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Статті в журналах з теми "Neural networks (NNs)"
Thakur, Amey. "Fundamentals of Neural Networks." International Journal for Research in Applied Science and Engineering Technology 9, no. VIII (August 15, 2021): 407–26. http://dx.doi.org/10.22214/ijraset.2021.37362.
Повний текст джерелаJwo, Dah-Jing, and Chien-Cheng Lai. "Neural Network-Based Geometry Classification for Navigation Satellite Selection." Journal of Navigation 56, no. 2 (May 2003): 291–304. http://dx.doi.org/10.1017/s0373463303002200.
Повний текст джерелаGuidotti, Dario. "Verification and Repair of Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (May 18, 2021): 15714–15. http://dx.doi.org/10.1609/aaai.v35i18.17854.
Повний текст джерелаIKEDA, TAKASHI, and MASAFUMI HAGIWARA. "CONTENT-BASED IMAGE RETRIEVAL SYSTEM USING NEURAL NETWORKS." International Journal of Neural Systems 10, no. 05 (October 2000): 417–24. http://dx.doi.org/10.1142/s0129065700000326.
Повний текст джерелаGhorbani, Behrooz, Song Mei, Theodor Misiakiewicz, and Andrea Montanari. "When do neural networks outperform kernel methods?*." Journal of Statistical Mechanics: Theory and Experiment 2021, no. 12 (December 1, 2021): 124009. http://dx.doi.org/10.1088/1742-5468/ac3a81.
Повний текст джерелаĐerek, Jurica, Marjan Sikora, Luka Kraljević, and Mladen Russo. "Using Neural Networks for Bicycle Route Planning." Applied Sciences 11, no. 21 (October 27, 2021): 10065. http://dx.doi.org/10.3390/app112110065.
Повний текст джерелаHu, Yibo, Yuzhe Ou, Xujiang Zhao, Jin-Hee Cho, and Feng Chen. "Multidimensional Uncertainty-Aware Evidential Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (May 18, 2021): 7815–22. http://dx.doi.org/10.1609/aaai.v35i9.16954.
Повний текст джерелаKrogh, Anders, and Søren Kamaric Riis. "Hidden Neural Networks." Neural Computation 11, no. 2 (February 1, 1999): 541–63. http://dx.doi.org/10.1162/089976699300016764.
Повний текст джерелаDeVore, Ronald, Boris Hanin, and Guergana Petrova. "Neural network approximation." Acta Numerica 30 (May 2021): 327–444. http://dx.doi.org/10.1017/s0962492921000052.
Повний текст джерелаWongsathan, Rati, and Pasit Pothong. "Heart Disease Classification Using Artificial Neural Networks." Applied Mechanics and Materials 781 (August 2015): 624–27. http://dx.doi.org/10.4028/www.scientific.net/amm.781.624.
Повний текст джерелаДисертації з теми "Neural networks (NNs)"
Fischer, Manfred M. "Neural networks. A class of flexible non-linear models for regression and classification." Elgar, 2015. http://epub.wu.ac.at/4763/1/NN%2DHandbook%2Dchapter_Fischer_20120809.pdf.
Повний текст джерелаZirpe, Milind A. "RAIN and NCS 5 benchmarks." abstract and full text PDF (free order & download UNR users only), 2007. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1447612.
Повний текст джерелаMattos, CÃsar Lincoln Cavalcante. "ComitÃs de Classificadores Baseados nas Redes SOM e Fuzzy ART com Sintonia de ParÃmetros e SeleÃÃo de Atributos via MetaheurÃsticas EvolucionÃrias." Universidade Federal do CearÃ, 2011. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=7034.
