Preferred design of recurrent neural network architecture using a multiobjective evolutionary algorithm with un-supervised information recruitment: a paradigm for modeling shape memory alloy actuators
DOI10.1007/S11012-014-9894-0zbMath1293.74315OpenAlexW1967271942MaRDI QIDQ2510324
Nasser L. Azad, Ahmad Mozaffari, Alireza Fathi
Publication date: 1 August 2014
Published in: Meccanica (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s11012-014-9894-0
shape memory alloy (SMA)preference-based optimizationself-organizing Pareto based evolutionary algorithm (SOPEA)simultaneous recurrent neural network (SRNN)
Control, switches and devices (``smart materials) in solid mechanics (74M05) Neural networks for/in biological studies, artificial life and related topics (92B20) Experimental work for problems pertaining to mechanics of deformable solids (74-05) Optimization of other properties in solid mechanics (74P10)
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