RESULTS AND CHALLENGES OF ARTIFICIAL NEURAL NETWORKS USED FOR
DECISION-MAKING AND CONTROL IN MEDICAL APPLICATIONS Adriana Albu, Page 1 …

As energy demand grows globally, the energy management system (EMS) is becoming
increasingly important. Energy prediction is an essential component in the first step to create …

医疗机器人在过去几十年获得了飞速发展, 根据医疗机器人的功能和用途将医疗机器人分为:
神经外科机器人, 骨科机器人, 腹腔镜机器人, 血管介入机器人, 假肢外骨骼机器人 …

This paper presents model-free sliding mode controllers and Takagi-Sugeno fuzzy
controllers for the flux and conductivity control of Reverse Osmosis Desalination Plants …

This article proposesa control performance standard (CPS)-based fuzzy event-triggered
scheme for load frequency control (LFC) of power systems with a limited communication …

In order to effectively decrease the joint-angular drifts and end-effector position
accumulation errors, a novel adaptive fuzzy recurrent neural network (AFRNN) is proposed …

This article studies the fuzzy adaptive output feedback control design problem for nonstrict
feedback multi-input–multi-output nonlinear systems with full-states prescribed performance …

This paper proposes a hybrid controller that consists of a second order data–driven Model–
Free Control (MFC), also known as intelligent proportional–integral (iPI), and a Takagi …

To improve the energy efficiency of a mobile robot, a novel energy modeling method for
mobile robots is proposed in this paper. The robot can calculate and predict energy …

In this paper a second-order data-driven Active Disturbance Rejection Control (ADRC) is
merged with a proportional-derivative Takagi-Sugeno Fuzzy (PDTSF) logic controller …