Повний текст джерелаO paradigma de classificaÃÃo baseada em comitÃs tem recebido considerÃvel atenÃÃo na literatura cientÃfica em anos recentes. Neste contexto, redes neurais supervisionadas tÃm sido a escolha mais comum para compor os classificadores base dos comitÃs. Esta dissertaÃÃo tem a intenÃÃo de projetar e avaliar comitÃs de classificadores obtidos atravÃs de modificaÃÃes impostas a algoritmos de aprendizado nÃo-supervisionado, tais como as redes Fuzzy ART e SOM, dando origem, respectivamente, Ãs arquiteturas ARTIE (ART in Ensembles) e MUSCLE (Multiple SOM Classifiers in Ensembles). A sintonia dos parÃmetros e a seleÃÃo dos atributos das redes neurais que compÃem as arquiteturas ARTIE e MUSCLE foram tratados por otimizaÃÃo metaheurÃstica, a partir da proposiÃÃo do algoritmo I-HPSO (Improved Hybrid Particles Swarm Optimization). As arquiteturas ARTIE e MUSCLE foram avaliadas e comparadas com comitÃs baseados nas redes Fuzzy ARTMAP, LVQ e ELM em 12 conjuntos de dados reais. Os resultados obtidos indicam que as arquiteturas propostas apresentam desempenhos superiores aos dos comitÃs baseados em redes neurais supervisionadas.
Bezerra, Alberto Guilherme de Oliveira. "Modelos de previsão de tarifa de água, aplicados a autarquias municipais e empresas privadas, nas regiões Sul e Sudeste do Brasil /." Ilha Solteira, 2019. http://hdl.handle.net/11449/183655.
Повний текст джерелаResumo: O objetivo do presente trabalho é avaliar modelos de previsão de tarifa de água, aplicados a autarquias municipais e empresas privadas, nas regiões Sul e Sudeste do Brasil. Utilizando a metodologia de cálculo e posterior comparação dos erros obtidos para as previsões, verificando também a aplicabilidade das tarifas previstas para cada sistema de abastecimento. Utilizou-se dois modelos de previsão, o primeiro, fundamentado em técnicas de regressão linear múltipla e o segundo, baseado na aplicação de redes neurais artificiais. Avaliando, dessa forma, a capacidade de os dois modelos em questão preverem os valores tarifários a serem cobrados pelos prestadores de serviços de abastecimento de água e coleta de esgoto, a partir da análise das tarifas anteriormente praticadas. Os dados subsidiários para elaboração dos modelos foram obtidos por meio do sistema nacional de informações sobre saneamento (SNIS). Confirmada a consistência do banco de dados primário, procedeu-se com processamento destes dados, e definição das variáveis mais intervenientes para a definição da tarifa por meio da técnica de análise de correlação. Propôs-se a classificação dos sistemas de acordo com a classe jurídica do prestador de serviço, os cenários financeiros (superávit ou déficit) destes prestadores e o porte populacional dos municípios atendidos. Os resultados obtidos indicaram que os processos de previsão, em ambos os modelos utilizados, foram capazes de prever com elevada acurácia as tarifas, e garanti... (Resumo completo, clicar acesso eletrônico abaixo)
Abstract: The objective of the present work was evaluating forecasting models for water tariff applied to municipal and private companies in the South and Southeast regions of Brazil. Using the calculation methodology and subsequent comparison of the errors obtained for the forecasts, also verifying the applicability of the forecast tariffs for each supply system. Two prediction models are used, the first based on multiple linear regression techniques and the second based on the application of artificial neural networks. Evaluating, in this way, the ability of the two models in question to predict the tariff values to be charged by the water supply and wastewater collection service providers, based on the analysis of the tariffs previously practiced. The subsidiary data for the elaboration of the models were obtained through the national sanitation information system (SNIS). Confirming the consistency of the primary database, we proceeded with processing of these data and definition of the most intervening variables for the definition of the tariff through the correlation analysis technique. The classification of the systems according to the legal class of the service provider, the financial scenarios (surplus or deficit) of these providers and the population size of the municipalities served were proposed. The obtained results indicated that the forecasting processes, in both models used, were able to predict with high accuracy the tariffs, and guaranteed the maintenance of the surplu... (Complete abstract click electronic access below)
Mestre
Tavares, Guilherme Farias. "Modelagem matemática e sistemas inteligentes para predição do comportamento alimentar de suínos nas fases de crescimento e terminação." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/11/11152/tde-28072017-082242/.
