Notice of Retraction: Analysis of Hyperbolic Tangent Passive Resistive Neuron With CMOS-Memristor Circuit

2018 ◽  
Author(s):  
Madina Kenzhina ◽  
Irina Dolzhikova
Keyword(s):  
Hyperbolic Tangent

INFLUENCE OF HEAT AND CHEMICAL REACTIONS ON HYPERBOLIC TANGENT FLUID MODEL FOR BLOOD FLOW THROUGH A TAPERED ARTERY WITH A STENOSIS

2012 ◽  
Vol 43 (1) ◽  
pp. 69-94 ◽  
Author(s):  
Noreen Sher Akbar ◽  
Sohail Nadeem ◽  
Mohamed Ali
Keyword(s):  
Blood Flow ◽  
Chemical Reactions ◽  
Fluid Model ◽  
Hyperbolic Tangent ◽  
Tapered Artery ◽  
Flow Through

Slip And Hall Effects On The Flow Of A Hyperbolic Tangent Fluid Through Porous Medium In A Planar Channel With Peristalsis

GIS Business ◽  
2020 ◽  
Vol 15 (1) ◽  
pp. 383-394
Author(s):  
K. Shalini ◽  
K.Rajasekhar
Keyword(s):  
Porous Medium ◽  
Pressure Gradient ◽  
Volume Flow Rate ◽  
Perturbation Technique ◽  
Volume Flow ◽  
Planar Channel ◽  
Hyperbolic Tangent ◽  
Closed Form Solutions ◽  
Long Wavelength ◽  
Hall Effects

In this paper, the effect of Slip and Hall effects on the flow of Hyperbolic tangent fluid through a porous medium in a planar channel with peristalsis under the assumption of long wavelength is investigated. A Closed form solutions are obtained for axial velocity and pressure gradient by employing perturbation technique. The effects of various emerging parameters on the pressure gradient, time averaged volume flow rate and frictional force are discussed with the aid of graphs.

VLSI implementation of transcendental function hyperbolic tangent for deep neural network accelerators

2021 ◽  
pp. 104270
Author(s):  
Gunjan Rajput ◽  
Gopal Raut ◽  
Mahesh Chandra ◽  
Santosh Kumar Vishvakarma
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Vlsi Implementation ◽  
Transcendental Function ◽  
Hyperbolic Tangent

scholarly journals Elite Exploitation: A Combination of Mathematical Concept and EMO Approach for Multi-Objective Decision Making

Symmetry ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 136
Author(s):  
Wenxiao Li ◽  
Yushui Geng ◽  
Jing Zhao ◽  
Kang Zhang ◽  
Jianxin Liu
Keyword(s):  
Decision Making ◽  
Optimization Problems ◽  
Hybrid Genetic Algorithm ◽  
Decision Makers ◽  
Superior Performance ◽  
Test Problems ◽  
Mathematical Function ◽  
Hyperbolic Tangent ◽  
Nsga Ii ◽  
Multi Objective

This paper explores the combination of a classic mathematical function named “hyperbolic tangent” with a metaheuristic algorithm, and proposes a novel hybrid genetic algorithm called NSGA-II-BnF for multi-objective decision making. Recently, many metaheuristic evolutionary algorithms have been proposed for tackling multi-objective optimization problems (MOPs). These algorithms demonstrate excellent capabilities and offer available solutions to decision makers. However, their convergence performance may be challenged by some MOPs with elaborate Pareto fronts such as CFs, WFGs, and UFs, primarily due to the neglect of diversity. We solve this problem by proposing an algorithm with elite exploitation strategy, which contains two parts: first, we design a biased elite allocation strategy, which allocates computation resources appropriately to elites of the population by crowding distance-based roulette. Second, we propose a self-guided fast individual exploitation approach, which guides elites to generate neighbors by a symmetry exploitation operator, which is based on mathematical hyperbolic tangent function. Furthermore, we designed a mechanism to emphasize the algorithm’s applicability, which allows decision makers to adjust the exploitation intensity with their preferences. We compare our proposed NSGA-II-BnF with four other improved versions of NSGA-II (NSGA-IIconflict, rNSGA-II, RPDNSGA-II, and NSGA-II-SDR) and four competitive and widely-used algorithms (MOEA/D-DE, dMOPSO, SPEA-II, and SMPSO) on 36 test problems (DTLZ1–DTLZ7, WGF1–WFG9, UF1–UF10, and CF1–CF10), and measured using two widely used indicators—inverted generational distance (IGD) and hypervolume (HV). Experiment results demonstrate that NSGA-II-BnF exhibits superior performance to most of the algorithms on all test problems.

A simple algebraic interface capturing scheme using hyperbolic tangent function

2005 ◽  
Vol 48 (9) ◽  
pp. 1023-1040 ◽  
Author(s):  
F. Xiao ◽  
Y. Honma ◽  
T. Kono
Keyword(s):  
Hyperbolic Tangent ◽  
Hyperbolic Tangent Function ◽  
Interface Capturing ◽  
Tangent Function
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