Technical Note: \cal Q-Learning
Machine Learning
The interpolation capabilities of the binary CMAC
Neural Networks
Learning Convergence of CMAC Algorithm
Neural Processing Letters
A Modified CMAC Algorithm Based on Credit Assignment
Neural Processing Letters
Self-Organizing Gaussian Fuzzy CMAC with Truth Value Restriction
ICITA '05 Proceedings of the Third International Conference on Information Technology and Applications (ICITA'05) Volume 2 - Volume 02
Cybernetics and Systems Analysis
Design and analysis of direct-action CMAC PID controller
Neurocomputing
Single-input CMAC control system
Neurocomputing
Autonomous biped gait pattern based on Fuzzy-CMAC neural networks
Integrated Computer-Aided Engineering
Nonlinear Systems Identification via Two Types of Recurrent Fuzzy CMAC
Neural Processing Letters
Design of intelligent power controller for DC–DC converters using CMAC neural network
Neural Computing and Applications
Self-organizing CMAC control for a class of MIMO uncertain nonlinear systems
IEEE Transactions on Neural Networks
Fuzzy CMAC with incremental Bayesian Ying-Yang learning and dynamic rule construction
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Gait transition and modulation in a quadruped robot: A brainstem-like modulation approach
Robotics and Autonomous Systems
Optimal design of CMAC neural-network controller for robotmanipulators
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Credit assigned CMAC and its application to online learning robust controllers
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
FCMAC-BYY: Fuzzy CMAC Using Bayesian Ying–Yang Learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An online self-constructing neural fuzzy inference network and its applications
IEEE Transactions on Fuzzy Systems
Hardware implementation of CMAC neural network with reduced storage requirement
IEEE Transactions on Neural Networks
A self-organizing HCMAC neural-network classifier
IEEE Transactions on Neural Networks
Hierarchically Clustered Adaptive Quantization CMAC and Its Learning Convergence
IEEE Transactions on Neural Networks
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The main goal of this paper is to provide a general methodology and a practical approach for the design of gait pattern for biped robotic applications directly usable by researchers and engineers. This approach, which is based on CMAC neural network, is an alternative way in comparison to the traditional Central Pattern Generator. In the proposed method, the CMAC neural networks are used to learn basic motions (e.g. reference gait) and a Fuzzy Inference System allows to merge these reference motions in order to built more complex gaits. The results of our biped robotic applications show how to design a self-adaptive gait pattern according to average velocity and external perturbations.