Повний текст джерелаThe swine production in an activity of great importance to Brazil and to the world. However, because they maintain a constant body temperature and, alterations in the thermic accommodation environment can directly affect their physiological and behavioral responses for maintaining the internal temperature. Thus, the objective of this study was to access the feeding behavior of growing-finishing pigs of different sirelines and gender and its relationship with climate variables (thermic environment). Furthermore, mathematical models based on classic logic was developed as well as an intelligent system for predicting the total time spent eating (TM, min day -1). The data was collected in an experimental farm located in Clay Center, Nebraska, United States. The experimental period contemplated two seasons (summer and winter), totalizing 63 days (9 weeks) of information collected for each season. The housed animals were from three different commercial sirelines: Landrace, Duroc and Yorkshire. Each pen presented a mix composition, being housed 40 animals of different sirelines and gender. In total, there were 240 housed animals, being 80 animals for each sireline among barrows and gilts. The data registered were air temperature (Tar, °C), dew point temperature (Tpo, °C) and relative humidity of the air (UR, %) every 5 minutes inside the facility. For TM, the data were collected and registered every 20 seconds by a radio frequency data collection system. The thermal comfort was analyzed from the Temperature and Humidity Index (THI) and Specific Enthalpy (H, kJ kg-1 of dry air). In order to evaluate the relationship between the thermic environment and TM, the multivariate statistics through principal component analysis (PCA) and grouping was utilized for obtaining the selection standards of variables to enter in the models. The fuzzy model and the artificial neural networks were developed in a MATLAB® R2015a environment through the Fuzzy and the Neural Network toolboxes with the objective to predict TM, having as entry variables: sireline, gender, age and THI. On the whole, the Tar averages were inside the thermoneutral zone (ZCT), however, these values were below the superior critic THI. In the face of the results analysis, it could be observed in ration to the feeding behavior that the Landrace gilt presented the shortest time eating with averages of 42.19 min day-1 and 43.73 min day-1 for winter and summer respectively followed by the barrow from the same sireline, while the other sirelines presented values above 60 min day-1. It was not observed a significative linear correlation between the thermic environment and TM once the animals were inside their ZCT throughout all the experimentation period, indicating that the feeding behavior was influenced mainly by the homeostatic and cognitivehedonic factors. The multivariate statistics divided the animals in 8 groups, being observed that animals of different sirelines and gender behave the same way throughout the experimentation period, making the mathematical modeling difficult. However, some groups presented a bigger amount of animals of determined sireline and gender, being utilized as \"standard groups\" for the development of the fuzzy model and the artificial neural network. The fuzzy model presented an R2 of 0,858 when utilizing the \"standard group\" data, however, for all the values the R2 was 0.549. In the other hand the neural network presented an R2 of 0.611 for the complete data and an R2 of 0.914 for the \"standard group\". Thus, the artificial neural network appeared to be a tool of a better precision and accuracy when predicting the feeding behavior of pigs on growing-finishing phases.
Cambraia, Mario Sergio. "Automação da redução de perdas técnicas nos sistemas reticulados de distribuição utilizando redes neurais artificiais em redes inteligentes (smart grid)." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/3/3143/tde-05032018-102829/.
Повний текст джерелаThis work presents the methodology, development and testing of an independent automation system, based on Artificial Neural Networks, to reduce technical losses in reticulated underground distribution networks by means of the optimal control of the capacitor banks present in the network. The proposed methodology includes typical functionalities of Intelligent Networks, including practical solutions for the positioning of current sensors in underground networks, collection of field measurements and transmission to the Distribution Operation Center and real-time control of field equipment (capacitors banks). Therefore, this work consists in the implementation of the solution through a low cost of investment in the mitigation of the control of the power factor in the points of delivery to the consumer, and with this there are improvements in the indicators of quality and reliability taking into account the regulatory and contractual requirements of supply of the distributors. The energy concessionaire AES Eletropaulo had great participation in this research project, providing the necessary data of the Reticulated Underground Distribution Network of the city center of São Paulo. The steps of the proposed methodology and the main aspects of system development are also described, as well as the tests performed to prove the results and validate the system.
