Thematische Bibliographien / Evolutionary development of neural network

Inhaltsverzeichnis

  1. Zeitschriftenartikel
  2. Dissertationen
  3. Bücher
  4. Buchteile
  5. Konferenzberichte
  6. Berichte der Organisationen

Auswahl der wissenschaftlichen Literatur zum Thema „Evolutionary development of neural network“

Autor: Grafiati
Veröffentlicht am 28. Juni 2021
Zuletzt aktualisiert am 29. Juli 2025

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Zeitschriftenartikel zum Thema "Evolutionary development of neural network"

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Leoshchenko, S. D., A. O. Oliinyk, S. A. Subbotin, Ye O. Gofman, and M. B. Ilyashenko. "EVOLUTIONARY METHOD FOR SYNTHESIS SPIKING NEURAL NETWORKS USING THE NEUROPATTHERN MECHANISM." Radio Electronics, Computer Science, Control, no. 3 (October 20, 2022): 77. http://dx.doi.org/10.15588/1607-3274-2022-3-8.

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Context. The problem of synthesizing pulsed neural networks based on an evolutionary approach to the synthesis of artificial neural networks using a neuropathic mechanism for constructing diagnostic models with a high level of accuracy is considered. The object of research is the process of synthesis of pulsed neural networks using an evolutionary approach and a neuropathic mechanism.
 Objective of the work is to develop a method for synthesizing pulsed neural networks based on an evolutionary approach using a neuropathic mechanism to build diagnostic models with a high level of accuracy
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Al-Khowarizmi, Al-Khowarizmi. "Model Classification Of Nominal Value And The Original Of IDR Money By Applying Evolutionary Neural Network." JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING 3, no. 2 (2020): 258–65. http://dx.doi.org/10.31289/jite.v3i2.3284.

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Indonesian Rupiah (IDR) banknotes have unique characteristics that distinguish them from one another, both in the form of numbers, zeros and background images. This pattern of each type of banknote will be modeled in order to test the nominal value and authenticity of IDR, so as to be able to distinguish not only IDR banknotes but also other denominations. Evolutionary Neural Network is the development of the concept of evolution to get a neural network (NN) using genetic algorithms (GA). In this paper the application of evolutionary neural networks with less input is able to have a better suc
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YASHIN, Sergei N., Egor V. KOSHELEV, and Dar'ya A. BYKOVA. "Evolutionary neural network modeling of the impact of digital technologies on the economic development of regions." Finance and Credit 30, no. 5 (2024): 1036–60. http://dx.doi.org/10.24891/fc.30.5.1036.

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Subject. This article focuses on the issues of evolutionary neural network modeling of the impact of digital technologies on economic development. Objectives. The article aims to study evolutionary neural network modeling of the impact of digital technologies on the economic development of the regions of Russia. The article also aims to identify regions that are leaders where the impact is positive and significant, as well as regions with prospects for such positive influence. Results. The article presents the author-developed methodology for evolutionary neural network modeling of the impact
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Kan, Xinglong, and Lin Li. "Comprehensive Evaluation of Tourism Resources Based on Multispecies Evolutionary Genetic Algorithm-Enabled Neural Networks." Computational Intelligence and Neuroscience 2021 (December 14, 2021): 1–11. http://dx.doi.org/10.1155/2021/1081814.

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With the development of neural network technology and the rapid growth of China’s tourism economic income at this stage, the research on the comprehensive evaluation of tourism resources has gradually emerged. Based on this, this paper studies the neural network comprehensive evaluation model based on multispecies evolutionary genetic algorithm and designs the neural network analysis system of influencing factors of tourism resources based on multispecies evolutionary genetic algorithm. The collection and acquisition of data information are realized from the aspects of resource income status,
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Retaj, Matroud Jasim, and Salman Atia Tayseer. "An evolutionary-convolutional neural network for fake image detection." An evolutionary-convolutional neural network for fake image detection 29, no. 3 (2023): 1657–67. https://doi.org/10.11591/ijeecs.v29.i3.pp1657-1667.

