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Prospect utility with hyperbolic tangent function
International Journal of Approximate ReasoningzbMATH Open Web Interface contents unavailable due to conflicting licenses.
Patrick Adjei +2 more
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On the Composition and Neutrix Composition of the Delta Function with the Hyperbolic Tangent and Its Inverse Functions [PDF]
Journal of Applied Mathematics, 2011 Let F be a distribution in 𝒟′ and let f be a locally summable function. The composition F(f(x)) of F and f is said to exist and be equal to the distribution h(x) if the limit of the sequence {Fn(f(x))} is equal to h(x), where Fn(x) = F(x)*δn(x) for n = 1,2, … and {δn(x)} is a certain regular sequence converging to the Dirac delta function. It is proved
Brian Fisher 0002, Adem Kiliçman
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A Novel Power Curve Modeling Framework for Wind Turbines
Advances in Electrical and Computer Engineering, 2019 This paper presents two main novelties concerning power curve modeling of wind turbines. First novelty lies in the hybridization of 5 widely-used parametric functions and 8 recently-developed metaheuristic optimization algorithms.
YESILBUDAK, M.
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Functional Equivalence and Path Connectivity of Reducible Hyperbolic Tangent Networks
Advances in Neural Information Processing Systems 36, 2023 Understanding the learning process of artificial neural networks requires clarifying the structure of the parameter space within which learning takes place. A neural network parameter's functional equivalence class is the set of parameters implementing the same input--output function. For many architectures, almost all parameters have a simple and well-
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Walailak Journal of Science and Technology, 2021 The novel coronavirus 2019 (COVID-19) pandemic was declared a global health crisis. The real-time accurate and predictive model of the number of infected cases could help inform the government of providing medical assistance and public health decision ...
Rati WONGSATHAN
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Configurable Model for Sigmoid and Hyperbolic Tangent Functions
Applications of Modelling and Simulation, 2023 Recurrent neural networks (RNNs) are considered to be among the most important types of neural networks especially for the applications where processing of a sequence of data comes to place. RNNs are in general computationally expensive and need a lot of processing time and power.
Khaled Salah +3 more
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Advances in Mechanical Engineering, 2017 The article proposes a robust control approach based on self-organizing Hermite-polynomial-based neural network disturbance observer for a class of non-affine nonlinear systems with input saturation, state constraint, and unknown compound disturbance ...
Qiang Zhang, Cui Wang
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Modeling Software Reliability with Learning and Fatigue
Mathematics, 2023 Software reliability growth models (SRGMs) based on the non-homogeneous Poisson process have played a significant role in predicting the number of remaining errors in software, enhancing software reliability.
Tahere Yaghoobi, Man-Fai Leung
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Prediction of Availability Indicator of Water Pipes Using Artificial Intelligence
E3S Web of Conferences, 2017 The paper presents the results of artificial neural networks application to the availability indicator prediction. The forecasted results indicate that artificial networks may be used to model the reliability level of the water supply systems.
Kutyłowska Małgorzata
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MathematicsA guaranteed performance event-triggered adaptive consensus control is established for uncertain multiagent systems under time-varying actuator faults. To eliminate the impact caused by actuator faults, an adaptive neural network compensation strategy is
Kairui Chen +5 more
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