Moretti, José Fernando [UNESP]. "Sistema inteligente baseado nas redes neurais artificiais para dosagem do concreto." Universidade Estadual Paulista (UNESP), 2010. http://hdl.handle.net/11449/100325.
Повний текст джерелаCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
O concreto é o material estrutural mais utilizado na construção civil. Há mais de um século e meio ele vem sendo estudado e aperfeiçoado. É confeccionado utilizando-se de matérias primas regionais, com características técnicas diferentes de outras regiões. O cimento também se apresenta com diversas formulações. Quantificar adequadamente esses materiais é o objetivo do estudo da dosagem do concreto, de tal modo a se obter um concreto que atenda às necessidades estruturais exigidas. Sendo a principal delas a resistência à compressão. A dosagem do concreto é uma prática essencialmente laboratorial quando se pensa em resultados aceitáveis. Através de experimentos são idealizados ábacos e diagramas que podem fornecer a resistência do concreto endurecido com diversas combinações de matérias primas utilizadas. Não há uma formulação matemática abrangente e bem definida para um processo generalizado de dosagem. A complexidade aumenta quando se adicionam outros componentes a mais no concreto simples e tradicional. Obter a relação entre eles é um trabalho contínuo. As redes neurais vêm sendo utilizadas na solução de problemas da engenharia civil, com ênfase na aplicação da técnica da retropropagação. Ela realiza satisfatoriamente as iterações entre as diversas variáveis, num processo de treinamento e aprendizagem, e tem sido capaz de generalizar soluções aceitáveis. Nesta pesquisa de doutorado é utilizada uma rede neural feedfoward com algoritmo retropropagação para prever a resistência e o módulo de elasticidade do concreto. Os dados de entrada são quantidades de materiais utilizadas para confeccionar 1 m3 de concreto adensado, de forma semelhante a dosagem por diagramas. Será aplicada na interpretação de diagramas de dosagem. Tais diagramas são amplamente utilizados por empresas na confecção de concretos,...
Concrete is the most widely used structural material in construction, for more than a century and a half it has been studied and improved. It's prepared using regional raw materials with different technical characteristics of other regions. The cement also performs with various formulations. Adequately quantify these materials is the goal of the study of the concrete mixtures proportion, to obtain a concrete that meets the structural needs required. The main one being the compressive strength. The strength of concrete is essentially a practice laboratory when one considers acceptable results. Through experiments are idealized abacus and diagrams that can provide the strength of hardened concrete with various combinations of raw materials used. There is no mathematical formulation of comprehensive and well defined for a generalized process of mixes. The complexity increases when other components is added in the most simple and traditional concrete. Obtain the relationship between them is a work in progress. Neural networks have been used in solving engineering problems, with emphasis on applying the technique of backpropagation. It performs satisfactorily iterations between the different variables in a process of training and learning, and has been able to generalize acceptable solutions. In this work is used a feedforward neural network with backpropagation algorithm to predict the compressive strength and modulus of elasticity of the concrete. The input data are quantities of materials used to fabricate 1,0 m3 of concrete hardened, similar dosing for diagrams and abacus. Such diagrams are widely used by companies in the manufacturing of concrete, yielding good precision in the final results. They are produced on the vast experience with the same materials and are highly regional representative to provide subsidies for training neural networks. This... (Complete abstract click electronic access below)
Barbato, Daniela Maria Lemos. "O efeito das lesões nas capacidades de memorização e generalização de um perceptron." Universidade de São Paulo, 1993. http://www.teses.usp.br/teses/disponiveis/54/54131/tde-05092008-144618/.