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The fast development in deep learning techniques, besides the wide spread of social networks, facilitated fabricating and distributing images and videos without prior knowledge. This paper developed an evolutionary learning algorithm to automatically design a convolutional neural network (CNN) architecture for deepfake detection. Genetic algorithm (GA) based on residual network (ResNet) and densely connected convolutional network (DenseNet) as building block units for feature extraction versus multilayer perceptron (MLP), random forest (RF) and support vector machine (SVM) as classifiers gener
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QIAO, Zenglin, Xinchao ZHAO, and Lingyu WU. "A Review of Evolutionary Deep Neural Architecture Search with Performance Predictor." Bulletin of Chinese Applied Mathematics 1, no. 1 (2023): 1–9. http://dx.doi.org/10.48014/bcam.20230822002.

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Automated design of deep neural networks using performance predictor has become a hot topic in current research. Neural architecture search (NAS) methods can be used to enable automatic design of neural network structures by defining different search spaces, search strategies, or optimization strategies. Evolutionary computation by many researchers as the search strategy for NAS, which is called evolutionary NAS (ENAS) . However, ENAS is time-consuming in evaluating the performance of network structures, which hinders the development of ENAS. Therefore, predicting network architecture performa
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Jasim, Retaj Matroud, and Tayseer Salman Atia. "An evolutionary- convolutional neural network for fake image detection." Indonesian Journal of Electrical Engineering and Computer Science 29, no. 3 (2023): 1657. http://dx.doi.org/10.11591/ijeecs.v29.i3.pp1657-1667.

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<p><span lang="EN-US">The fast development in deep learning techniques, besides the wide spread of social networks, facilitated fabricating and distributing images and videos without prior knowledge. This paper developed an evolutionary learning algorithm to automatically design a convolutional neural network (CNN) architecture for deepfake detection. Genetic algorithm (GA) based on residual network (ResNet) and densely connected convolutional network (DenseNet) as building block units for feature extraction versus multilayer perceptron (MLP), random forest (RF) and support vector
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Li, Xiao Guang. "Research on the Development and Applications of Artificial Neural Networks." Applied Mechanics and Materials 556-562 (May 2014): 6011–14. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.6011.

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Intelligent control is a class of control techniques that use various AI computing approaches like neural networks, Bayesian probability, fuzzy logic, machine learning, evolutionary computation and genetic algorithms. In computer science and related fields, artificial neural networks are computational models inspired by animals’ central nervous systems (in particular the brain) that are capable of machine learning and pattern recognition. They are usually presented as systems of interconnected “neurons” that can compute values from inputs by feeding information through the network. Like other
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Xue, Yu, Pengcheng Jiang, Ferrante Neri, and Jiayu Liang. "A Multi-Objective Evolutionary Approach Based on Graph-in-Graph for Neural Architecture Search of Convolutional Neural Networks." International Journal of Neural Systems 31, no. 09 (2021): 2150035. http://dx.doi.org/10.1142/s0129065721500350.

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With the development of deep learning, the design of an appropriate network structure becomes fundamental. In recent years, the successful practice of Neural Architecture Search (NAS) has indicated that an automated design of the network structure can efficiently replace the design performed by human experts. Most NAS algorithms make the assumption that the overall structure of the network is linear and focus solely on accuracy to assess the performance of candidate networks. This paper introduces a novel NAS algorithm based on a multi-objective modeling of the network design problem to design
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Zhao, Hongfei, Zhiguo Shi, Zhefeng Gong, and Shibo He. "Modeling the Evolution of Biological Neural Networks Based on Caenorhabditis elegans Connectomes across Development." Entropy 25, no. 1 (2022): 51. http://dx.doi.org/10.3390/e25010051.

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Knowledge of the structural properties of biological neural networks can help in understanding how particular responses and actions are generated. Recently, Witvliet et al. published the connectomes of eight isogenic Caenorhabditis elegans hermaphrodites at different postembryonic ages, from birth to adulthood. We analyzed the basic structural properties of these biological neural networks. From birth to adulthood, the asymmetry between in-degrees and out-degrees over the C. elegans neuronal network increased with age, in addition to an increase in the number of nodes and edges. The degree dis
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Dissertationen zum Thema "Evolutionary development of neural network"

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Bush, Brian O. "Development of a fuzzy system design strategy using evolutionary computation." Ohio : Ohio University, 1996. http://www.ohiolink.edu/etd/view.cgi?ohiou1178656308.

