tehnika 25, no. 6 (2023): 299–305. http://dx.doi.org/10.17587/nmst.25.299-305.

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The article briefly discusses: the development of photonic neural networks of artificial intelligence, the principles of construction and stages of development of photonic neural networks. The types of training of artificial intelligence neural networks, the advantages of such networks, as well as the scope of their application for creating more efficient artificial intelligence systems based on the photonic approach are described.
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Moskvin, G. "Back Propagation and Transformation Methods in Artificial Intelligence Systems." Environment. Technology. Resources. Proceedings of the International Scientific and Practical Conference 1 (June 26, 2006): 367. http://dx.doi.org/10.17770/etr2003vol1.2028.

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Detailed description of methods of back propagation and back transformation also distributions for training of neural networks is given. A comparative estimation of a priority of methods of back transformation and back propagation for the decision of tasks of synthesis and training of neural networks, also for intelligent automatic measuring and AI systems for the first time is carried out.
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ZAKARYAN, Arman. "Application of Artificial Intelligence (Neural Networks) in Education." Main Issues Of Pedagogy And Psychology 19, no. 1 (2021): 78–87. http://dx.doi.org/10.24234/miopap.v19i1.395.

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This article focuses on the use of artificial intelligence in the educational environment. We consider the latest technology, which already plays a huge role for both teachers and students. Currently, there are many systems for the development of training systems, among which artificial neural networks occupy a substantial place. The article presents an example of the use of artificial neural networks, which can play a significant role in developing educational systems.
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Dissertations / Theses on the topic "Training systems in artificial neural networks"

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Melcherson, Tim. "Image Augmentation to Create Lower Quality Images for Training a YOLOv4 Object Detection Model." Thesis, Uppsala universitet, Signaler och system, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-429146.

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Research in the Arctic is of ever growing importance, and modern technology is used in news ways to map and understand this very complex region and how it is effected by climate change. Here, animals and vegetation are tightly coupled with their environment in a fragile ecosystem, and when the environment undergo rapid changes it risks damaging these ecosystems severely.  Understanding what kind of data that has potential to be used in artificial intelligence, can be of importance as many research stations have data archives from decades of work in the Arctic. In this thesis, a YOLOv4 object d
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Manesco, Luis Fernando. "Modelagem de um processo fermentativo por rede Perceptron multicamadas com atraso de tempo." Universidade de São Paulo, 1996. http://www.teses.usp.br/teses/disponiveis/18/18133/tde-22012018-103016/.

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A utilização de Redes Neurais Artificias para fins de identificação e controle de sistemas dinâmicos têm recebido atenção especial de muitos pesquisadores, principalmente no que se refere a sistemas não lineares. Neste trabalho é apresentado um estudo sobre a utilização de um tipo em particular de Rede Neural Artificial, uma Perceptron Multicamadas com Atraso de Tempo, na estimação de estados da etapa fermentativa do processo de Reichstein para produção de vitamina C. A aplicação de Redes Neurais Artificiais a este processo pode ser justificada pela existência de problemas associados à esta et
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De, las Casas Zolezzi Humberto Jose. "Model-Free Optimization of Trajectory and Impedance Parameters on Exercise Robots with Applications to Human Performance and Rehabilitation." Cleveland State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=csu1625490714196286.

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Lundström, Christoffer, and Camilla Heiding. "Predictive Autoscaling of Systems using Artificial Neural Networks." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-104714.

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Autoscalers handle the scaling of instances in a system automatically based on specified thresholds such as CPU utilization. Reactive autoscalers do not take the delay of initiating a new instance into account, which may lead to overutilization. By applying machine learning methodology to predict future loads and the desired number of instances, it is possible to preemptively initiate scaling such that new instances are available before demand occurs. Leveraging efficient scaling policies keeps the costs and energy consumption low while ensuring the availability of the system. In this thesis,
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Berry, Ian Michael. "Data classification using unsupervised artificial neural networks." Thesis, University of Sussex, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390079.

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Shi, Arthur Q. X. "Structural Damage Assessment Using Artificial Neural Networks and Artificial Immune Systems." DigitalCommons@CalPoly, 2015. https://digitalcommons.calpoly.edu/theses/1499.

