affine arithmetic
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2022 ◽  
Vol 204 ◽  
pp. 107711
Author(s):  
Antonio Pepiciello ◽  
Fabrizio De Caro ◽  
Alfredo Vaccaro ◽  
Sasa Djokic

Author(s):  
John Penaloza Moran ◽  
Julio C. Lopez ◽  
Antonio Padilha Feltrin

2021 ◽  
Vol 15 ◽  
Author(s):  
James Paul Turner ◽  
Thomas Nowotny

Motivated by the challenge of investigating the reproducibility of spiking neural network simulations, we have developed the Arpra library: an open source C library for arbitrary precision range analysis based on the mixed Interval Arithmetic (IA)/Affine Arithmetic (AA) method. Arpra builds on this method by implementing a novel mixed trimmed IA/AA, in which the error terms of AA ranges are minimised using information from IA ranges. Overhead rounding error is minimised by computing intermediate values as extended precision variables using the MPFR library. This optimisation is most useful in cases where the ratio of overhead error to range width is high. Three novel affine term reduction strategies improve memory efficiency by merging affine terms of lesser significance. We also investigate the viability of using mixed trimmed IA/AA and other AA methods for studying reproducibility in unstable spiking neural network simulations.


2021 ◽  
pp. 0309524X2199277
Author(s):  
Hongfen Zhang ◽  
Youchao Zhang

Aiming at the influence of the uncertainty of power system operating parameters such as wind power fluctuation on AC-DC hybrid system, an interval optimal power flow calculation method based on interval and affine arithmetic is proposed in this paper. First, AC and DC interval power flow model is constructed based on the relationship between interval and affine arithmetic, and the uncertainties such as the new energy generation output of the system are expressed as interval variables; static security performance index (PI) is introduced in AC-DC multi-objective optimal power flow objective functions, which take the system’s power generation cost and network loss into account; the Pareto optimal solution set is distributed uniformly in space by using the particle swarm algorithm to solve the interval optimal power flow model. Finally, MATLAB simulation examples are used to verify that the method can optimize the system’s power generation cost, network loss and static safety index while considering wind power fluctuation.


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