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Townsend, Joseph Paul. "Artificial development of neural-symbolic networks." Thesis, University of Exeter, 2014. http://hdl.handle.net/10871/15162.

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Artificial neural networks (ANNs) and logic programs have both been suggested as means of modelling human cognition. While ANNs are adaptable and relatively noise resistant, the information they represent is distributed across various neurons and is therefore difficult to interpret. On the contrary, symbolic systems such as logic programs are interpretable but less adaptable. Human cognition is performed in a network of biological neurons and yet is capable of representing symbols, and therefore an ideal model would combine the strengths of the two approaches. This is the goal of Neural-Symbol
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Hytychová, Tereza. "Evoluční návrh neuronových sítí využívající generativní kódování." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445478.

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The aim of this work is to design and implement a method for the evolutionary design of neural networks with generative encoding. The proposed method is based on J. F. Miller's approach and uses a brain model that is gradually developed and which allows extraction of traditional neural networks. The development of the brain is controlled by programs created using cartesian genetic programming. The project was implemented in Python with the use of Numpy library. Experiments have shown that the proposed method is able to construct neural networks that achieve over 90 % accuracy on smaller datase
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Kadiyala, Akhil. "Development and Evaluation of an Integrated Approach to Study In-Bus Exposure Using Data Mining and Artificial Intelligence Methods." University of Toledo / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1341257080.

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Adams, Bryan (Bryan Paul) 1977. "Evolutionary, developmental neural networks for robust robotic control." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/37900.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.<br>Includes bibliographical references (p. 136-143).<br>The use of artificial evolution to synthesize controllers for physical robots is still in its infancy. Most applications are on very simple robots in artificial environments, and even these examples struggle to span the "reality gap," a name given to the difference between the performance of a simulated robot and the performance of a.real robot using the same evolved controller. This dissertation describes three methods for
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Tsui, Kwok Ching. "Neural network design using evolutionary computing." Thesis, King's College London (University of London), 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.299918.

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Hayward, Serge. "Financial forecasting and modelling with an evolutionary artificial neural network." Thesis, Queen Mary, University of London, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.439394.

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Hlynka, Markian D. "A framework for an automated neural network designer using evolutionary algorithms." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0014/MQ41716.pdf.

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Jagadeesan, Ananda Prasanna. "Real time evolutionary algorithms in robotic neural control systems." Thesis, Robert Gordon University, 2006. http://hdl.handle.net/10059/436.

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This thesis describes the use of a Real-Time Evolutionary Algorithm (RTEA) to optimise an Artificial Neural Network (ANN) on-line (in this context “on-line” means while it is in use). Traditionally, Evolutionary Algorithms (Genetic Algorithms, Evolutionary Strategies and Evolutionary Programming) have been used to train networks before use - that is “off-line,” as have other learning systems like Back-Propagation and Simulated Annealing. However, this means that the network cannot react to new situations (which were not in its original training set). The system outlined here uses a Simulated L
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Jakobsson, Henrik. "Inversion of an Artificial Neural Network Mapping by Evolutionary Algorithms with Sharing." Thesis, University of Skövde, Department of Computer Science, 1998. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-165.

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<p>Inversion of the artificial neural network mapping is a relatively unexplored field of science. By inversion we mean that a search is conducted to find what input patterns that corresponds to a specific output pattern according to the analysed network. In this report, an evolutionary algorithm is proposed to conduct the search for input patterns. The hypothesis is that the inversion with the evolutionary search-method will result in multiple, separate and equivalent input patterns and not get stuck in local optima which possibly would cause the inversion to result in erroneous answer. Besid
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Bücher zum Thema "Evolutionary development of neural network"

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Lou, Padgett Mary, Lindblad Thomas, Society for Computer Simulation, and United States. National Aeronautics and Space Administration., eds. Sixth, Seventh, and Eighth Workshops on Virtual Intelligence: Academic/Industrial/NASA/Defense: Technical interchange and tutorials : International Conferences on Virtual Intelligence, Fuzzy Systems, Evolutionary Computing, and Virtual Reality 1996. SPIE, 1996.

