Academic literature on the topic 'Complex-valued neural networks'
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Journal articles on the topic "Complex-valued neural networks"
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.
Full textBoonsatit, 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 (September 13, 2022): 515. http://dx.doi.org/10.3390/fractalfract6090515.
Full textNitta, Tohru. "Orthogonality of Decision Boundaries in Complex-Valued Neural Networks." Neural Computation 16, no. 1 (January 1, 2004): 73–97. http://dx.doi.org/10.1162/08997660460734001.
Full textNitta, Tohru. "Learning Transformations with Complex-Valued Neurocomputing." International Journal of Organizational and Collective Intelligence 3, no. 2 (April 2012): 81–116. http://dx.doi.org/10.4018/joci.2012040103.
Full textGuo, 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.
Full textNITTA, TOHRU. "THE UNIQUENESS THEOREM FOR COMPLEX-VALUED NEURAL NETWORKS WITH THRESHOLD PARAMETERS AND THE REDUNDANCY OF THE PARAMETERS." International Journal of Neural Systems 18, no. 02 (April 2008): 123–34. http://dx.doi.org/10.1142/s0129065708001439.
Full textValle, Marcos Eduardo. "Complex-Valued Recurrent Correlation Neural Networks." IEEE Transactions on Neural Networks and Learning Systems 25, no. 9 (September 2014): 1600–1612. http://dx.doi.org/10.1109/tnnls.2014.2341013.
Full textKobayashi, Masaki. "Symmetric Complex-Valued Hopfield Neural Networks." IEEE Transactions on Neural Networks and Learning Systems 28, no. 4 (April 2017): 1011–15. http://dx.doi.org/10.1109/tnnls.2016.2518672.
Full textKobayashi, Masaki. "Bicomplex Projection Rule for Complex-Valued Hopfield Neural Networks." Neural Computation 32, no. 11 (November 2020): 2237–48. http://dx.doi.org/10.1162/neco_a_01320.
Full textKobayashi, 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.
Full textDissertations / Theses on the topic "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.
Full textRadar 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.
Full textHu, Qiong. "Statistical parametric speech synthesis based on sinusoidal models." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/28719.
Full text"Dynamical analysis of complex-valued recurrent neural networks with time-delays." 2013. http://library.cuhk.edu.hk/record=b5884392.
Full textThesis (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, and 王書凡. "Monaural Source Separation Based on Complex-valued Deep Neural Network." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/fyvr7y.
Full text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, and 俞果. "Complex-Valued Deep Recurrent Neural Network for Singing Voice Separation." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/4waab5.
Full text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.
Books on the topic "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.
Full textHirose, Akira. Complex-Valued Neural Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-33457-6.
Full textHirose, Akira, ed. Complex-Valued Neural Networks. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118590072.
Full textHirose, Akira. Complex-Valued Neural Networks. 2nd ed. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.
Find full textAizenberg, 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.
Full textservice), SpringerLink (Online, ed. Complex-Valued Neural Networks with Multi-Valued Neurons. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.
Find full textSuresh, Sundaram, Narasimhan Sundararajan, and 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.
Full textSuresh, Sundaram. Supervised Learning with Complex-valued Neural Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.
Find full text1963-, Hirose Akira, ed. Complex-valued neural networks: Theories and applications. River Edge, NJ: World Scientific, 2003.
Find full textZhang, Ziye, Zhen Wang, Jian Chen, and 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.
Full textBook chapters on the topic "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.
Full textWong, 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, 235–78. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118590072.ch10.
Full textFiori, 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.
Full textNitta, 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.
Full textAmin, Md Faijul, and 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.
Full textIsokawa, Teijiro, Haruhiko Nishimura, and 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.
Full textKuroe, 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.
Full textSavitha, Ramasamy, Sundaram Suresh, and 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.
Full textManyakov, 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, 185–208. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118590072.ch8.
Full textHong, 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, 209–34. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118590072.ch9.
Full textConference papers on the topic "Complex-valued neural networks"
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.
Full textPopa, 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.
Full textKagan, 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.
Full textPopa, 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.
Full textSavitha, 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.
Full textMandic, 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.
Full textOuabi, Othmane-Latif, Radmila Pribic, and 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.
Full textLv, Yiqi, Xiang Meng, Yong Luo, and 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.
Full textRen, Lei, Chunyang Liu, and 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.
Full textMasuyama, Naoki, and 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.
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