System identification: theory for the user
System identification: theory for the user
Fuzzy self-organizing controller and its application for dynamic processes
Fuzzy Sets and Systems - Fuzzy Control
An introduction to fuzzy control
An introduction to fuzzy control
Digital control system analysis and design (3rd ed.)
Digital control system analysis and design (3rd ed.)
Genetic Algorithms
ANN-based estimator for distillation using Levenberg-Marquardt approach
Engineering Applications of Artificial Intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Stability indices for a self-organizing fuzzy controlled robot: A case study
Engineering Applications of Artificial Intelligence
Radial basis function networks with hybrid learning for system identification with outliers
Applied Soft Computing
Radial basis function neural network-based adaptive critic control of induction motors
Applied Soft Computing
Fuzzy enabled congestion control for Differentiated Services Networks
Applied Soft Computing
Chaos control of new Mathieu-van der Pol systems by fuzzy logic constant controllers
Applied Soft Computing
An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Global Synchronization in an Array of Delayed Neural Networks With Hybrid Coupling
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Paper: A linguistic self-organizing process controller
Automatica (Journal of IFAC)
Design of a grey-prediction self-organizing fuzzy controller for active suspension systems
Applied Soft Computing
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The maintenance of a constant cutting force operation via control of the turning systems can increase the metal removal rate (MRR) and tool life. However, an increase in cutting depth reduces feed rate during the constant cutting force operation, resulting in lower productivity for the machine tool. To eliminate the problem, this study proposed an MRR scheme to assist a turning system in constructing a constant turning force system with fixed MRR. This study also presented a self-organizing fuzzy controller (SOFC) for manipulating such a system to maintain a constant turning force operation and improve the productivity of the machine tool. Nevertheless, it is difficult to select a suitable learning rate and an appropriate weighting distribution for the design of an SOFC. To overcome the difficulty, this study developed a hybrid self-organizing fuzzy and radial basis-function neural-network controller (HSFRBNC) for such turning systems. The HSFRBNC uses a radial basis function neural-network to adjust in real time the learning rate and the weighting distribution parameters of the SOFC to appropriate values, rather than obtaining the parameters by trial and error. This strategy solves the problem of determining appropriate parameters for designing an SOFC. Simulation results showed that the HSFRBNC achieved better control performance than the SOFC when it came to the control of a constant turning force system with fixed MRR.