Add to ORKGIn this paper, we investigate the use of adaptive techniques in the optimiza-tion of navigation of Khepera mobile robot in an unstructured and dynamic environment. We optimize the performance of our simplified fuzzy controller using neural network that utilizes genetic algorithm learning. The adaptation of the system involves the tuning of the control rules thereby trimming the control actions, and adjusting the fuzzy controller output gain. We realised an improved performance in our adaptive neuro-fuzzy controller with genetic training for various implemented behaviours on the robot
In this paper, a genetic-fuzzy approach is developed for solving the motion planning problem of a mo...
AbstractIn this paper, a genetic-fuzzy approach is developed for solving the motion planning problem...
This paper presents an automatic design method for fuzzy systems using genetic algorithms. A flexibl...
In this paper, we investigate the use of adaptive techniques in the optimiza-tion of navigation of K...
In this paper, we investigate the use of adaptive techniques in the optimization of navigation of Kh...
Recently, the mobile robots have great importance in the manufacturing processes. They are widely us...
An autonomous mobile robot operating in an unstructured environment must be able to learn with dynam...
AbstractThis paper presents a learning method which automatically designs fuzzy logic controllers (F...
In this paper an algorithm based on the adaptive neuro-fuzzy controller is provided to enhance the t...
International audienceThis is a study of an application of neuraltechnics to the learning of control...
This work investigates the possibility of using a novel evolutionary based technique as a solution f...
Hybrid neuro-fuzzy controller is one of the techniques that is used as a tool to control a mobile ro...
[[abstract]]In this article, a novel on-line genetic algorithm-based fuzzy-neural sliding mode contr...
Three soft computing paradigms for automated learning in robotic systems are briefly described. The ...
This paper provides an overview on evolutionary learning methods for the automated design and optimi...
In this paper, a genetic-fuzzy approach is developed for solving the motion planning problem of a mo...
AbstractIn this paper, a genetic-fuzzy approach is developed for solving the motion planning problem...
This paper presents an automatic design method for fuzzy systems using genetic algorithms. A flexibl...
In this paper, we investigate the use of adaptive techniques in the optimiza-tion of navigation of K...
In this paper, we investigate the use of adaptive techniques in the optimization of navigation of Kh...
Recently, the mobile robots have great importance in the manufacturing processes. They are widely us...
An autonomous mobile robot operating in an unstructured environment must be able to learn with dynam...
AbstractThis paper presents a learning method which automatically designs fuzzy logic controllers (F...
In this paper an algorithm based on the adaptive neuro-fuzzy controller is provided to enhance the t...
International audienceThis is a study of an application of neuraltechnics to the learning of control...
This work investigates the possibility of using a novel evolutionary based technique as a solution f...
Hybrid neuro-fuzzy controller is one of the techniques that is used as a tool to control a mobile ro...
[[abstract]]In this article, a novel on-line genetic algorithm-based fuzzy-neural sliding mode contr...
Three soft computing paradigms for automated learning in robotic systems are briefly described. The ...
This paper provides an overview on evolutionary learning methods for the automated design and optimi...
In this paper, a genetic-fuzzy approach is developed for solving the motion planning problem of a mo...
AbstractIn this paper, a genetic-fuzzy approach is developed for solving the motion planning problem...
This paper presents an automatic design method for fuzzy systems using genetic algorithms. A flexibl...