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Structural health monitoring (SHM) systems have been technologically advancing over the past few years. Improvements in fabrication and microelectronics allow the development of highly sophisticated sensor arrays, capable of detecting and transmitting an unprecedented amount of data. As the complexity of the hardware increases, research has been performed in developing the means to best utilize and effectively process the data. Algorithms from other computational fields are being introduced for the first time into SHM systems. Among them, the artificial neural network (ANN) and artificial immu
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Kogel, Wendy E. "Faster Training of Neural Networks for Recommender Systems." Digital WPI, 2002. https://digitalcommons.wpi.edu/etd-theses/607.

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In this project we investigate the use of artificial neural networks(ANNs) as the core prediction function of a recommender system. In the past, research concerned with recommender systems that use ANNs have mainly concentrated on using collaborative-based information. We look at the effects of adding content-based information and how altering the topology of the network itself affects the accuracy of the recommendations generated. In particular, we investigate a mixture of experts topology. We create two expert clusters in the hidden layer of the ANN, one for content-based data and another fo
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Rodríguez, Villegas Antoni. "Polyp segmentation using artificial neural networks." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-98001.

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Colorectal cancer is the second cause of cancer death in the world. Aiming to early detect and prevent this type of cancer, clinicians perform screenings through the colon searching for polyps (colorectal cancer precursor lesions).If found, these lesions are susceptible of being removed in order to further ana-lyze their malignancy degree. Automatic polyp segmentation is of primary impor-tance when it comes to computer-aided medical diagnosis using images obtained in colonoscopy screenings. These results allow for more precise medical diagnosis which can lead to earlier detection.This project
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Park, Dong Chul. "Identification of stationary/nonstationary systems using artificial neural networks /." Thesis, Connect to this title online; UW restricted, 1990. http://hdl.handle.net/1773/5822.

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Hsu, Kuo-Lin, Hoshin Vijai Gupta, and Soroosh Sorooshian. "A SUPERIOR TRAINING STRATEGY FOR THREE-LAYER FEEDFORWARD ARTIFICIAL NEURAL NETWORKS." Department of Hydrology and Water Resources, University of Arizona (Tucson, AZ), 1996. http://hdl.handle.net/10150/614171.

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A new algorithm is proposed for the identification of three-layer feedforward artificial neural networks. The algorithm, entitled LLSSIM, partitions the weight space into two major groups: the input- hidden and hidden -output weights. The input- hidden weights are trained using a multi -start SIMPLEX algorithm and the hidden -output weights are identified using a conditional linear- least- square estimation approach. Architectural design is accomplished by progressive addition of nodes to the hidden layer. The LLSSIM approach provides globally superior weight estimates with fewer functio
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Books on the topic "Training systems in artificial neural networks"

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Guan, Biing T. Modeling training site vegetation coverage probability with a random optimization procedure: An artificial neural network approach. US Army Corps of Engineers, Construction Engineering Research Laboratories, 1998.

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United States. National Aeronautics and Space Administration., ed. Object-oriented control system design using on-line training of artificial neural networks: Final report, grant no. NAG3-1661, December 01, 1996 - April 30, 1997 ... Howard University/NASA Lewis cooperative research studies. Howard University, College of Engineering, Architecture and Computer Sciences, Electrical Engineering Dept., 1997.

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United States. National Aeronautics and Space Administration., ed. Object-oriented control system design using on-line training of artificial neural networks: Final report, grant no. NAG3-1661, December 01, 1996 - April 30, 1997 ... Howard University/NASA Lewis cooperative research studies. Howard University, College of Engineering, Architecture and Computer Sciences, Electrical Engineering Dept., 1997.

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United States. National Aeronautics and Space Administration., ed. Object-oriented control system design using on-line training of artificial neural networks: Final report, grant no. NAG3-1661, December 01, 1996 - April 30, 1997 ... Howard University/NASA Lewis cooperative research studies. Howard University, College of Engineering, Architecture and Computer Sciences, Electrical Engineering Dept., 1997.

