Kalman filtering: theory and practice
Kalman filtering: theory and practice
Neurofuzzy adaptive modelling and control
Neurofuzzy adaptive modelling and control
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Fuzzy Control Systems Design and Analysis: A Linear Matrix Inequality Approach
Fuzzy Control Systems Design and Analysis: A Linear Matrix Inequality Approach
Multivariate Density Estimation: an SVM Approach
Multivariate Density Estimation: an SVM Approach
Case Study: An Intelligent Decision-Support System
IEEE Intelligent Systems
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
A Fuzzy Discrete Event System Approach to Determining Optimal HIV/AIDS Treatment Regimens
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An intelligent mobile vehicle navigator based on fuzzy logic andreinforcement learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A hybrid clustering and gradient descent approach for fuzzymodeling
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Dynamical optimal training for interval type-2 fuzzy neural network (T2FNN)
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Cumulative distribution functions from Dempster-Shafer belief structures
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A random set description of a possibility measure and its natural extension
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A note on the robust stability of uncertain stochastic fuzzy systems with time-delays
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Optimal tracking design for stochastic fuzzy systems
IEEE Transactions on Fuzzy Systems
A probabilistic fuzzy logic system for modeling and control
IEEE Transactions on Fuzzy Systems
Enhanced Fuzzy System Models With Improved Fuzzy Clustering Algorithm
IEEE Transactions on Fuzzy Systems
A Probabilistic Neural-Fuzzy Learning System for Stochastic Modeling
IEEE Transactions on Fuzzy Systems
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In this paper, a probabilistic fuzzy logic system (PFLS) is discussed for modeling the stochastic and imprecise information. The PFLS uses a 3-dimensional probabilistic fuzzy set to capture the imprecise stochastic information. A unique 3-dimensional probabilistic fuzzy logic is designed to perform rule inference under such imprecise and stochastic environment. When the PFLS and neural networks are integrated in a unified framework, it can further adapt to time varying dynamics so as to improve its modeling performance. The paper briefly reviews this unique development and potential power of probabilistic fuzzy logic system.