“…One of the most innovative models for optimisation is genetic algorithm. GA is a method that stochastically optimises functions and was created based on natural genetics and biological evolution mechanics [117]. These models could be utilized to optimise the performance of a predictive model via assigning the most significant values of different settings.…”
Section: Proposed Optimisation Model Based On Multi Objective Genementioning
“…One of the most innovative models for optimisation is genetic algorithm. GA is a method that stochastically optimises functions and was created based on natural genetics and biological evolution mechanics [117]. These models could be utilized to optimise the performance of a predictive model via assigning the most significant values of different settings.…”
Section: Proposed Optimisation Model Based On Multi Objective Genementioning
In this work a genetic algorithm for ensemble neural network architecture optimization applied to COVID-19 time series prediction is proposed. The main objective of this paper is to show the results of the optimized number of neurons in two hidden layers of an ensemble artificial neural network used for time series prediction using a real genetic algorithm. The time series dataset used in this work is the confirmed and death cases of COVID-19 of 12 states of Mexico (and information about the whole country). Being the COVID-19 the pandemic that has been affecting many lives in Mexico, for this reason, this work seeks to find a prediction for confirmed and death cases in this country.
“…Evolutionary algorithms are powerful regression techniques because of their generality in optimizing both the structure and gain parameters associated with a controller [15]. Generally speaking, they provide an effective alternative search strategy to find optimal solutions in a highdimensional search space [16,17].…”
Section: Introductionmentioning
confidence: 99%
“…They have been successful in many diverse control applications, including system identification, parameter tuning and optimal control. This methodology is a rapidly developing field at the intersection of control engineering and natural sciences [15][16][17][18][19].…”
This paper presents a real-time machine learning control (MLC) of articulated robotic manipulators using artificial bee colony optimization (ABC) algorithm incorporated with fuzzy theory. The modified ABC with dynamic weight is used to optimize the fuzzy structure and fractional order. The fractional parameters, fuzzy membership functions and rule base are determined by means of the ABC computation. This ABC-fuzzy hybrid learning algorithm is applied to real-time MLC of robotic manipulators by including fractional order proportional-integral-derivative (FOPID) control strategy. The MLC's control gain parameters are online tuned via the ABC-fuzzy optimization. With the kinematics analysis of a sixdegree-of-freedom (DOF) articulated arm via reverse coordinates approach, an ABC-fuzzy MLC is developed to achieve motion control. A real-time operating system (RTOS) on a microprocessor collaborates with the ABC-fuzzy MLC to meet critical timing constraint by considering the dynamics of actuators. Finally, the mechatronic design and experimental setup of a six-DOF articulated robotic manipulator are constructed. Experimental results and comparative works are provided to demonstrate the merit of the proposed methods. Compared with the conventional control schemes, the proposed ABC-fuzzy MLC has theoretical and practice significance in term of real-time capability, online parameter tuning, convergent behavior and hybrid MLC. The proposed MLC methodologies are applicable to designing real-time modern controllers in both industry and academia. INDEX TERMS Artificial bee colony optimization, fuzzy theory, machine learning control, robotic arm.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
