A genetic algorithm approach to a neural-network-based inverse kinematics solution of robotic manipulators based on error minimization

Information Sciences

Volumn 222, Issue , 2013, Pages 528-543

  Köker, Raşit ^a,b  

^a SAKARYA UNIVERSITY   (Turkey)

^b INTERNATIONAL UNIVERSITY OF SARAJEVO   (Bosnia and Herzegovina)

Author keywords

6 Degree of freedom robot; Elman networks; Genetic algorithms; Inverse kinematics problem; Neural networks; Robotics

Indexed keywords

ALGEBRAIC APPROACHES; ELMAN NETWORK; ELMAN NEURAL NETWORK; ERROR MINIMIZATION; EVOLUTIONARY TECHNIQUES; FITNESS FUNCTIONS; GENETIC ALGORITHM APPROACH; HYBRID APPROACH; INITIAL POPULATION; INVERSE KINEMATICS PROBLEM; INVERSE KINEMATICS SOLUTIONS; MICROMETER LEVELS; POSITION ERRORS; REDUNDANT ROBOT; ROBOT CONTROLS; ROBOTIC MANIPULATORS; SIGNIFICANT DIGITS; STANFORD; TRAINING SETS;

DIGITAL ARITHMETIC; FLEXIBLE MANIPULATORS; GENETIC ALGORITHMS; INVERSE KINEMATICS; ITERATIVE METHODS; ROBOTICS;

NEURAL NETWORKS;

EID: 84870066342     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2012.07.051     Document Type: Article

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