Повний текст джерелаPerceptrons are layered, feed-forward neural networks. In this work we consider a per-ceptron composed of one input layer with N sensor neurons Si = ±1; i = 1, ... , N which are connected to a single motor neuron δ through the synaptic weights Wj; i = 1, ... , N, which are constrained to take on the values ±1 only. Using the Statistical Mechanics formalism developed by Gardner (1988), we study the effects of eliminating a fraction of synaptic weights on the memorization and generalization capabilities of the neural network described above. We consider also the effects of noise acting during the perceptron training stage. We consider two types of dilution: annealed dilution, where the weights are cut so as to minimize the training error and quenched dilution, where the weights are cut randomly. The annealed dilution which models brain damage in very young patients can improve the memorization ability and, in the case of training with noise, it can also improve the generalization ability. On the other hand, the quenched dilution which models lesions on adult brains always degrades the performance of the network, its main effect being to introduce an effective noise in the training examples.
Janes, Ricardo. "Proposição de um algoritmo para identificação biométrica de pessoas baseado nos padrões de veias das mãos." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/3/3143/tde-20072016-082931/.
Повний текст джерелаThe system has been assembled as a prototype then were acquired and storaged 520 images from the dorsal side of the right hand of 52 different users, and then is accomplished an extracting of a region of interest defined by the largest square portion of the dorsal hand. Then a pre-processing of image has been applied using three different methods of image equalization and smoothing for later extraction of the veins characteristics using the Curvelet Transform in \"wrapping\" function and application of the Local Binary Pattern algorithm (LBP) for scanning the extracted content. On the next step, an identification analysis has been performed using five different classification methods. First, a probabilistic Naive Bayes classifier was used, second a classifier based on linear regression called Kernel Nearest Neighbor (K-NN) was applied, third and fourth two algorithms based on decision trees, C4.5 and Random Forest were tested, and finally an algorithm based on artificial neural networks Multilayer Perceptron was performed. The classifiers have been tested using the cross-validation method, and the information was separated by 10 folds wherein 10% of the data were used for training and 90% of the data were used for testing. From the same data resulted of the pre-processing step, two algorithms have been applied for selection features, the first based on the correlation based feature selection and the second in selecting attributes based to the concept of entropy data. The results proof that the equalization method by contrast limited adaptive histogram equalization, in the pre-processing stage, shown the best results. From the application of classifiers, the best result was achieved by using the artificial neural network proposal and the false acceptance rate (FAR) and false rejection rate (FRR) found through the processing were estimated in 0.038 and 0.003 respectively. Tests were also performed to assess the minimum amount of images needed to identify people and as result five images per user were found as the ideal number. Finally, the assessment of the biometric system permanence was performed using acquired images after a year of the first analysis and the results shown that the system is robust, even that the pictures contain minor changes proportional to index variations of body mass of users.
Mon-Ma, Marly Mitiko. "Análise da importância das variáveis intervenientes nos acidentes de trânsito em interseções urbanas utilizando redes neurais artificiais." Universidade Federal de São Carlos, 2005. https://repositorio.ufscar.br/handle/ufscar/4403.
Повний текст джерелаThe technological development has generated great amount of potential data bases in order to supply information for several aspects related to road safety. However, the transformation of these great amount of data in useful information for the technicians, public managers and the population in general, requests the modeling and the treatment of these data using some analysis tools that allow a visualization of the results in form easily understandable. This work presents a new methodology of traffic accidents analysis based in the artificial neural network (ANN). ANN can be very useful for organizations, public or particular, mainly to those that propose to understand the phenomena of the traffic in order to looking for solutions integrated to several areas such as education, engineering and fiscalization. This research had as general objective to identify the patterns of traffic accidents that happened at urban intersections. The data of accidents that happened in the period from 2000 to 2003, in the city of São Carlos were used for the case study, in order to subsidize the elaboration and the evaluation of public policies of traffic accidents reduction and specially the reduction of accident severity. The study explores the assumption that different accident types are related to different patterns. The patterns obtained by ANN showed that there are significant differences in the factors that can affect the different types of accidents. The knowledge of the patterns of each accident type is essential to develop actions corrective or preventive road safety's improvement in order to avoid undesirable effects when these actions are implemented. However, the comparison between the patterns of the different types of accidents was difficult due to the heterogeneity of the situations and the different elements that compose the road environment that can affect the occurrence of the accident.
