Relevant bibliographies by topics / Complex-valued neural networks

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

Academic literature on the topic 'Complex-valued neural networks'

Author: Grafiati
Published: 25 May 2024
Last updated: 31 July 2025

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Journal articles on the topic "Complex-valued neural networks"

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Hirose, Akira. "Complex-valued Neural Networks." IEEJ Transactions on Electronics, Information and Systems 131, no. 1 (2011): 2–8. http://dx.doi.org/10.1541/ieejeiss.131.2.

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Guo, Song, and 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.

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This paper deals with a class of neutral-type complex-valued neural networks with delays. By means of Mawhin’s continuation theorem, some criteria on existence of periodic solutions are established for the neutral-type complex-valued neural networks. By constructing an appropriate Lyapunov-Krasovskii functional, some sufficient conditions are derived for the global exponential stability of periodic solutions to the neutral-type complex-valued neural networks. Finally, numerical examples are given to show the effectiveness and merits of the present results.
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Valle, Marcos Eduardo. "Complex-Valued Recurrent Correlation Neural Networks." IEEE Transactions on Neural Networks and Learning Systems 25, no. 9 (2014): 1600–1612. http://dx.doi.org/10.1109/tnnls.2014.2341013.

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Kobayashi, Masaki. "Symmetric Complex-Valued Hopfield Neural Networks." IEEE Transactions on Neural Networks and Learning Systems 28, no. 4 (2017): 1011–15. http://dx.doi.org/10.1109/tnnls.2016.2518672.

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Boonsatit, Nattakan, Santhakumari Rajendran, Chee Peng Lim, Anuwat Jirawattanapanit, and Praneesh Mohandas. "New Adaptive Finite-Time Cluster Synchronization of Neutral-Type Complex-Valued Coupled Neural Networks with Mixed Time Delays." Fractal and Fractional 6, no. 9 (2022): 515. http://dx.doi.org/10.3390/fractalfract6090515.

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The issue of adaptive finite-time cluster synchronization corresponding to neutral-type coupled complex-valued neural networks with mixed delays is examined in this research. A neutral-type coupled complex-valued neural network with mixed delays is more general than that of a traditional neural network, since it considers distributed delays, state delays and coupling delays. In this research, a new adaptive control technique is developed to synchronize neutral-type coupled complex-valued neural networks with mixed delays in finite time. To stabilize the resulting closed-loop system, the Lyapun
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Dong, Yu-Chao, Xi-Kun Li, Ming Yang, et al. "Quantum state classification via complex-valued neural networks." Laser Physics Letters 21, no. 10 (2024): 105206. http://dx.doi.org/10.1088/1612-202x/ad7246.

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Abstract To efficiently complete quantum information processing tasks, quantum neural networks (QNNs) should be introduced rather than the common classical neural networks, but the QNNs in the current noisy intermediate-scale quantum era cannot perform better than classical neural networks because of scale and the efficiency limits. So if the quantum properties can be introduced into classical neural networks, more efficient classical neural networks may be constructed for tasks in the field of quantum information. Complex numbers play an indispensable role in the standard quantum theory, and
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Flores, Alexandra Macarena, Víctor José Huilca, César Palacios-Arias, María José López, Omar Darío Delgado, and María Belén Paredes. "From Iterative Methods to Neural Networks: Complex-Valued Approaches in Medical Image Reconstruction." Electronics 14, no. 10 (2025): 1959. https://doi.org/10.3390/electronics14101959.

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Complex-valued neural networks have emerged as an effective instrument in image reconstruction, exhibiting significant advancements compared to conventional techniques. This study introduces an innovative methodology to tackle the difficulties related to image reconstruction within medical microwave imaging. Initially, in the estimation phase, the proposed methodology integrates the Born iterative method with quadratic programming. Subsequently, in the refinement stage, the study explores the application of complex-valued neural networks to enhance the quality of reconstructions. The research
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Nitta, Tohru. "Orthogonality of Decision Boundaries in Complex-Valued Neural Networks." Neural Computation 16, no. 1 (2004): 73–97. http://dx.doi.org/10.1162/08997660460734001.

