Results 31 to 40 of about 10,487,885 (363)
Jurnal Serambi Engineering, 2022 Neural network Backpropagation is a good method to use to determine RGB color (Red, Green, Blue) because it can give high accuracy values. Neural network backpropagation there are several activation functions that can be used.
Ikhwan Pamungkas +2 more
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An n-Sigmoid Activation Function to Improve the Squeeze-and-Excitation for 2D and 3D Deep Networks
Electronics, 2023 The Squeeze-and-Excitation (SE) structure has been designed to enhance the neural network performance by allowing it to execute positive channel-wise feature recalibration and suppress less useful features.
Desire Burume Mulindwa, Shengzhi Du
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Sigmoid functions for the smooth approximation to the absolute value function [PDF]
Moroccan Journal of Pure and Applied Analysis, 2020 Abstract We present smooth approximations to the absolute value function |x| using sigmoid functions. In particular, x erf(x/μ) is proved to be a better smooth approximation for |x| than x tanh(x/μ) and
Yogesh J. Bagul, Christophe Chesneau
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IEEE Access, 2020 A non-iterative learning algorithm for artificial neural networks is an alternative to optimize the neural network parameters with extremely fast convergence time.
Syukron Abu Ishaq Alfarozi +3 more
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Advanced Functional Materials, EarlyView.The characteristics of a vertical n–p–i–p heterostructure transistor device, which exhibits a voltage‐tunable transition between Gaussian and sigmoid functions, are investigated. The mixed state of the transfer curve enables the utilization of both exploitation and exploration, improving computational performance in reinforcement learning tasks ...
Jisoo Park +7 more
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IEEE Access, 2020 Since the existing terrain classification algorithm based on deep learning is not ideal for unbalanced PolSAR classification, a effective terrain classification algorithm based on wavelet kernel sparse deep coding network under unbalanced data set is ...
Xiangdong Chen, Jianghong Deng
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Sensors, 2019 Since second-order statistics-based methods rely heavily on Gaussianity assumption and fractional lower-order statistics-based methods depend on a priori knowledge of non-Gaussian noise, there remains a void in wideband bistatic multiple-input/multiple ...
Li Li, Nicolas H. Younan, Xiaofei Shi
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Approximation by Superpositions of a Sigmoidal Function
Zeitschrift für Analysis und ihre Anwendungen, 2003 We generalize a result of B. Gao and Y. Xu [J. Math. Anal. Appl. 178 (1993) 221–226] concerning the approximation of functions of bounded variation by linear combinations of a fixed sigmoidal function to the class of functions of bounded f-variation. Also, in the case of one variable, a proposition of A. R. Barron [IEEE Trans. Inf. Theory 36 (1993) 930–
LEWICKI G, MARINO, Giuseppe
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Approximation by superpositions of a sigmoidal function [PDF]
Mathematics of Control, Signals, and Systems, 1989 In this paper we demonstrate that finite linear combinations of compositions of a fixed, univariate function and a set ofaffine functionals can uniformly approximate any continuous function of n real variables with support in the unit hypercube; only mild conditions are imposed on the univariate function.
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Transportation Safety and Environment, 2021 This paper investigates the problem of parameter identification for ship nonlinear Nomoto model with small test data, a nonlinear innovation-based identification algorithm is presented by embedding sigmoid function in the stochastic gradient algorithm.
Xianku Zhang +2 more
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