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C, Jain L., and Johnson R. P, eds. Automatic generation of neural network architecture using evolutionary computation. World Scientific, 1997.

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Jorgensen, Charles C. Development of a sensor coordinated kinematic model for neural network controller training. Research Institute for Advanced Computer Science, NASA Ames Research Center, 1990.

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International Symposium on Computational Intelligence and Design (3rd 2010 Hangzhou, Zhejiang, China). Proceedings: 2010 International Symposium on Computational Intelligence and Design : ICSID 2010 : 29-31 October 2010, Hangzhou, Zhejiang, China. IEEE Computer Society, 2010.

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International, Symposium on Computational Intelligence and Design (1st 2008 Wuhan China). Proceedings of the 2008 International Symposium on Computational Intelligence and Design: October 17-18, 2008, Wuhan, China. IEEE Computer Society, 2008.

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International Symposium on Computational Intelligence and Design (2nd 2009 Changsha, China). Proceedings: 2009 International Symposium on Computational Intelligence and Design : Changsha, China, 12-14 December 2009. IEEE Computer Society, 2008.

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International Conference on Innovative Computing, Information and Control (1st 2006 Beijing, China). ICICIC 2006: First International Conference on Innovative Computing, Information and Control : 30 August-1 September, 2006, Beijing, China. Edited by Pan Jeng-Shyang, Shi Peng 1958-, Zhao Yao, and Institute of Electrical and Electronics Engineers. IEEE Computer Society, 2006.

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Bel'zeckiy, Anatoliy. Marketing: theory, methods and models of the market as a whole. INFRA-M Academic Publishing LLC., 2024. https://doi.org/10.12737/2134337.

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In the monograph, based on the innovative concept of organized market integrity from the standpoint of the methodology of holism (integrity), methods of measurement, spectral analysis and synthesis of a mixed heterogeneous market structure in interaction with the external environment are developed. Models of structure, functioning, kinetics of behavior, neural network forecasting and market development as a whole are presented. The practical issues of the implementation and assessment of the quality of market models as integrity are considered. The laws of market development are formulated. It
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(Editor), Mark J. Embrechts, Hugh F. Van Landingham (Editor), and Seppo J. Ovaska (Editor), eds. Smcia/01: Proceedings of the 2001 IEEE Mountain Workshop on Soft Computing in Industrial Applications, Donaldson-Brown Hotel, Virginia Tech Blacksburg, Va.June. Ieee, 2001.

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Sun, Yanan, Mengjie Zhang, and Gary Yen. Evolutionary Deep Neural Network Design: Fundamentals and Methods. Wiley & Sons, Incorporated, John, 2022.

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Buchteile zum Thema "Evolutionary development of neural network"

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Cho, Sung-Bae, and Katsunori Shimohara. "Grammatical Development of Evolutionary Modular Neural Networks." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48873-1_53.

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Shakya, S., M. Kern, G. Owusu, and C. M. Chin. "Dynamic Pricing with Neural Network Demand Models and Evolutionary Algorithms." In Research and Development in Intelligent Systems XXVII. Springer London, 2010. http://dx.doi.org/10.1007/978-0-85729-130-1_16.

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Manuputty, J., P. Sen, and D. Todd. "Development of an Iterative Neural Network and Genetic Algorithm Procedure for Shipyard Scheduling." In Evolutionary Design and Manufacture. Springer London, 2000. http://dx.doi.org/10.1007/978-1-4471-0519-0_27.

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Shailaja, M., and A. V. Sita Rama Raju. "Development of Back Propagation Neural Network (BPNN) Model to Predict Combustion Parameters of Diesel Engine." In Swarm, Evolutionary, and Memetic Computing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48959-9_7.

