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Artykuły w czasopismach na temat "Neural network RBF"
Zhu, Jian Min, Peng Du i Ting Ting Fu. "Research for RBF Neural Networks Modeling Accuracy of Determining the Basis Function Center Based on Clustering Methods". Advanced Materials Research 317-319 (sierpień 2011): 1529–36. http://dx.doi.org/10.4028/www.scientific.net/amr.317-319.1529.
Pełny tekst źródłaLuan, Tiantian, Mingxiao Sun, Guoqing Xia i Daidai Chen. "Evaluation for Sortie Generation Capacity of the Carrier Aircraft Based on the Variable Structure RBF Neural Network with the Fast Learning Rate". Complexity 2018 (22.10.2018): 1–19. http://dx.doi.org/10.1155/2018/6950124.
Pełny tekst źródłaYakovyna, V. S. "Software failures prediction using RBF neural network". Odes’kyi Politechnichnyi Universytet. Pratsi, nr 2 (15.06.2015): 111–18. http://dx.doi.org/10.15276/opu.2.46.2015.20.
Pełny tekst źródłaWen, Hui, Tao Yan, Zhiqiang Liu i Deli Chen. "Integrated neural network model with pre-RBF kernels". Science Progress 104, nr 3 (lipiec 2021): 003685042110261. http://dx.doi.org/10.1177/00368504211026111.
Pełny tekst źródłaLiu, Yunbing. "Research on Nonlinear Time Series Processing Method for Automatic Building Construction Management". Journal of Control Science and Engineering 2022 (30.06.2022): 1–6. http://dx.doi.org/10.1155/2022/7025223.
Pełny tekst źródłaYu, Fa Hong, Mei Jia Chen i Wei Zhi Liao. "A Novel Learning Evaluation Method Based on RBF Neural Network". Applied Mechanics and Materials 385-386 (sierpień 2013): 1697–700. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.1697.
Pełny tekst źródłaLi, Hui Jun, i Li Zhang. "Prediction of Tensile Strength Based on RBF Neural Network". Advanced Materials Research 476-478 (luty 2012): 1309–12. http://dx.doi.org/10.4028/www.scientific.net/amr.476-478.1309.
Pełny tekst źródłaLiu, Dong Dong. "A Method about Load Distribution of Rolling Mills Based on RBF Neural Network". Advanced Materials Research 279 (lipiec 2011): 418–22. http://dx.doi.org/10.4028/www.scientific.net/amr.279.418.
Pełny tekst źródłaTsoulos, Ioannis G., Alexandros Tzallas i Evangelos Karvounis. "A Two-Phase Evolutionary Method to Train RBF Networks". Applied Sciences 12, nr 5 (25.02.2022): 2439. http://dx.doi.org/10.3390/app12052439.
Pełny tekst źródłaYu, Ying. "GDP Economic Forecasting Model Based on Improved RBF Neural Network". Mathematical Problems in Engineering 2022 (9.09.2022): 1–11. http://dx.doi.org/10.1155/2022/7630268.
Pełny tekst źródłaRozprawy doktorskie na temat "Neural network RBF"
FERREIRA, Aida Araújo. "Comparação de arquiteturas de redes neurais para sistemas de reconheceimento de padrões em narizes artificiais". Universidade Federal de Pernambuco, 2004. https://repositorio.ufpe.br/handle/123456789/2465.