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This letter presents some results of an analysis on the decision boundaries of complex-valued neural networks whose weights, threshold values, input and output signals are all complex numbers. The main results may be summarized as follows. (1) A decision boundary of a single complex-valued neuron consists of two hypersurfaces that intersect orthogonally, and divides a decision region into four equal sections. The XOR problem and the detection of symmetry problem that cannot be solved with two-layered real-valued neural networks, can be solved by two-layered complex-valued neural networks with
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Song, Qiankun, Qian Wu, and Yurong Liu. "Stabilization of chaotic quaternion-valued neutral-type neural networks via sampled-data control with two-sided looped functional approach." Nonlinear Analysis: Modelling and Control 29, no. 6 (2024): 1150–66. https://doi.org/10.15388/namc.2024.29.37852.

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The quaternion-valued neutral-type neural networks (QVNTNNs) stability problem through designing sampled-data controller is investigated in this paper. A main stability criterion of the considered neural networks (NNs) is obtained in the form of linear matrix inequalities (LMIs) based on the two-sided looped functional method. The effectiveness of the criterion is shown by a numerical example. It needs to be emphasized that the considered QVNTNNs model in this paper is not broken down into real-valued or complex-valued models in stability analysis, and the acquired criterion holds for both rea
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Kobayashi, Masaki. "Bicomplex Projection Rule for Complex-Valued Hopfield Neural Networks." Neural Computation 32, no. 11 (2020): 2237–48. http://dx.doi.org/10.1162/neco_a_01320.

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A complex-valued Hopfield neural network (CHNN) with a multistate activation function is a multistate model of neural associative memory. The weight parameters need a lot of memory resources. Twin-multistate activation functions were introduced to quaternion- and bicomplex-valued Hopfield neural networks. Since their architectures are much more complicated than that of CHNN, the architecture should be simplified. In this work, the number of weight parameters is reduced by bicomplex projection rule for CHNNs, which is given by the decomposition of bicomplex-valued Hopfield neural networks. Comp
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Dissertations / Theses on the topic "Complex-valued neural networks"

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Barrachina, Jose Agustin. "Complex-valued neural networks for radar applications." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG094.

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Le traitement des signaux radars et des images SAR nécessite généralement des représentations et des opérations à valeurs complexes, telles que les transformées de Fourier et d'ondelettes, les filtres de Wiener et les filtres adaptés, etc. Cependant, la grande majorité des architectures d'apprentissage profond sont actuellement basées sur des opérations à valeurs réelles, ce qui limite leur capacité d'apprentissage à partir de données complexes. Malgré l'émergence des réseaux de neurones à valeurs complexes (CVNN), leur application au radar et à l'imagerie SAR manque encore d'études sur leur p
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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.

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Hu, Qiong. "Statistical parametric speech synthesis based on sinusoidal models." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/28719.

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This study focuses on improving the quality of statistical speech synthesis based on sinusoidal models. Vocoders play a crucial role during the parametrisation and reconstruction process, so we first lead an experimental comparison of a broad range of the leading vocoder types. Although our study shows that for analysis / synthesis, sinusoidal models with complex amplitudes can generate high quality of speech compared with source-filter ones, component sinusoids are correlated with each other, and the number of parameters is also high and varies in each frame, which constrains its application
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"Dynamical analysis of complex-valued recurrent neural networks with time-delays." 2013. http://library.cuhk.edu.hk/record=b5884392.

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Hu, Jin.<br>Thesis (Ph.D.)--Chinese University of Hong Kong, 2013.<br>Includes bibliographical references (leaves 140-153).<br>Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.<br>Abstracts also in Chinese.
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Wang, Shu-Fan, and 王書凡. "Monaural Source Separation Based on Complex-valued Deep Neural Network." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/fyvr7y.

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碩士<br>國立中央大學<br>資訊工程學系<br>104<br>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
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Yu, Kuo, and 俞果. "Complex-Valued Deep Recurrent Neural Network for Singing Voice Separation." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/4waab5.

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碩士<br>國立中央大學<br>資訊工程學系<br>105<br>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
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Books on the topic "Complex-valued neural networks"

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Hirose, Akira. Complex-Valued Neural Networks. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27632-3.

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Hirose, Akira. Complex-Valued Neural Networks. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-33457-6.

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Hirose, Akira, ed. Complex-Valued Neural Networks. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118590072.

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Hirose, Akira. Complex-Valued Neural Networks. 2nd ed. Springer Berlin Heidelberg, 2012.