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United States. National Aeronautics and Space Administration., ed. Object-oriented control system design using on-line training of artificial neural networks: Final report, grant no. NAG3-1661, December 01, 1996 - April 30, 1997 ... Howard University/NASA Lewis cooperative research studies. Howard University, College of Engineering, Architecture and Computer Sciences, Electrical Engineering Dept., 1997.

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Zurada, Jacek M. Introduction to artificial neural systems. West, 1992.

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Stefan, Wermter, and Sun Ron 1960-, eds. Hybrid neural systems. Springer, 2000.

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Anastassiou, George A. Intelligent Systems: Approximation by Artificial Neural Networks. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21431-8.

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Anastassiou, George A. Intelligent Systems: Approximation by Artificial Neural Networks. Springer Berlin Heidelberg, 2011.

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Elmasry, Mohamed I. VLSI Artificial Neural Networks Engineering. Springer US, 1994.

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Book chapters on the topic "Training systems in artificial neural networks"

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Czarnowski, Ireneusz, and Piotr Jedrzejowicz. "An Approach to Artificial Neural Network Training." In Research and Development in Intelligent Systems XIX. Springer London, 2003. http://dx.doi.org/10.1007/978-1-4471-0651-7_11.

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Ly, Tu Nga, Rafał Kern, Khanindra Pathak, Krzysztof Wołk, and Erik Dawid Burnell. "Contextual Soft Dropout Method in Training of Artificial Neural Networks." In Intelligent Information and Database Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73280-6_55.

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Buscariolli, Luiza, Ricardo Caneloi dos Santos, and Ahda P. Grilo Pavani. "Methodology for Training Artificial Neural Networks for Islanding Detection of Photovoltaic Distributed Generators." In Lecture Notes in Networks and Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16075-2_30.

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Rubini, Luca, Rossella Cancelliere, Patrick Gallinari, Andrea Grosso, and Antonino Raiti. "Computational Experience with Pseudoinversion-Based Training of Neural Networks Using Random Projection Matrices." In Artificial Intelligence: Methodology, Systems, and Applications. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10554-3_24.

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Abusnaina, Ahmed A., Rosni Abdullah, and Ali Kattan. "Enhanced MWO Training Algorithm to Improve Classification Accuracy of Artificial Neural Networks." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07692-8_18.

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Danelian, Vadim, Andrei Kliuev, Vladimir Makarov, Mikhail Pesin, and Valerii Stolbov. "Data Augmentation in Artificial Neural Network Training Using Regression Models." In Information Systems Engineering and Management. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-86547-3_15.

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Trafalis, Theodore B., Tarek A. Tutunji, and Nicolas P. Couëllan. "Interior Point Methods for Supervised Training of Artificial Neural Networks with Bounded Weights." In Lecture Notes in Economics and Mathematical Systems. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-59179-2_22.

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Tsurikov, Alexander N., and Alexander N. Guda. "Practical Application of the Original Method for Artificial Neural Network’s Training." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68321-8_9.

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Çetinkaya, Ali. "Artificial Neural Network Approach for Predicting Student Achievement in Scratch Training." In Studies in Systems, Decision and Control. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-51997-0_15.

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Ahmad Sharawardi, Nur Shidah, Yun-Huoy Choo, Shin-Horng Chong, and Nur Ikhwan Mohamad. "Isotonic Muscle Fatigue Prediction for Sport Training Using Artificial Neural Network Modelling." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60618-7_57.

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Conference papers on the topic "Training systems in artificial neural networks"

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Silverman, D. C., and E. M. Rosen. "Corrosion Prediction from Polarization Scans Using an Artificial Neural Network Integrated with an Expert System." In CORROSION 1992. NACE International, 1992. https://doi.org/10.5006/c1992-92264.

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Abstract Artificial neural networks are forms of artificial intelligence which learn correlative patterns between input and output information without a specific model. They then use the learned relationships to make predictions. An artificial neural network was constructed to recognize certain relationships in potentiodynamic polarization scans to predict if crevice corrosion, pitting, and general corrosion are possible concerns. The network so constructed was shown to be able to make appropriate predictions using scans not included in the original training. The resulting network was incorpor
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Chen, Yuzhong, and Yue Zhong. "Digital course scheduling system for data-driven industrial worker training based on 5G enabling environment." In 2024 International Conference on Artificial Intelligence, Deep Learning and Neural Networks (AIDLNN). IEEE, 2024. https://doi.org/10.1109/aidlnn65358.2024.00033.