Pełny tekst źródłaInstituto Federal de Educação, Ciência e Tecnologia de Pernambuco
Um nariz artificial é um sistema modular composto de duas partes principais: um sistema sensor, formado de elementos que detectam odores e um sistema de reconhecimento de padrões que classifica os odores detectados. Redes neurais artificiais têm sido utilizadas como sistema de reconhecimento de padrões para narizes artificiais e vêm apresentando resultados promissores. Desde os anos 80, pesquisas para criação de narizes artificiais, que permitam detectar e classificar odores, vapores e gases automaticamente, têm tido avanços significativos. Esses equipamentos podem ser utilizados no monitoramento ambiental para controlar a qualidade do ar, na área de saúde para realizar diagnóstico de doenças e nas indústrias de alimentos para o controle de qualidade e o monitoramento de processos de produção. Esta dissertação investiga a utilização de quatro técnicas diferentes de redes neurais para criação de sistemas de reconhecimento de padrões em narizes artificiais. O trabalho está dividido em quatro partes principais: (1) introdução aos narizes artificiais, (2) redes neurais artificiais para sistema de reconhecimento de padrões, (3) métodos para medir o desempenho de sistemas de reconhecimento de padrões e comparar os resultados e (4) estudo de caso. Os dados utilizados para o estudo de caso, foram obtidos por um protótipo de nariz artificial composto por um arranjo de oito sensores de polímeros condutores, expostos a nove tipos diferentes de aguarrás. Foram adotadas as técnicas Multi-Layer Perceptron (MLP), Radial Base Function (RBF), Probabilistic Neural Network (PNN) e Time Delay Neural Network (TDNN) para criar os sistemas de reconhecimento de padrões. A técnica PNN foi investigada em detalhes, por dois motivos principais: esta técnica é indicada para realização de tarefas de classificação e seu treinamento é feito em apenas um passo, o que torna a etapa de criação dessas redes muito rápida. Os resultados foram comparados através dos valores dos erros médios de classificação utilizando o método estatístico de Teste de Hipóteses. As redes PNN correspondem a uma nova abordagem para criação de sistemas de reconhecimento de padrões de odor. Estas redes tiveram um erro médio de classificação de 1.1574% no conjunto de teste. Este foi o menor erro obtido entre todos os sistemas criados, entretanto mesmo com o menor erro médio de classificação, os testes de hipóteses mostraram que os classificadores criados com PNN não eram melhores do que os classificadores criados com a arquitetura RBF, que obtiveram um erro médio de classificação de 1.3889%. A grande vantagem de criar classificadores com a arquitetura PNN foi o pequeno tempo de treinamento dos mesmos, chegando a ser quase imediato. Porém a quantidade de nodos na camada escondida foi muito grande, o que pode ser um problema, caso o sistema criado deva ser utilizado em equipamentos com poucos recursos computacionais. Outra vantagem de criar classificadores com redes PNN é relativa à quantidade reduzida de parâmetros que devem ser analisados, neste caso apenas o parâmetro relativo à largura da função Gaussiana precisou ser investigado
Damasceno, Nielsen Castelo. "Separa??o cega de fontes lineares e n?o lineares usando algoritmo gen?tico, redes neurais artificiais RBF e negentropia de R?nyi como medida de independ?ncia". Universidade Federal do Rio Grande do Norte, 2010. http://repositorio.ufrn.br:8080/jspui/handle/123456789/15358.
Pełny tekst źródłaConventional methods to solve the problem of blind source separation nonlinear, in general, using series of restrictions to obtain the solution, often leading to an imperfect separation of the original sources and high computational cost. In this paper, we propose an alternative measure of independence based on information theory and uses the tools of artificial intelligence to solve problems of blind source separation linear and nonlinear later. In the linear model applies genetic algorithms and R?nyi of negentropy as a measure of independence to find a separation matrix from linear mixtures of signals using linear form of waves, audio and images. A comparison with two types of algorithms for Independent Component Analysis widespread in the literature. Subsequently, we use the same measure of independence, as the cost function in the genetic algorithm to recover source signals were mixed by nonlinear functions from an artificial neural network of radial base type. Genetic algorithms are powerful tools for global search, and therefore well suited for use in problems of blind source separation. Tests and analysis are through computer simulations
Os m?todos convencionais para resolver o problema de separa??o cega de fontes n?o lineares em geral utilizam uma s?rie de restri??es ? obten??o da solu??o, levando muitas vezes a uma n?o perfeita separa??o das fontes originais e alto custo computacional. Neste trabalho, prop?e-se uma alternativa de medida de independ?ncia com base na teoria da informa??o e utilizam-se ferramentas da intelig?ncia artificial para resolver problemas de separa??o cega de fontes lineares e posteriormente n?o lineares. No modelo linear aplica-se algoritmos gen?ticos e a Negentropia de R?nyi como medida de independ?ncia para encontrar uma matriz de separa??o linear a partir de misturas lineares usando sinais de forma de ondas, ?udios e imagens. Faz-se uma compara??o com dois tipos de algoritmos de An?lise de Componentes Independentes bastante difundidos na literatura. Posteriormente, utiliza-se a mesma medida de independ?ncia como fun??o custo no algoritmo gen?tico para recuperar sinais de fontes que foram misturadas por fun??es n?o lineares a partir de uma rede neural artificial do tipo base radial. Algoritmos gen?ticos s?o poderosas ferramentas de pesquisa global e, portanto, bem adaptados para utiliza??o em problemas de separa??o cega de fontes. Os testes e as an?lises se d?o atrav?s de simula??es computacionais
Pham, Hoang Anh. "Coordination de systèmes sous-marins autonomes basée sur une méthodologie intégrée dans un environnement Open-source". Electronic Thesis or Diss., Toulon, 2021. http://www.theses.fr/2021TOUL0020.
