Literatura científica selecionada sobre o tema "Complex-valued neural networks"
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Artigos de revistas sobre o assunto "Complex-valued neural networks"
Hirose, Akira. "Complex-valued Neural Networks". IEEJ Transactions on Electronics, Information and Systems 131, n.º 1 (2011): 2–8. http://dx.doi.org/10.1541/ieejeiss.131.2.
Texto completo da fonteBoonsatit, Nattakan, Santhakumari Rajendran, Chee Peng Lim, Anuwat Jirawattanapanit e Praneesh Mohandas. "New Adaptive Finite-Time Cluster Synchronization of Neutral-Type Complex-Valued Coupled Neural Networks with Mixed Time Delays". Fractal and Fractional 6, n.º 9 (13 de setembro de 2022): 515. http://dx.doi.org/10.3390/fractalfract6090515.
Texto completo da fonteNitta, Tohru. "Orthogonality of Decision Boundaries in Complex-Valued Neural Networks". Neural Computation 16, n.º 1 (1 de janeiro de 2004): 73–97. http://dx.doi.org/10.1162/08997660460734001.
Texto completo da fonteNitta, Tohru. "Learning Transformations with Complex-Valued Neurocomputing". International Journal of Organizational and Collective Intelligence 3, n.º 2 (abril de 2012): 81–116. http://dx.doi.org/10.4018/joci.2012040103.
Texto completo da fonteGuo, Song, e Bo Du. "Global Exponential Stability of Periodic Solution for Neutral-Type Complex-Valued Neural Networks". Discrete Dynamics in Nature and Society 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/1267954.
Texto completo da fonteNITTA, TOHRU. "THE UNIQUENESS THEOREM FOR COMPLEX-VALUED NEURAL NETWORKS WITH THRESHOLD PARAMETERS AND THE REDUNDANCY OF THE PARAMETERS". International Journal of Neural Systems 18, n.º 02 (abril de 2008): 123–34. http://dx.doi.org/10.1142/s0129065708001439.
Texto completo da fonteValle, Marcos Eduardo. "Complex-Valued Recurrent Correlation Neural Networks". IEEE Transactions on Neural Networks and Learning Systems 25, n.º 9 (setembro de 2014): 1600–1612. http://dx.doi.org/10.1109/tnnls.2014.2341013.
Texto completo da fonteKobayashi, Masaki. "Symmetric Complex-Valued Hopfield Neural Networks". IEEE Transactions on Neural Networks and Learning Systems 28, n.º 4 (abril de 2017): 1011–15. http://dx.doi.org/10.1109/tnnls.2016.2518672.
Texto completo da fonteKobayashi, Masaki. "Bicomplex Projection Rule for Complex-Valued Hopfield Neural Networks". Neural Computation 32, n.º 11 (novembro de 2020): 2237–48. http://dx.doi.org/10.1162/neco_a_01320.
Texto completo da fonteKobayashi, Masaki. "Fast Recall for Complex-Valued Hopfield Neural Networks with Projection Rules". Computational Intelligence and Neuroscience 2017 (2017): 1–6. http://dx.doi.org/10.1155/2017/4894278.
Texto completo da fonteTeses / dissertações sobre o assunto "Complex-valued neural networks"
Barrachina, Jose Agustin. "Complex-valued neural networks for radar applications". Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG094.
Texto completo da fonteRadar signal and SAR image processing generally require complex-valued representations and operations, e.g., Fourier, wavelet transforms, Wiener, matched filters, etc. However, the vast majority of architectures for deep learning are currently based on real-valued operations, which restrict their ability to learn from complex-valued features. Despite the emergence of Complex-Valued Neural Networks (CVNNs), their application on radar and SAR still lacks study on their relevance and efficiency. And the comparison against an equivalent Real-Valued Neural Network (RVNN) is usually biased.In this thesis, we propose to investigate the merits of CVNNs for classifying complex-valued data. We show that CVNNs achieve better performance than their real-valued counterpart for classifying non-circular Gaussian data. We also define a criterion of equivalence between feed-forward fully connected and convolutional CVNNs and RVNNs in terms of trainable parameters while keeping a similar architecture. We statistically compare the performance of equivalent Multi-Layer Perceptrons (MLPs), Convolutional Neural Networks (CNNs), and Fully Convolutional Neural Networks (FCNNs) for polarimetric SAR image segmentation. SAR image splitting and balancing classes are also studied to avoid learning biases. In parallel, we also proposed an open-source toolbox to facilitate the implementation of CVNNs and the comparison with real-equivalent networks
Minin, Alexey [Verfasser]. "Modeling of Dynamical Systems with Complex Valued Recurrent Neural Networks / Alexey Minin. Gutachter: Alois Knoll ; Mark J. Embrechts. Betreuer: Alois Knoll ; Hans-Georg Zimmermann". München : Universitätsbibliothek der TU München, 2012. http://d-nb.info/1024963985/34.