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Wang, Hua. "Intelligent Prediction and Training Optimization of Sports using Enhanced Whale Optimized Artificial Neural Network." In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS). IEEE, 2024. http://dx.doi.org/10.1109/iacis61494.2024.10721754.

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K�ksal, Ece S., Erdal Aydin, and Metin T�rkay. "An Efficient Convex Training Algorithm for Artificial Neural Networks by Utilizing Piecewise Linear Approximations and Semi-Continuous Formulations." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.125995.

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Artificial neural networks are widely used as data-driven models for capturing complex, nonlinear systems. However, suboptimal training remains a significant challenge due to the nonlinearity of activation functions and the reliance on local solvers, which makes achieving global solutions difficult. One solution involves reformulating activation functions as piecewise linear approximations to convexify the problem, though this approach often requires substantial CPU time. This study demonstrates that a tailored branch-and-bound algorithm can effectively address these challenges by efficiently
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Santana, Vinicius V., Carine M. Rebello, Erbet A. Costa, et al. "Recurrent Deep Learning Models for Multi-step Ahead Prediction: Comparison and Evaluation for Real Electrical Submersible Pump (ESP) System." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.107762.

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Predicting processes� future behavior based on past data is vital for automatic control and dynamic optimization in engineering. Recent advances in deep learning, particularly Artificial Neural Networks, have improved predictions in various engineering fields. Recurrent Neural Networks (RNNs) are well-suited for time series data, as they naturally evolve through dynamic systems with recurrent updates. Despite their high predictive power, RNNs may underperform if their training ignores the model's future application. In Model Predictive Control, for example, the model evolves over time using on
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Shamsoddini, Rahim. "Selection of the Training Function of Artificial Neural Network to Predict the Behavior of the Vortex Tube." In 2024 19th Iranian Conference on Intelligent Systems (ICIS). IEEE, 2024. https://doi.org/10.1109/icis64839.2024.10887469.

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Neto, Mario Tasso Ribeiro Serra, Marco Antonio Florenzano Mollinetti, and Rodrigo Lisboa Pereira. "Evolutionary artificial bee colony for neural networks training." In 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). IEEE, 2017. http://dx.doi.org/10.1109/fskd.2017.8393309.

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Kattan, Ali, Rosni Abdullah, and Rosalina Abdul Salam. "Harmony Search Based Supervised Training of Artificial Neural Networks." In 2010 International Conference on Intelligent Systems, Modelling and Simulation (ISMS). IEEE, 2010. http://dx.doi.org/10.1109/isms.2010.31.

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Lindsay, James, and Sidney Givigi. "Continuous Action Learning Automata Optimizer for training Artificial Neural Networks." In 2023 IEEE International Systems Conference (SysCon). IEEE, 2023. http://dx.doi.org/10.1109/syscon53073.2023.10131086.

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Юсупбеков, Нодирбек, Шухрат Гулямов, Улугбек Мухамедханов, and Барно Ешматова. "GAS DETERMINATION USING ARTIFICIAL NEURAL NETWORKS." In Status and development trends of standardization and technical regulation in the world. Tashkent state technical university, 2022. http://dx.doi.org/10.51346/tstu-conf.22.1-77-0077.

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The recognition of various gases has been performed - part of the tasks solved by these systems. For this purpose, various types of classifiers are used, among which artificial neural networks are widely used. They are used to classify various types of smokes, fires, aromas, gases, etc. These systems use metal oxide gas sensors, which are widely used due to their high sensitivity, low cost and short duration of the chemical reaction. When training the network, a three-dimensional approximation of the sensor characteristics is carried out.
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Reports on the topic "Training systems in artificial neural networks"

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Yaroshchuk, Svitlana O., Nonna N. Shapovalova, Andrii M. Striuk, Olena H. Rybalchenko, Iryna O. Dotsenko, and Svitlana V. Bilashenko. Credit scoring model for microfinance organizations. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/3683.

