Results 31 to 40 of about 217,698 (308)
Univariant Approximation by Superpositions of a Sigmoidal Function
Journal of Mathematical Analysis and Applications, 1993 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Gao, B., Xu, Y.
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Coefficient estimates for starlike and convex functions related to sigmoid functions
Ukrains’kyi Matematychnyi Zhurnal, 2023 UDC 517.5 We give sharp coefficient bounds for starlike and convex functions related to modified sigmoid functions. We also provide some sharp coefficients bounds for the inverse functions and sharp bounds for the initial logarithmic coefficients and some coefficient differences.
Raza, M., Thomas, D. K., Riaz, A.
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Two-Dimensional Convolutional Recurrent Neural Networks for Speech Activity Detection [PDF]
, 2019 Speech Activity Detection (SAD) plays an important role in mobile communications and automatic speech recognition (ASR). Developing efficient SAD systems for real-world applications is a challenging task due to the presence of noise.
Chen, Liming +7 more
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Optimal Performance and Application for Seagull Optimization Algorithm Using a Hybrid Strategy
Entropy, 2022 This paper aims to present a novel hybrid algorithm named SPSOA to address problems of low search capability and easy to fall into local optimization of seagull optimization algorithm.
Qingyu Xia +5 more
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Turning on the alarm: the neural mechanisms of the transition from innocuous to painful sensation [PDF]
, 2011 The experience of pain occurs when the level of a stimulus is sufficient to elicit a marked affective response, putatively to warn the organism of potential danger and motivate appropriate behavioral responses.
Backonja, Miroslav +3 more
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Constructive Approximation by Superposition of Sigmoidal Functions
Analysis in Theory and Applications, 2013 In this paper, a constructive theory is developed for approximating func- tions of one or more variables by superposition of sigmoidal functions. This is done in the uniform norm as well as in the L p norm. Results for the simultaneous approx- imation, with the same order of accuracy, of a function and its derivatives (whenever these exist), are ...
COSTARELLI, DANILO, SPIGLER, Renato
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SinLU: Sinu-Sigmoidal Linear Unit
Mathematics, 2022 Non-linear activation functions are integral parts of deep neural architectures. Given the large and complex dataset of a neural network, its computational complexity and approximation capability can differ significantly based on what activation function
Ashis Paul +4 more
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Kongzhi Yu Xinxi Jishu, 2022 Small wind power generation systems have the characteristics of nonlinear strong coupling and the application requirements of small weight and low cost.
WANG Hongru, ZHANG Zhigang
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Approximation order to a function in ( ) by superposition of a sigmoidal function
Applied Mathematics Letters, 2002 Abstract We investigate the approximation error to a continuous function defined on the whole real line by superposition of a sigmoidal function. With the minimal constraints on a continuous function, we show that the approximation order by 3n superposition of a sigmoidal function is O (1/n).
Hong, Bum Il, Hahm, Nahmwoo
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, 2019 Long Short-Term Memory (LSTM) infers the long term dependency through a cell state maintained by the input and the forget gate structures, which models a gate output as a value in [0,1] through a sigmoid function.
Jang, JoonHo +3 more
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