Generalization Performance of Artificial Neural Network Based-Demodulator for Eigenvalue Modulated Signal

机译：基于人工神经网络基于特征值调制信号的泛化性能

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摘要

The generalization performance of an artificial neural network-based demodulator for an eigenvalue modulated signal is investigated. Successful demodulation for transmission distances from zero to 3,000 km and carrier frequency offset within 1 GHz is demonstrated.

机译：研究了用于特征值调制信号的人工神经网络的解调器的泛化性能。对1GHz内的距离零到3,000km和载波频率偏移的传输距离成功解调。

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《Opto-Electronics and Communications Conference》|2020年|1-3|共3页
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Ken Mishina; Daisuke Hisano; Yuki Yoshida; Akihiro Maruta;
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Frequency modulation; Artificial neural networks; Numerical simulation; Eigenvalues and eigenfunctions; Filtering theory; Demodulation;

机译：频率调制;人工神经网络;数值模拟;特征值和特征碰撞;过滤理论;解调;

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专利

1. Demodulation of optical eigenvalue modulated signals using neural networks [J] . Shohei Yamamoto, Ken Mishina, Akihiro Maruta IEICE Communications Express . 2019,第12期

机译：利用神经网络对光特征值调制信号进行解调
2. Investigation and Comparison of Generalization Ability of Multi-Layer Perceptron and Radial Basis Function Artificial Neural Networks for Signal Power Loss Prediction [J] . Virginia C. Ebhota, Joseph Isabona, Viranjay M. Srivastava International Journal on Communications Antenna and Propagation . 2019,第1期

机译：多层感知器和径向基函数人工神经网络对信号功率损耗预测能力的研究与比较
3. Investigation and Comparison of Generalization Ability of Multi-Layer Perceptron and Radial Basis Function Artificial Neural Networks for Signal Power Loss Prediction [J] . Virginia C. Ebhota, Joseph Isabona, Viranjay M. Srivastava International Journal on Communications Antenna and Propagation . 2019,第1期

机译：用于信号功率损耗预测的多层感知和径向基函数人工神经网络的泛化能力的调查与比较
4. Artificial Neural Network-Based-Receiver for Eigenvalue-Modulated Signal in Presence of Optical CFO [C] . Ken Mishina, Yuki Yoshida, Daisuke Hisano, European Conference on Optical Communications . 2020

机译：基于人工神经网络的接收器，用于在光学CFO存在下的特征值调制信号
5. Radial complexity estimation for improved generalization in artificial neural networks. [D] . Myers, Lemuel Ray, Jr. 1998

机译：径向复杂度估计可改善人工神经网络的泛化能力。
6. Prediction of the performance of artificial neural networks in mapping sEMG to finger joint angles via signal pre-investigation techniques [O] . Wafa Batayneh, Enas Abdulhay, Mohammad Alothman 2020

机译：通过信号预调查技术预测将sEMG映射到手指关节角度的人工神经网络的性能
7. Demodulation of optical eigenvalue modulated signals using neural networks [O] . Shohei Yamamoto, Ken Mishina, Akihiro Maruta 2019

机译：使用神经网络解调光学特征值调制信号

Generalization Performance of Artificial Neural Network Based-Demodulator for Eigenvalue Modulated Signal

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