Real-valued genetic algorithms for fuzzy grey prediction system
Fuzzy Sets and Systems
Genetically trained cellular neural networks
Neural Networks
Design of truss-structures for minimum weight using genetic algorithms
Finite Elements in Analysis and Design
Search space boundary extension method in real-coded genetic algorithms
Information Sciences—Informatics and Computer Science: An International Journal - Special issue on evolutionary algorithms
Robot Dynamics and Control
Evolutionary computation and Wright's equation
Theoretical Computer Science - Natural computing
Automatic generation of fuzzy rule-based models from data by genetic algorithms
Information Sciences—Informatics and Computer Science: An International Journal - Special issue on recent advances in soft computing
Application of a breeder genetic algorithm for finite impulse filter optimization
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: FEA 2002
A hybrid decision tree/genetic algorithm method for data mining
Information Sciences: an International Journal - Special issue: Soft computing data mining
Information Sciences—Informatics and Computer Science: An International Journal
The evolutionary learning rule for system identification
Applied Soft Computing
Genetic regulatory network-based symbiotic evolution
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Decoupling control for spatial six-degree-of-freedom electro-hydraulic parallel robot
Robotics and Computer-Integrated Manufacturing
Journal of Intelligent and Robotic Systems
Hi-index | 12.05 |
In this paper, we propose a novel multi-crossover genetic algorithm (GA) to identify the system parameters of a two-link robot. The resulted system model by the proposed GA is then applied to the feedback linearization control such that the two-link robot system can be transferred to a linear model with a nonlinear bounded time-varying uncertainty. To deal with the uncertainty, a sliding mode control approach is designed to achieve the tracking control. Finally, some simulation results are demonstrated to show the utility of our proposed method.