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Barrios, D., A. Carrascal, D. Manrique, and J. Rios. "ADANNET: Automatic Design of Artificial Neural Networks by Evolutionary Techniques." In Research and Development in Intelligent Systems XVIII. Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0119-2_6.

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Dong, Xueshi, Wenyong Dong, Yunfei Yi, Yajie Wang, and Xiaosong Xu. "The Recent Developments and Comparative Analysis of Neural Network and Evolutionary Algorithms for Solving Symbolic Regression." In Intelligent Computing Theories and Methodologies. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22180-9_70.

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Rocha, Miguel, Paulo Cortez, and José Neves. "Evolutionary Neural Network Learning." In Progress in Artificial Intelligence. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-24580-3_10.

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Witt, Nicolas, Mark Deutel, Jakob Schubert, Christopher Sobel, and Philipp Woller. "Energy-Efficient AI on the Edge." In Unlocking Artificial Intelligence. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-64832-8_19.

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AbstractThis chapter shows methods for the resource-optimized design of AI functionality for edge devices powered by microprocessors or microcontrollers. The goal is to identify Pareto-optimal solutions that satisfy both resource restrictions (energy and memory) and AI performance. To accelerate the design of energyefficient classical machine learning pipelines, an AutoML tool based on evolutionary algorithms is presented, which uses an energy prediction model from assembly instructions (prediction accuracy 3.1%) to integrate the energy demand into a multiobjective optimization approach. For t
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Khan, Gul Muhammad. "Evolutionary Computation." In Evolution of Artificial Neural Development. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67466-7_3.

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Mat Noor, R. A. "Recent Developments of Neural Networks in Biodiesel Applications." In Swarm, Evolutionary, and Memetic Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20294-5_30.

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Konferenzberichte zum Thema "Evolutionary development of neural network"

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Liu, Zhi, Wei Song, and Mingshuo Song. "Neural Network-Assisted Particle Swarm Dynamic Optimization." In 2024 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2024. http://dx.doi.org/10.1109/cec60901.2024.10612102.

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Hu, Hao, Tao Wang, Yanan Li, Zhao Zhang, and Xinggunag Peng. "Neural architecture search-based BP neural network for underwater glider motion parameter generation." In 2025 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2025. https://doi.org/10.1109/cec65147.2025.11042943.

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Szymanski, Julian, Jan Dobrosolski, Higinio Mora, and Karol Draszawka. "Neural network agents trained by declarative programming tutors." In 2024 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2024. http://dx.doi.org/10.1109/cec60901.2024.10611953.

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Leresche, Xavier, Alban Goupil, Valeriu Vrabie, and Loïc Kolodziejczak. "Adaptive L1 Regularization for Neural Network-Based Symbolic Regression." In 2025 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2025. https://doi.org/10.1109/cec65147.2025.11042914.

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Holliday, Andrew, and Gregory Dudek. "A Neural-Evolutionary Algorithm for Autonomous Transit Network Design." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10611313.

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Roy, Anthony M., Erik K. Antonsson, and Andrew A. Shapiro. "Genetic Evolution for the Development of Robust Artificial Neural Network Logic Gates." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-87448.

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Control tasks involving dramatic non-linearities, such as decision making, can be challenging for classical design methods. However, autonomous stochastic design methods have proved effective. In particular, Genetic Algorithms (GA) that create phenotypes by the application of genotypes comprising rules are robust and highly scalable. Such encodings are useful for complex applications such as artificial neural net design. This paper outlines an evolutionary algorithm that creates C++ programs which in turn create Artificial Neural Networks (ANNs) that can functionally perform as an exclusive-OR
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Miller, Julian F., and Dennis G. Wilson. "A developmental artificial neural network model for solving multiple problems." In GECCO '17: Genetic and Evolutionary Computation Conference. ACM, 2017. http://dx.doi.org/10.1145/3067695.3075976.

