RESUMO
This work explores the polynomial fuzzy stabilization for positive systems. The traditional quadratic Lyapunov function and basic stability analysis may not be favourable for stability investigation due to the absence of the positivity property and membership functions. Therefore, a fuzzy co-positive polynomial Lyapunov-Krasovskii (FCPL) function which considers the positivity is proposed firstly through an imperfect premise matching (IPM) approach. Secondly, the symbol transfer technique which takes into account fuzzy membership knowledge relaxes the stability conditions. The number of symbols is reduced by two constraints: (1) the last and next moments of the membership functions of the FCPL function; (2) membership functions of the fuzzy model and the controller. Finally, the polynomial fuzzy controller with symbols is obtained. Two examples are implemented to verify the proposed methods.
RESUMO
The field of retinal prosthesis has been steadily developing over the last two decades. Despite the many obstacles, clinical trials for electronic approaches are in progress and already demonstrating some success. Optogenetic/optoelectronic retinal prosthesis may prove to have even greater capabilities. Although resolutions are now moving beyond recognition of simple shapes, it will nevertheless be poor compared to normal vision. If we define the aim to be to return mobility and natural scene recognition to the patient, it is important to maximize the useful visual information we attempt to transfer. In this paper, we highlight a method to simplify the scene, perform spatial image compression, and then apply spike coding. We then show the potential for translation on standard consumer processors. The algorithms are applicable to all forms of visual prosthesis, but we particularly focus on optogenetic approaches.