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The purpose of the work is the development and application of models for scoring assessment of microfinance institution borrowers. This model allows to increase the efficiency of work in the field of credit. The object of research is lending. The subject of the study is a direct scoring model for improving the quality of lending using machine learning methods. The objective of the study: to determine the criteria for choosing a solvent borrower, to develop a model for an early assessment, to create software based on neural networks to determine the probability of a loan default risk. Used rese
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Pasupuleti, Murali Krishna. Quantum-Enhanced Machine Learning: Harnessing Quantum Computing for Next-Generation AI Systems. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv125.

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Abstract Quantum-enhanced machine learning (QML) represents a paradigm shift in artificial intelligence by integrating quantum computing principles to solve complex computational problems more efficiently than classical methods. By leveraging quantum superposition, entanglement, and parallelism, QML has the potential to accelerate deep learning training, optimize combinatorial problems, and enhance feature selection in high-dimensional spaces. This research explores foundational quantum computing concepts relevant to AI, including quantum circuits, variational quantum algorithms, and quantum k
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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detecti
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Mu, Hong H., Y. P. Kakad, and B. G. Sherlock. Application of Artificial Neural Networks in the Design of Control Systems. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada384438.

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Arhin, Stephen, Babin Manandhar, Hamdiat Baba Adam, and Adam Gatiba. Predicting Bus Travel Times in Washington, DC Using Artificial Neural Networks (ANNs). Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.1943.

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Washington, DC is ranked second among cities in terms of highest public transit commuters in the United States, with approximately 9% of the working population using the Washington Metropolitan Area Transit Authority (WMATA) Metrobuses to commute. Deducing accurate travel times of these metrobuses is an important task for transit authorities to provide reliable service to its patrons. This study, using Artificial Neural Networks (ANN), developed prediction models for transit buses to assist decision-makers to improve service quality and patronage. For this study, we used six months of Automati
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Nikiforov, Vladimir. Laser technology and integrated technical systems in devices and instruments for ophthalmology using elements of artificial intelligence associated with artificial neural networks. Intellectual Archive, 2019. http://dx.doi.org/10.32370/iaj.2123.

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Nikiforov, Vladimir. Laser equipment and complex technical systems in devices and tools for ophthalmology, that use elements of artificial intelligence interlinked with artificial neural networks. Intellectual Archive, 2019. http://dx.doi.org/10.32370/iaj.2172.

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Semerikov, Serhiy O., Illia O. Teplytskyi, Yuliia V. Yechkalo, and Arnold E. Kiv. Computer Simulation of Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot. [б. в.], 2018. http://dx.doi.org/10.31812/123456789/2648.

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The article substantiates the necessity to develop training methods of computer simulation of neural networks in the spreadsheet environment. The systematic review of their application to simulating artificial neural networks is performed. The authors distinguish basic approaches to solving the problem of network computer simulation training in the spreadsheet environment, joint application of spreadsheets and tools of neural network simulation, application of third-party add-ins to spreadsheets, development of macros using the embedded languages of spreadsheets; use of standard spreadsheet ad
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Nikiforov, Vladimir. The use of composite materials in smart medical equipment, including with innovative laser systems, controlled and controlled complexes with elements of artificial intelligence and artificial neural networks. Intellectual Archive, 2019. http://dx.doi.org/10.32370/iaj.2133.

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Semerikov, Serhiy, Illia Teplytskyi, Yuliia Yechkalo, Oksana Markova, Vladimir Soloviev, and Arnold Kiv. Computer Simulation of Neural Networks Using Spreadsheets: Dr. Anderson, Welcome Back. [б. в.], 2019. http://dx.doi.org/10.31812/123456789/3178.

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The authors of the given article continue the series presented by the 2018 paper “Computer Simulation of Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot”. This time, they consider mathematical informatics as the basis of higher engineering education fundamentalization. Mathematical informatics deals with smart simulation, information security, long-term data storage and big data management, artificial intelligence systems, etc. The authors suggest studying basic principles of mathematical informatics by applying cloud-oriented means of various levels including those traditio
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