![]() ![]() ![]() Optimizer is a handy yet powerful tool that allows you full control over unnecessary system features. It is due to some default features and capabilities that are unnecessary and take up space, affecting the system’s smooth running. It might sound like a surprise to you, but the truth is, having fresh OS installations doesn’t make or guarantee the optimal performance of a computer system. The ANIO method has an evident potential for addressing the problem of optimization in motor control.Enhance your computer’s performance by tweaking Windows configuration, privacy settings, and security, and clean your storage using the open-source Optimizer. The cost functions obtained with the ANIO method yielded more accurate results than other optimization methods. The cost functions were found to be quadratic with nonzero linear terms. The unknown cost function was reconstructed successfully for each performer, as well as for the group data. The latter plane was determined using the ANIO method. ![]() In agreement with the Lagrange principle for the inverse optimization, the plane of experimental observations was close to the plane resulting from the direct optimization. Because the constraints in each trial were different, such a propensity is a manifestation of a neural mechanism (not the task mechanics). The experimental data recorded from 80 trials showed a tendency to lie on a 2-dimensional hyperplane in the 4-dimensional finger-force space. In the experiment, subjects (n = 8) grasped an instrumented handle and maintained it at rest in the air with various external torques, loads, and target grasping forces applied to the object. ![]() The core of the ANIO method is the Theorem of Uniqueness that specifies conditions for unique (with some restrictions) estimation of the objective functions. The cost function was determined from experimental data by applying the recently developed Analytical Inverse Optimization (ANIO) method (Terekhov et al. The goal of the research is to reconstruct the unknown cost (objective) function(s) presumably used by the neural controller for sharing the total force among individual fingers in multifinger prehension. ![]()
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