Pełny tekst źródłaThis thesis studies the coordination of autonomous underwater robots in the context of coastal seabed exploration or facility inspections. Investigating an integrated methodology, we have created a framework to design and simulate low-cost underwater robot controls with different model assumptions of increasing complexity (linear, non-linear, and finally non-linear with uncertainties). By using this framework, we have studied algorithms to solve the problem of formation control, collision avoidance between robots and obstacle avoidance of a group of underwater robots. More precisely, we first consider underwater robot models as linear systems of simple integrator type, from which we can build a formation controller using consensus and avoidance algorithms. We then extend these algorithms for the nonlinear dynamic model of a Bluerov robot in an iterative design process. Then we have integrated a Radial Basis Function neural network, already proven in convergence and stability, with the algebraic controller to estimate and compensate for uncertainties in the robot model. Finally, we have presented simulation results and real basin tests to validate the proposed concepts. This work also aims to convert a remotely operated ROV into an autonomous ROV-AUV hybrid
Soukup, Jiří. "Metody a algoritmy pro rozpoznávání obličejů". Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2008. http://www.nusl.cz/ntk/nusl-374588.
Pełny tekst źródłaLi, Junxu. "A Dynamic Parameter Tuning Algorithm For Rbf Neural Networks". Fogler Library, University of Maine, 1999. http://www.library.umaine.edu/theses/pdf/LiJ1999.pdf.
Pełny tekst źródłaGuo, Zhihao. "Intelligent multiple objective proactive routing in MANET with predictions on delay, energy, and link lifetime". online version, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=case1195705509.
Pełny tekst źródłaMedagam, Peda Vasanta Reddy. "Online optimal control for a class of nonlinear system using RBF neural networks /". Available to subscribers only, 2008. http://proquest.umi.com/pqdweb?did=1650508351&sid=19&Fmt=2&clientId=1509&RQT=309&VName=PQD.
Pełny tekst źródłaMachado, Madson Cruz. "Sintonia RNA-RBF para o Projeto Online de Sistemas de Controle Adaptativo". Universidade Federal do Maranhão, 2017. http://tedebc.ufma.br:8080/jspui/handle/tede/1744.
Pełny tekst źródłaMade available in DSpace on 2017-07-18T19:31:22Z (GMT). No. of bitstreams: 1 MadsonMachado.pdf: 3046442 bytes, checksum: 71cc6800f83fdbf38b97607067653f63 (MD5) Previous issue date: 2017-05-26
The need to increase industrial productivity coupled with quality and low cost requirements has generated a demand for the development of high performance controllers. Motivated by this demand, we presented in this work models, algorithms and a methodology for the online project of high-performance control systems. The models have characteristics of adaptability through adaptive control system architectures. The models developed were based on artificial neural networks of radial basis function type, for the online project of model reference adaptive control systems associated with the of sliding modes control. The algorithms and the embedded system developed for the online project were evaluated for tracking mobile targets, in this case, the solar radiation. The control system has the objective of keeping the surface of the photovoltaic module perpendicular to the solar radiation, in this way the energy generated by the module will be as high as possible. The process consists of a photovoltaic panel coupled in a structure that rotates around an axis parallel to the earth’s surface, positioning the panel in order to capture the highest solar radiation as function of its displacement throughout the day.