Texto completo da fonteHu, Qiong. "Statistical parametric speech synthesis based on sinusoidal models". Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/28719.
Texto completo da fonte"Dynamical analysis of complex-valued recurrent neural networks with time-delays". 2013. http://library.cuhk.edu.hk/record=b5884392.
Texto completo da fonteThesis (Ph.D.)--Chinese University of Hong Kong, 2013.
Includes bibliographical references (leaves 140-153).
Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Abstracts also in Chinese.
Wang, Shu-Fan, e 王書凡. "Monaural Source Separation Based on Complex-valued Deep Neural Network". Thesis, 2016. http://ndltd.ncl.edu.tw/handle/fyvr7y.
Texto completo da fonte國立中央大學
資訊工程學系
104
Deep neural networks (DNNs) have become a popular means of separating a target source from a mixed signal. Almost all DNN-based methods modify only the magnitude spectrum of the mixture. The phase spectrum is left unchanged, which is inherent in the short-time Fourier transform (STFT) coefficients of the input signal. However, recent studies have revealed that incorporating phase information can improve the perceptual quality of separated sources. Accordingly, in this paper, estimating the STFT coefficients of target sources from an input mixture is regarded a regression problem. A fully complex-valued deep neural network is developed herein to learn the nonlinear mapping from complex-valued STFT coefficients of a mixture to sources. The proposed method is applied to speech separation and singing separation.
Yu, Kuo, e 俞果. "Complex-Valued Deep Recurrent Neural Network for Singing Voice Separation". Thesis, 2017. http://ndltd.ncl.edu.tw/handle/4waab5.
Texto completo da fonte國立中央大學
資訊工程學系
105
Deep neural networks (DNN) have performed impressively in the processing of multimedia signals. Most DNN-based approaches were developed to handle real-valued data; very few have been designed for complex-valued data, despite their being essential for processing various types of multimedia signal. Accordingly, this work presents a complex-valued deep recurrent neural network (C-DRNN) for singing voice separation. The C-DRNN operates on the complex-valued short-time discrete Fourier transform (STFT) domain. A key aspect of the C-DRNN is that the activations and weights are complex-valued. The goal herein is to reconstruct the singing voice and the background music from a mixed signal. For error back-propagation, CR-calculus is utilized to calculate the complex-valued gradients of the objective function. To reinforce model regularity, two constraints are incorporated into the cost function of the C-DRNN. The first is an additional masking layer that ensures the sum of separated sources equals the input mixture. The second is a discriminative term that preserves the mutual difference between two separated sources. Finally, the proposed method is evaluated using the MIR-1K dataset and a singing voice separation task. Experimental results demonstrate that the proposed method outperforms the state-of-the-art DNN-based methods.
Livros sobre o assunto "Complex-valued neural networks"
Hirose, Akira. Complex-Valued Neural Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27632-3.
Texto completo da fonteHirose, Akira. Complex-Valued Neural Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-33457-6.
Texto completo da fonteHirose, Akira, ed. Complex-Valued Neural Networks. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118590072.
Texto completo da fonteHirose, Akira. Complex-Valued Neural Networks. 2a ed. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.
Encontre o texto completo da fonteAizenberg, Igor. Complex-Valued Neural Networks with Multi-Valued Neurons. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20353-4.
Texto completo da fonteservice), SpringerLink (Online, ed. Complex-Valued Neural Networks with Multi-Valued Neurons. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.
Encontre o texto completo da fonteSuresh, Sundaram, Narasimhan Sundararajan e Ramasamy Savitha. Supervised Learning with Complex-valued Neural Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-29491-4.
Texto completo da fonteSuresh, Sundaram. Supervised Learning with Complex-valued Neural Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.
Encontre o texto completo da fonte1963-, Hirose Akira, ed. Complex-valued neural networks: Theories and applications. River Edge, NJ: World Scientific, 2003.
Encontre o texto completo da fonteZhang, Ziye, Zhen Wang, Jian Chen e Chong Lin. Complex-Valued Neural Networks Systems with Time Delay. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-5450-4.
Texto completo da fonteCapítulos de livros sobre o assunto "Complex-valued neural networks"
Hirose, Akira. "Application Fields and Fundamental Merits of Complex-Valued Neural Networks". In Complex-Valued Neural Networks, 1–31. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118590072.ch1.
Texto completo da fonteWong, Wai Kit, Gin Chong Lee, Chu Kiong Loo, Way Soong Lim e Raymond Lock. "Quaternionic Fuzzy Neural Network for View-Invariant Color Face Image Recognition". In Complex-Valued Neural Networks, 235–78. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118590072.ch10.