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Aizenberg, Igor. Complex-Valued Neural Networks with Multi-Valued Neurons. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20353-4.

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service), SpringerLink (Online, ed. Complex-Valued Neural Networks with Multi-Valued Neurons. Springer Berlin Heidelberg, 2011.

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Suresh, Sundaram, Narasimhan Sundararajan, and Ramasamy Savitha. Supervised Learning with Complex-valued Neural Networks. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-29491-4.

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1963-, Hirose Akira, ed. Complex-valued neural networks: Theories and applications. World Scientific, 2003.

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Suresh, Sundaram. Supervised Learning with Complex-valued Neural Networks. Springer Berlin Heidelberg, 2013.

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Zhang, Ziye, Zhen Wang, Jian Chen, and Chong Lin. Complex-Valued Neural Networks Systems with Time Delay. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-5450-4.

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Book chapters on the topic "Complex-valued neural networks"

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Hirose, Akira. "Application Fields and Fundamental Merits of Complex-Valued Neural Networks." In Complex-Valued Neural Networks. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118590072.ch1.

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Wong, Wai Kit, Gin Chong Lee, Chu Kiong Loo, Way Soong Lim, and Raymond Lock. "Quaternionic Fuzzy Neural Network for View-Invariant Color Face Image Recognition." In Complex-Valued Neural Networks. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118590072.ch10.

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Fiori, Simone. "Neural System Learning on Complex-Valued Manifolds." In Complex-Valued Neural Networks. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118590072.ch2.

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Nitta, Tohru. "N-Dimensional Vector Neuron and Its Application to theN-Bit Parity Problem." In Complex-Valued Neural Networks. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118590072.ch3.

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Amin, Md Faijul, and Kazuyuki Murase. "Learning Algorithms in Complex-Valued Neural Networks using Wirtinger Calculus." In Complex-Valued Neural Networks. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118590072.ch4.

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Isokawa, Teijiro, Haruhiko Nishimura, and Nobuyuki Matsui. "Quaternionic Neural Networks for Associative Memories." In Complex-Valued Neural Networks. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118590072.ch5.

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Kuroe, Yasuaki. "Models of Recurrent Clifford Neural Networks and Their Dynamics." In Complex-Valued Neural Networks. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118590072.ch6.

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Savitha, Ramasamy, Sundaram Suresh, and Narasimhan Sundarara. "Meta-Cognitive Complex-Valued Relaxation Network and Its Sequential Learning Algorithm." In Complex-Valued Neural Networks. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118590072.ch7.

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Manyakov, Nikolay V., Igor Aizenberg, Nikolay Chumerin, and Marc M. Van Hulle. "Multilayer Feedforward Neural Network with Multi-Valued Neurons for Brain-Computer Interfacing." In Complex-Valued Neural Networks. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118590072.ch8.

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Hong, Xia, Sheng Chen, and Chris J. Harris. "Complex-Valued B-Spline Neural Networks for Modeling and Inverse of Wiener Systems." In Complex-Valued Neural Networks. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118590072.ch9.

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Conference papers on the topic "Complex-valued neural networks"

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Zhang, Mengxuan, Jingyuan Shi, Long Liu, Xilu Wang, and Licheng Jiao. "Evolutionary Complex-Valued CNN for PolSAR Image Classification." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650936.

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Lei, Jierui, Qingyi Yang, Bo Li, and Wenjian Zhang. "CCFN: Depression Detection via Multimodal Fusion with Complex-valued Capsule Network." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650262.

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Xiao, Jiang, Yi Zhong, and Yun Li. "Growing NAS for Complex-Valued CNNs in Digital Predistortion Power Amplifier Applications." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651335.

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Wang, Qingchen, Zhe Li, Zdenka Babic, Wei Deng, Ljubiša Stanković, and Danilo P. Mandic. "Widely Linear Matched Filter: A Lynchpin towards the Interpretability of Complex-valued CNNs." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651012.

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Zhang, Zhuo, Yiran Wang, Jing Bai, Zhu Xiao, and Huaji Zhou. "Uncovering the Authentic RF Fingerprint: Exploiting Random Window Slicing and Complex-Valued Network." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651357.

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Ishikawa, Masaya, and 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.

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Popa, 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.

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Kagan, Evgeny, Alexander Rybalov, and 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.

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Popa, 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.

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Savitha, R., S. Suresh, and 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.

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