A necessidade de aumentar a produtividade industrial, associada com os requisitos de qualidade e baixo custo, gerou uma demanda para o desenvolvimento de controladores de alto desempenho. Motivado por esta demanda, apresentou-se neste trabalho modelos, algoritmos e uma metodologia para o projeto online de sistemas de controle de alto desempenho. Os modelos apresentam características de adaptabilidade por meio de arquiteturas de sistemas de controle adaptativo. O desenvolvimento de modelos, baseia-se em redes neurais artificiais (RNA), do tipo função de base radial (RBF, radial basis function), para o projeto online de sistemas de controle adaptativo do tipo modelo de referência associado com o controle de modos deslizantes (SMC, sliding mode control). Os algoritmos e o sistema embarcado desenvolvidos para o projeto online são avaliados para o rastreamento de alvos móveis, neste caso, o rastreamento da radiação solar. O sistema de controle tem o objetivo de manter a superfície do módulo fotovoltaico perpendicular à radiação solar, pois dessa forma a energia gerada pelo módulo será a maior possível. O processo consiste de um painel fotovoltaico acoplado em uma estrutura que gira em torno de um eixo paralelo à superfície da terra, posicionando o painel de forma a capturar a maior radiação solar em função de seu deslocamento ao longo do dia.
Turner, Joseph Vernon. "Application of Artificial Neural Networks in Pharmacokinetics". Thesis, The University of Sydney, 2003. http://hdl.handle.net/2123/488.
Pełny tekst źródłaTurner, Joseph Vernon. "Application of Artificial Neural Networks in Pharmacokinetics". University of Sydney, 2003. http://hdl.handle.net/2123/488.
Pełny tekst źródłaKsiążki na temat "Neural network RBF"
Liu, Jinkun. Radial Basis Function (RBF) Neural Network Control for Mechanical Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34816-7.
Pełny tekst źródłaHong, X. A Givens rotation based fast backward elimination algorithm for RBF neural network pruning. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1996.
Znajdź pełny tekst źródłaLiu, Jinkun. Radial Basis Function (RBF) Neural Network Control for Mechanical Systems: Design, Analysis and Matlab Simulation. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.
Znajdź pełny tekst źródłaRadial Basis Function Rbf Neural Network Control For Mechanical Systems Design Analysis And Matlab Simulation. Springer-Verlag Berlin and Heidelberg GmbH &, 2013.
Znajdź pełny tekst źródłaLuppi, Pierre-Hervé, Olivier Clément, Christelle Peyron i Patrice Fort. Neurobiology of REM sleep. Redaktorzy Sudhansu Chokroverty, Luigi Ferini-Strambi i Christopher Kennard. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199682003.003.0003.
Pełny tekst źródłaGöknar, Izzet, i Levent Sevgi. Complex Computing-Networks: Brain-Like and Wave-oriented Electrodynamic Algorithms. Springer London, Limited, 2006.
Znajdź pełny tekst źródła(Editor), I. C. Göknar, i L. Sevgi (Editor), red. Complex Computing-Networks : Brain-like and Wave-oriented Electrodynamic Algorithms (Springer Proceedings in Physics) (Springer Proceedings in Physics). Springer, 2006.
Znajdź pełny tekst źródłaCzęści książek na temat "Neural network RBF"
Burdsall, B., i C. Giraud-Carrier. "GA-RBF: A Self-Optimising RBF Network". W Artificial Neural Nets and Genetic Algorithms, 346–49. Vienna: Springer Vienna, 1998. http://dx.doi.org/10.1007/978-3-7091-6492-1_76.
Pełny tekst źródłaLiu, Jinkun. "Adaptive RBF Neural Network Control". W Radial Basis Function (RBF) Neural Network Control for Mechanical Systems, 71–112. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34816-7_4.
Pełny tekst źródłaLiu, Jinkun. "Digital RBF Neural Network Control". W Radial Basis Function (RBF) Neural Network Control for Mechanical Systems, 293–309. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34816-7_9.
Pełny tekst źródłaLiu, Jinkun. "Discrete RBF Neural Network Control". W Intelligent Control Design and MATLAB Simulation, 215–34. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5263-7_10.
Pełny tekst źródłaLiu, Jinkun. "Adaptive RBF Neural Network Control". W Intelligent Control Design and MATLAB Simulation, 159–87. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5263-7_8.
Pełny tekst źródłaLiu, Jinkun. "RBF Neural Network Design and Simulation". W Radial Basis Function (RBF) Neural Network Control for Mechanical Systems, 19–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34816-7_2.
Pełny tekst źródłaLi, JingBing, HuaiQiang Zhang, YouLing Zhou i Yong Bai. "RBF Neural Network Case Teaching Research". W Communications in Computer and Information Science, 351–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22418-8_49.
