Principles of data mining
Stable Adaptive Neural Network Control
Stable Adaptive Neural Network Control
Information Visualization and Visual Data Mining
IEEE Transactions on Visualization and Computer Graphics
Parallel Processing Applied to the Planning of Hydrothermal Systems
IEEE Transactions on Parallel and Distributed Systems
Linear and Nonlinear Iterative Learning Control (Lecture Notes in Control and Information Sciences, 291)
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Innovations in Machine Learning: Theory and Applications (Studies in Fuzziness and Soft Computing)
Innovations in Machine Learning: Theory and Applications (Studies in Fuzziness and Soft Computing)
Foundations of Fuzzy Control
Multi-Objective Machine Learning (Studies in Computational Intelligence) (Studies in Computational Intelligence)
Planning Algorithms
Machine Learning and Data Mining: Introduction to Principles and Algorithms
Machine Learning and Data Mining: Introduction to Principles and Algorithms
Data-driven fuzzy modeling for Takagi-Sugeno-Kang fuzzy system
Information Sciences: an International Journal
Reinforcement learning and adaptive dynamic programming for feedback control
IEEE Circuits and Systems Magazine
Fuzzy wavelet neural network models for prediction and identification of dynamical systems
IEEE Transactions on Neural Networks
Information Sciences: an International Journal
Combined data-driven and event-driven scheduling technique for fast distributed cosimulation
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Model Construction of Boolean Network via Observed Data
IEEE Transactions on Neural Networks
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Nowadays, with modern sensor technologies, the inputs, outputs and states of the system can be accurately measured. Based on this fact, analyzing the properties of the system, which has unknown mathematical model, directly by using the measured data, has become feasible. In this paper, some data-based methods are proposed for state stability analysis of a class of nonlinear discrete-time systems. We also discuss the problem of finding the domain of attraction of the equilibrium point, using these data-based methods.