A comparison of five algorithms for the training of CMAC memories for learning control systems
Automatica (Journal of IFAC)
Original Contribution: The CMAC and a theorem of Kolmogorov
Neural Networks
The nature of statistical learning theory
The nature of statistical learning theory
Neural Networks for Optimization and Signal Processing
Neural Networks for Optimization and Signal Processing
An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems
Computers and Industrial Engineering
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
Smooth trajectory tracking of three-link robot: a self-organizingCMAC approach
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Credit assigned CMAC and its application to online learning robust controllers
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robust and fast learning for fuzzy cerebellar model articulation controllers
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hybrid approach of selecting hyperparameters of support vector machine for regression
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robust TSK fuzzy modeling for function approximation with outliers
IEEE Transactions on Fuzzy Systems
Hierarchical image coding via cerebellar model arithmetic computers
IEEE Transactions on Image Processing
Learning convergence of CMAC technique
IEEE Transactions on Neural Networks
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For real-world applications, the obtained data are always subject to noise or outliers. The learning mechanism of cerebellar model articulation controller (CMAC), a neurological model, is to imitate the cerebellum of human being. CMAC has an attractive property of learning speed in which a small subset addressed by the input space determines output instantaneously. For fuzzy cerebellar model articulation controller (FCMAC), the concept of fuzzy is incorporated into CMAC to improve the accuracy problem. However, the distributions of errors into the addressed hypercubes may cause unacceptable learning performance for input data with noise or outliers. For robust fuzzy cerebellar model articulation controller (RFCMAC), the robust learning of M-estimator can be embedded into FCMAC to degrade noise or outliers. Meanwhile, support vector machine (SVR) is a machine learning theory based algorithm which has been applied successfully to a number of regression problems when noise or outliers exist. Unfortunately, the practical application of SVR is limited to defining a set of parameters for obtaining admirable performance by the user. In this paper, a robust learning algorithm based on support SVR and RFCMAC is proposed. The proposed algorithm has both the advantage of SVR, the ability to avoid corruption effects, and the advantage of RFCMAC, the ability to obtain attractive properties of learning performance and to increase accurate approximation. Additionally, particle swarm optimization (PSO) is applied to obtain the best parameters setting for SVR. From simulation results, it shows that the proposed algorithm outperforms other algorithms.