complex-valued neural networks(CVNNs)have shown their excellent efficiency compared to their real counterparts in speech enhancement,image and signal *** throughout the years have made many efforts to improve the lear...
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complex-valued neural networks(CVNNs)have shown their excellent efficiency compared to their real counterparts in speech enhancement,image and signal *** throughout the years have made many efforts to improve the learning algorithms and activation functions of *** CVNNs have proven to have better performance in handling the naturally complex-valued data and signals,this area of study will grow and expect the arrival of some effective improvements in the ***,there exists an obvious reason to provide a comprehensive survey paper that systematically collects and categorizes the advancement of *** this paper,we discuss and summarize the recent advances based on their learning algorithms,activation functions,which is the most challenging part of building a CVNN,and ***,we outline the structure and applications of complex-valued convolutional,residual and recurrent neural ***,we also present some challenges and future research directions to facilitate the exploration of the ability of CVNNs.
This paper presents application of complex neural network for calculating complex resonating frequency of microstrip patch antenna on superstrate. The results obtained from neural network agrees well with the theoreti...
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ISBN:
(纸本)9781479932672
This paper presents application of complex neural network for calculating complex resonating frequency of microstrip patch antenna on superstrate. The results obtained from neural network agrees well with the theoretical results.
Among the useful blind equalization algorithms, stochastic-gradient iterative equalization schemes are based on minimizing a nonconvex and nonlinear cost function, However, as they use a linear FIR filter with a conve...
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Among the useful blind equalization algorithms, stochastic-gradient iterative equalization schemes are based on minimizing a nonconvex and nonlinear cost function, However, as they use a linear FIR filter with a convex decision region, their residual estimation error is high. In this paper, four nonlinear blind equalization schemes that employ a complex-valued multilayer perceptron instead of the linear filter are proposed and their learning algorithms are derived. After the important properties that a suitable complex-valued activation function must possess are discussed, a new complex-valued activation function is developed for the proposed schemes to deal with QAM signals of any constellation sizes. It has been further proven that by the nonlinear transformation of the proposed function, the correlation coefficient between the real and imaginary parts of input data decreases when they are jointly Gaussian random variables. Last, the effectiveness of the proposed schemes is verified in terms of initial convergence speed and MSE in the steady state. In particular, even without carrier phase tracking procedure, the proposed schemes correct an arbitrary phase rotation caused by channel distortion.
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