Texto completo da fonteFiori, Simone. "Neural System Learning on Complex-Valued Manifolds". In Complex-Valued Neural Networks, 33–57. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118590072.ch2.
Texto completo da fonteNitta, Tohru. "N-Dimensional Vector Neuron and Its Application to theN-Bit Parity Problem". In Complex-Valued Neural Networks, 59–74. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118590072.ch3.
Texto completo da fonteAmin, Md Faijul, e Kazuyuki Murase. "Learning Algorithms in Complex-Valued Neural Networks using Wirtinger Calculus". In Complex-Valued Neural Networks, 75–102. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118590072.ch4.
Texto completo da fonteIsokawa, Teijiro, Haruhiko Nishimura e Nobuyuki Matsui. "Quaternionic Neural Networks for Associative Memories". In Complex-Valued Neural Networks, 103–31. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118590072.ch5.
Texto completo da fonteKuroe, Yasuaki. "Models of Recurrent Clifford Neural Networks and Their Dynamics". In Complex-Valued Neural Networks, 133–51. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118590072.ch6.
Texto completo da fonteSavitha, Ramasamy, Sundaram Suresh e Narasimhan Sundarara. "Meta-Cognitive Complex-Valued Relaxation Network and Its Sequential Learning Algorithm". In Complex-Valued Neural Networks, 153–83. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118590072.ch7.
Texto completo da fonteManyakov, Nikolay V., Igor Aizenberg, Nikolay Chumerin e Marc M. Van Hulle. "Multilayer Feedforward Neural Network with Multi-Valued Neurons for Brain-Computer Interfacing". In Complex-Valued Neural Networks, 185–208. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118590072.ch8.
Texto completo da fonteHong, Xia, Sheng Chen e Chris J. Harris. "Complex-Valued B-Spline Neural Networks for Modeling and Inverse of Wiener Systems". In Complex-Valued Neural Networks, 209–34. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118590072.ch9.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Complex-valued neural networks"
Ishikawa, Masaya, e Kazuyuki Murase. "Complex-valued online classifier". In 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016. http://dx.doi.org/10.1109/ijcnn.2016.7727779.
Texto completo da fontePopa, Calin-Adrian. "Complex-Valued Deep Boltzmann Machines". In 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 2018. http://dx.doi.org/10.1109/ijcnn.2018.8489359.
Texto completo da fonteKagan, Evgeny, Alexander Rybalov e Ronald Yager. "Complex-Valued Logic for Neural Networks". In 2018 IEEE International Conference on the Science of Electrical Engineering in Israel (ICSEE). IEEE, 2018. http://dx.doi.org/10.1109/icsee.2018.8646029.
Texto completo da fontePopa, Calin-Adrian. "Complex-valued convolutional neural networks for real-valued image classification". In 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, 2017. http://dx.doi.org/10.1109/ijcnn.2017.7965936.
Texto completo da fonteSavitha, R., S. Suresh e N. Sundararajan. "Complex-valued function approximation using a Fully Complex-valued RBF (FC-RBF) learning algorithm". In 2009 International Joint Conference on Neural Networks (IJCNN 2009 - Atlanta). IEEE, 2009. http://dx.doi.org/10.1109/ijcnn.2009.5178624.
Texto completo da fonteMandic, Danilo P. "Complex valued recurrent neural networks for noncircular complex signals". In 2009 International Joint Conference on Neural Networks (IJCNN 2009 - Atlanta). IEEE, 2009. http://dx.doi.org/10.1109/ijcnn.2009.5178960.
Texto completo da fonteOuabi, Othmane-Latif, Radmila Pribic e Sorin Olaru. "Stochastic Complex-valued Neural Networks for Radar". In 2020 28th European Signal Processing Conference (EUSIPCO). IEEE, 2021. http://dx.doi.org/10.23919/eusipco47968.2020.9287425.
Texto completo da fonteLv, Yiqi, Xiang Meng, Yong Luo e Yan Pei. "Glucose Sensing Utilizing Complex-Valued Neural Networks". In CNIOT'23: 2023 4th International Conference on Computing, Networks and Internet of Things. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3603781.3603845.
Texto completo da fonteRen, Lei, Chunyang Liu e Ying Zhang. "Noise Benefits in Complex-Valued Neural Networks". In 2023 8th International Conference on Image, Vision and Computing (ICIVC). IEEE, 2023. http://dx.doi.org/10.1109/icivc58118.2023.10269993.
Texto completo da fonteMasuyama, Naoki, e Chu Kiong Loo. "Quantum-Inspired Complex-Valued Multidirectional Associative Memory". In 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, 2015. http://dx.doi.org/10.1109/ijcnn.2015.7280403.
Texto completo da fonte