Real-Time Nonlinear Solid Mechanics Computations for Fast Inverse Material Parameter Optimization in Cardiac Mechanics

2019 ◽  
Vol 145 (4) ◽  
pp. 04019020
Author(s):  
Ritesh Rao Rama ◽  
Sebastian Skatulla
Keyword(s):  
Real Time ◽  
Parameter Optimization ◽  
Material Parameter ◽  
Solid Mechanics ◽  
Cardiac Mechanics ◽  
Nonlinear Solid Mechanics

scholarly journals Adaptive Balancing of Robots and Mechatronic Systems

2018 ◽  
Author(s):  
Liviu Ciupitu
Keyword(s):  
Exact Solutions ◽  
Real Time ◽  
Solid Mechanics ◽  
Vertical Plane ◽  
Approximate Solutions ◽  
Real Time Control ◽  
Mechatronic Systems ◽  
Static Loads ◽  
Time Control ◽  
Romanian Academy

Present paper is dealing with the adaptive static balancing of robot or other mechatronic arms that are moving in vertical plane and whose static loads are variable, by using counterweights and springs. Some simple passive and approximate solutions are proposed and an example is shown. The active and exact solutions by using adaptive real time control in the case of unknown variation of static loads are simulated on VIPRO platform developed at Institute of Solid Mechanics of Romanian Academy.

Meta-Heuristic Parameter Optimization for ANN and Real-Time Applications of ANN

2022 ◽  
pp. 166-201
Author(s):  
Asha Gowda Karegowda ◽  
Devika G.
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Parameter Optimization ◽  
Heuristic Algorithms ◽  
Search Space ◽  
Optimization Method ◽  
Classification Problems ◽  
Local Optima ◽  
Consistent Solution ◽  
Real Time Applications

Artificial neural networks (ANN) are often more suitable for classification problems. Even then, training of ANN is a surviving challenge task for large and high dimensional natured search space problems. These hitches are more for applications that involves process of fine tuning of ANN control parameters: weights and bias. There is no single search and optimization method that suits the weights and bias of ANN for all the problems. The traditional heuristic approach fails because of their poorer convergence speed and chances of ending up with local optima. In this connection, the meta-heuristic algorithms prove to provide consistent solution for optimizing ANN training parameters. This chapter will provide critics on both heuristics and meta-heuristic existing literature for training neural networks algorithms, applicability, and reliability on parameter optimization. In addition, the real-time applications of ANN will be presented. Finally, future directions to be explored in the field of ANN are presented which will of potential interest for upcoming researchers.

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