NEFCLASSmdash;a neuro-fuzzy approach for the classification of data
SAC '95 Proceedings of the 1995 ACM symposium on Applied computing
Integrating Robotics Research with Undergraduate Education
IEEE Intelligent Systems
Soft Computing and Tools of Intelligent Systems Design: Theory and Applications
Soft Computing and Tools of Intelligent Systems Design: Theory and Applications
Multi-phase sumo maneuver learning
Robotica
Applied Artificial Intelligence
NEFCLASS based extraction of fuzzy rules and classification of risks of low back disorders
Expert Systems with Applications: An International Journal
GA-TSKfnn: Parameters tuning of fuzzy neural network using genetic algorithms
Expert Systems with Applications: An International Journal
Neuro-fuzzy rule generation: survey in soft computing framework
IEEE Transactions on Neural Networks
| Hi-index | 12.08 |
This paper proposes the application of Neuro-Fuzzy (NF) hybrid system for Sumo Robot (SR) control. This robot is frequently designed by engineering students for robotic competition. As the relation between sensors output signals and motors control pulses is highly nonlinear in SR, soft computing techniques can be used to define this nonlinear relation and control of the robot in a competition ring. Application of intelligent methods for SR control not only simplifies robot control and improves robot responses during competition, but also encourages engineering students to use intelligent methods for solving real world's problems. Regarding above rationale, a NF controller for SR control is proposed and implemented. Firstly, a Fuzzy Inference System (FIS) for detecting and tracking of the opponent in the competition ring is developed, which relates sensor output signals to motor control pulses. Secondly, Artificial Neural Networks (ANN) based learning algorithm is used for rule extraction and tuning the FIS parameters. The design approach of the proposed controller is presented in detail, and effectiveness of the controller is demonstrated by hardware implementation and experimental results. The results show that the intelligent control methods can be easily applied in various robot competitions by engineering students.