Pełny tekst źródłaWei, Xiaotao, Houkuan Huang i Shengfeng Tian. "A Modified RBF Neural Network for Network Anomaly Detection". W Advances in Neural Networks - ISNN 2006, 261–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11760191_38.
Pełny tekst źródłaRoberts, Stephen, i Lionel Tarassenko. "Automated Sleep EEg Analysis using an RBF Network". W Applications of Neural Networks, 305–20. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4757-2379-3_13.
Pełny tekst źródłaGueorguieva, Natacha, i Iren Valova. "Building RBF Neural Network Topology through Potential Functions". W Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003, 1033–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44989-2_123.
Pełny tekst źródłaStreszczenia konferencji na temat "Neural network RBF"
Servin, M., i F. J. Cuevas. "New kind of classifier neural network using RBFs". W OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1991. http://dx.doi.org/10.1364/oam.1991.mii3.
Pełny tekst źródłaTitsias, M., i A. Likas. "A probabilistic RBF network for classification". W 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.2000.860779.
Pełny tekst źródłaBi, Jing, Kun Zhang i Xiaojing Cheng. "Intrusion Detection Based on RBF Neural Network". W 2009 International Symposium on Information Engineering and Electronic Commerce (IEEC). IEEE, 2009. http://dx.doi.org/10.1109/ieec.2009.80.
Pełny tekst źródłaZhou, Kaili, Yaohong Kang, Yan Huang i Erli Feng. "Encrypting Algorithm Based on RBF Neural Network". W Third International Conference on Natural Computation (ICNC 2007). IEEE, 2007. http://dx.doi.org/10.1109/icnc.2007.353.
Pełny tekst źródłaLi, Zhang, Xu Qingyang, Jin Shibo i Li Jiangning. "Coking flue temperature RBF neural network model". W 2015 27th Chinese Control and Decision Conference (CCDC). IEEE, 2015. http://dx.doi.org/10.1109/ccdc.2015.7161862.
Pełny tekst źródłaShijie, Yan, i Wang Xu. "RBF Neural Network Adaptive Control of Microturbine". W 2009 WRI Global Congress on Intelligent Systems. IEEE, 2009. http://dx.doi.org/10.1109/gcis.2009.66.
Pełny tekst źródłaXiang-Bin Yan, Zhen Wang, Shu-Hua Yu i Yi-Jun Li. "Time series forecasting with RBF neural network". W Proceedings of 2005 International Conference on Machine Learning and Cybernetics. IEEE, 2005. http://dx.doi.org/10.1109/icmlc.2005.1527764.
Pełny tekst źródłaWang, Lei, i Zhongyi Zuo. "Travel Mode Recognition Using RBF Neural Network". W 14th COTA International Conference of Transportation Professionals. Reston, VA: American Society of Civil Engineers, 2014. http://dx.doi.org/10.1061/9780784413623.069.
Pełny tekst źródłaKanojia, Mahendra G., i Siby Abraham. "Breast cancer detection using RBF neural network". W 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I). IEEE, 2016. http://dx.doi.org/10.1109/ic3i.2016.7917990.
Pełny tekst źródłaArthy, G., i C. N. Marimuthu. "Immune RBF neural network algorithm for DSTATCOM". W 2016 International Conference on Computer Communication and Informatics. IEEE, 2016. http://dx.doi.org/10.1109/iccci.2016.7480035.
Pełny tekst źródłaRaporty organizacyjne na temat "Neural network RBF"
Idakwo, Gabriel, Sundar Thangapandian, Joseph Luttrell, Zhaoxian Zhou, Chaoyang Zhang i Ping Gong. Deep learning-based structure-activity relationship modeling for multi-category toxicity classification : a case study of 10K Tox21 chemicals with high-throughput cell-based androgen receptor bioassay data. Engineer Research and Development Center (U.S.), lipiec 2021. http://dx.doi.org/10.21079/11681/41302.
Pełny tekst źródłaAlwan, Iktimal, Dennis D. Spencer i Rafeed Alkawadri. Comparison of Machine Learning Algorithms in Sensorimotor Functional Mapping. Progress in Neurobiology, grudzień 2023. http://dx.doi.org/10.60124/j.pneuro.2023.30.03.
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