O desenvolvimento tecnológico tem gerado grandes bases de dados com potencial para fornecer informações sobre diversos aspectos relacionados com a segurança viária. No entanto, a conversão de um grande volume de dados em informações úteis para os técnicos, gestores públicos e a população em geral, requer a modelagem e o tratamento destes dados utilizando ferramentas de análise que permitam uma visualização dos resultados de forma facilmente compreensível. Este trabalho apresenta uma nova metodologia para análise de acidentes de trânsito fundamentada na rede neural artificial (RNA). A RNA pode ser de grande utilidade para organizações públicas e privadas, principalmente para aquelas que se propõem compreender os fenômenos do trânsito a fim de buscar soluções integradas em diversas áreas tais como educação, engenharia e fiscalização. A pesquisa teve como objetivo geral identificar os padrões de acidentes de trânsito que ocorreram nas interseções urbanas. Os dados de acidentes que ocorreram no período de 2000 a 2003, na cidade de São Carlos foram utilizados para o estudo de caso, visando fornecer subsídios para a elaboração e a avaliação de políticas públicas voltadas para redução do número de acidentes de trânsito e essencialmente na redução global da severidade. O estudo explora a suposição de que diferentes tipos de acidente estão relacionados com padrões distintos. Os padrões obtidos através da RNA mostram que há divergências significativas nos fatores que podem influenciar os diferentes tipos de acidentes. Conhecer padrões de cada tipo de acidente se faz necessária para que as medidas corretivas ou preventivas voltadas para a melhoria da segurança viária não resultem em efeitos indesejados quando são implementadas, no entanto comparações entre padrões de diferentes tipos de acidentes mostraram-se particularmente difíceis devido à heterogeneidade das situações e dos diferentes elementos que compõem o ambiente viário e que podem influenciar na ocorrência do acidente.
Книги з теми "Neural networks (NNs)"
Raff, Lionel, Ranga Komanduri, Martin Hagan, and Satish Bukkapatnam. Neural Networks in Chemical Reaction Dynamics. Oxford University Press, 2012. http://dx.doi.org/10.1093/oso/9780199765652.001.0001.
Повний текст джерелаFox, Raymond. The Use of Self. Oxford University Press, 2011. http://dx.doi.org/10.1093/oso/9780190616144.001.0001.
Повний текст джерелаPadilla, Claudia R., and Mario F. Mendez. Neuropsychiatric Features Across Neurodegenerative Diseases. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780190233563.003.0006.
Повний текст джерелаЧастини книг з теми "Neural networks (NNs)"
Müller, Achim F., and Hans Georg Zimmermann. "Symbolic Prosody Modeling by Causal Retro-causal NNs with Variable Context Length." In Artificial Neural Networks — ICANN 2001, 57–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44668-0_9.
Повний текст джерелаDong, Guowei, Yongming Li, Duo Meng, Fuming Sun, and Rui Bai. "Adaptive NNs Fault-Tolerant Control for Nonstrict-Feedback Nonlinear Systems." In Advances in Neural Networks - ISNN 2017, 11–19. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59081-3_2.
Повний текст джерелаKhedr, Haitham, James Ferlez, and Yasser Shoukry. "PEREGRiNN: Penalized-Relaxation Greedy Neural Network Verifier." In Computer Aided Verification, 287–300. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81685-8_13.
Повний текст джерелаKim, Wook-Dong, Sung-Kwun Oh, and Hyun-Ki Kim. "Fuzzy Clustering-Based Polynomial Radial Basis Function Neural Networks (p-RBF NNs) Classifier Designed with Particle Swarm Optimization." In Advances in Neural Networks – ISNN 2011, 464–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21105-8_54.
Повний текст джерелаMamalakis, Antonios, Imme Ebert-Uphoff, and Elizabeth A. Barnes. "Explainable Artificial Intelligence in Meteorology and Climate Science: Model Fine-Tuning, Calibrating Trust and Learning New Science." In xxAI - Beyond Explainable AI, 315–39. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04083-2_16.