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Huan, Tran Thien, Cao Van Kien, and Ho Pham Huy Anh. "Adaptive Evolutionary Neural Network Gait Generation for Humanoid Robot Optimized with Modified Differential Evolution Algorithm." In 2018 4th International Conference on Green Technology and Sustainable Development (GTSD). IEEE, 2018. http://dx.doi.org/10.1109/gtsd.2018.8595586.

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Smolin, Vladimir Sergeevich. "Neural network algorithms as the cutting edge of self-organization processes." In Horizons of mathematical modeling and theory of self-organization. Keldysh Institute of Applied Mathematics, 2024. http://dx.doi.org/10.20948/k95-11.

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With the creation of strong AI, not only humanity, but the entire life evolution enters a new era. Life, as the most complex and most amazing self-organization process, is difficult to accurately describe, but attempts to identify the basic conditions for its development have been made for a long time ago. As it became clear about 70 years ago, the main condition for the life development is the knowledge accumulation in DNA, making it possible to reproduce life forms that have undergone evolutionary selection. Many other important conditions for the development of “living” self-organization we
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LA PAZ-MARÍN, MÓNICA DE, PILAR CAMPOY-MUÑOZ, and CÉSAR HERVÁS-MARTÍNEZ. "EVOLUTIONARY NEURAL NETWORK CLASSIFIERS FOR MONITORING RESEARCH, DEVELOPMENT AND INNOVATION PERFORMANCE IN EUROPEAN UNION MEMBER STATES." In Proceedings of the XVII SIGEF Congress. WORLD SCIENTIFIC, 2012. http://dx.doi.org/10.1142/9789814415774_0021.

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Berichte der Organisationen zum Thema "Evolutionary development of neural network"

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McDonnell, J. R., W. C. Page, and D. E. Waagen. Neural Network Construction Using Evolutionary Search. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada290862.

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Matteucci, Matteo. ELeaRNT: Evolutionary Learning of Rich Neural Network Topologies. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada456062.

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Patro, S., and W. J. Kolarik. Integrated evolutionary computation neural network quality controller for automated systems. Office of Scientific and Technical Information (OSTI), 1999. http://dx.doi.org/10.2172/350895.

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Leij, F. J., and M. T. Van Genuchten. Development of Pedotransfer Functions with Neural Network Models. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada394563.

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Fox-Rabinovitz, M. S., and V. M. Krasnopolsky. Development of Ensemble Neural Network Convection Parameterizations for Climate Models. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1039344.

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Perdigão, Rui A. P. Neuro-Quantum Cyber-Physical Intelligence (NQCPI). Synergistic Manifolds, 2024. http://dx.doi.org/10.46337/241024.

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Neuro-Quantum Cyber-Physical Intelligence (NQCPI) is hereby introduced, entailing a novel framework for nonlinear natural-based neural post-quantum information physics, along with novel advances in far-from-equilibrium thermodynamics and evolutionary cognition in post-quantum neurobiochemistry for next-generation information physical systems intelligence. NQCPI harnesses and operates with the higher-order nonlinear nature of previously elusive quantum behaviour, including in open chaotic dissipative systems in thermodynamically and magneto-electrodynamically disruptive conditions, such as in n
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Rajagopalan, A., G. Washington, G. Rizzoni, and Y. Guezennec. Development of Fuzzy Logic and Neural Network Control and Advanced Emissions Modeling for Parallel Hybrid Vehicles. Office of Scientific and Technical Information (OSTI), 2003. http://dx.doi.org/10.2172/15006009.

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Harouaka, Khadouja, Rachel Richardson, Evan Glasscock, et al. Development of a hybrid neural network and transfer learning model for optimized ICP-MS/MS operation. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/2202285.

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9

Krasnopolsky, V., M. Fox-Rabinovitz, and A. Belochitski. Development of neural network convection parameterizations for climate and NWP models using Cloud Resolving Model simulations. Office of Scientific and Technical Information (OSTI), 2011. http://dx.doi.org/10.2172/1039340.

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Valdez, Luis, and Alexander Heifetz. Preliminary Assessment of Qiskit Quantum Simulator Capabilities for Development of Quantum Hopfield Neural Network for Anomaly Detection Applications (Q4 Report). Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1832153.

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