Neurofuzzy adaptive modelling and control
Neurofuzzy adaptive modelling and control
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
The neural network model RuleNet and its application to mobile robot navigation
Fuzzy Sets and Systems - Special issue on methods for data analysis in classificatin and control
Learning from History for Behavior-Based Mobile Robots in Non-Stationary Conditions
Machine Learning - Special issue on learning in autonomous robots
Connectionist Learning in Behaviour-Based Mobile Robots: A Survey
Artificial Intelligence Review
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Computational Intelligence: Imitating Life
Computational Intelligence: Imitating Life
Reinforcement Learning in the Multi-Robot Domain
Autonomous Robots
Neurofuzzy Motion Planners for Intelligent Robots
Journal of Intelligent and Robotic Systems
A New Approach to Design Fuzzy Controllers for Mobile Robots Navigation
CIRA '97 Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation
A neuro-fuzzy controller for mobile robot navigation and multirobotconvoying
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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The era of mobile robotics for use in service and field applications is gaining momentum. The need for adaptability becomes self evident in allowing robots to evolve better behaviors to meet overall task criteria. We report the use of neuro-fuzzy learning for teaching mobile robot behaviors, selecting exemplar cases from a potential continuum of behaviors. Proximate active sensing was successfully achieved with infrared in contrast to the usual ultrasonics and viewed the front area of robot movement. The well-knowTi ANFIS architecture has been modified compressing layers to a necessary minimum with weight normalization achieved by using a sigmoidal function. Trapezoidal basis functions (B splines of order 2) with a partition of 1 were used to speed up computation. Reference to previous reinforcement learning results was made in terms of speed of learning and quality of behavior. Even with the limited input information, appropriate learning invariably took place in a reliable manner.