Повний текст джерелаBaniadamdizaj, Shima. "Localization Using DeepLab in Document Images Taken by Smartphones." In Digital Interaction and Machine Intelligence, 63–74. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11432-8_6.
Повний текст джерелаAoun, Mario Antoine. "STDP within NDS Neurons." In Advances in Neural Networks - ISNN 2010, 33–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13278-0_5.
Повний текст джерелаGridin, Ivan. "NNI Recipes." In Automated Deep Learning Using Neural Network Intelligence, 357–77. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8149-9_7.
Повний текст джерелаErnesto, Burattini. "NES: a Neuron-like net for a diagnostic Expert System." In International Neural Network Conference, 675. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0643-3_41.
Повний текст джерелаTran, Hoang-Dung, Neelanjana Pal, Patrick Musau, Diego Manzanas Lopez, Nathaniel Hamilton, Xiaodong Yang, Stanley Bak, and Taylor T. Johnson. "Robustness Verification of Semantic Segmentation Neural Networks Using Relaxed Reachability." In Computer Aided Verification, 263–86. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81685-8_12.
Повний текст джерелаТези доповідей конференцій з теми "Neural networks (NNs)"
Benussi, Elias, Andrea Patane', Matthew Wicker, Luca Laurenti, and Marta Kwiatkowska. "Individual Fairness Guarantees for Neural Networks." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/92.
Повний текст джерелаPollmeier, K., C. R. Burrows, and K. A. Edge. "Condition Monitoring of an Electrohydraulic Position Control System Using Artificial Neural Networks." In ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-62309.
Повний текст джерелаGhoreyshi, M., P. Pilidis, and K. W. Ramsden. "Diagnostics of a Small Jet Engine-Neural Networks Approach." In ASME Turbo Expo 2005: Power for Land, Sea, and Air. ASMEDC, 2005. http://dx.doi.org/10.1115/gt2005-68511.
Повний текст джерелаZhan, Huixin, Kun Zhang, Chenyi Hu, and Victor S. Sheng. "Gated Graph Neural Networks (GG-NNs) for Abstractive Multi-Comment Summarization." In 2021 IEEE International Conference on Big Knowledge (ICBK). IEEE, 2021. http://dx.doi.org/10.1109/ickg52313.2021.00050.
Повний текст джерелаSzabo, T., L. Antoni, G. Horvath, and B. Feher. "A full-parallel digital implementation for pre-trained NNs." In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium. IEEE, 2000. http://dx.doi.org/10.1109/ijcnn.2000.857873.
Повний текст джерелаWang, Jun, Kevin Chiu, and Mark Fuge. "Learning to Abstract and Compose Mechanical Device Function and Behavior." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22714.
Повний текст джерелаBettocchi, R., M. Pinelli, P. R. Spina, M. Venturini, and G. A. Zanetta. "Assessment of the Robustness of Gas Turbine Diagnostics Tools Based on Neural Networks." In ASME Turbo Expo 2006: Power for Land, Sea, and Air. ASMEDC, 2006. http://dx.doi.org/10.1115/gt2006-90118.
Повний текст джерелаHaridas, Akash, and Nagabhushana Rao Vadlamani. "Modelling Wall-Pressure Spectra in Turbulent Boundary Layers Using Neural Networks." In ASME 2021 Gas Turbine India Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/gtindia2021-76301.
Повний текст джерелаBarclay, Andrew, and Jonathan Corney. "Automated Classification of Components for Manufacturing Planning: Single-View Convolutional Neural Network for Global Shape Identification." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22335.
Повний текст джерелаHe, S., and N. Sepehri. "Experimental Study of a Neural Generalized Predictive Force Control for a Hydraulic Actuator." In ASME 2000 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/imece2000-2313.
Повний текст джерелаЗвіти організацій з теми "Neural networks (NNs)"
Galili, Naftali, Roger P. Rohrbach, Itzhak Shmulevich, Yoram Fuchs, and Giora Zauberman. Non-Destructive Quality Sensing of High-Value Agricultural Commodities Through Response Analysis. United States Department of Agriculture, October 1994. http://dx.doi.org/10.32747/1994.7570549.bard.
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