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
Analysis of direct action fuzzy PID controller structures
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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A completely new type of fuzzy logic system will be developed from the existing fuzzy structure and applied to modeling and control of complex processes under incomplete dynamics in the manufacturing industry. Using a unique three-dimensional membership function (fuzz grade, time and probability), the probabilistic processing features can be added into the existing fuzzy configuration to construct a probabilistic fuzzy inference engine. Thus, this developed probabilistic fuzzy logic system (PFLS) is able to learn uncertain information in both fuzzy and stochastic nature. The proposed PFLS will be very suitable to modeling of the complex stochastic process with incomplete dynamics. All the existing learning theories and methods can be directly applied to the proposed PFLS to enhance its learning performance. Integrated into the fuzzy-PID structure, it will turn into a probabilistic fuzzy logic controller for the stochastic control. Successful application of the proposed PLFS to the selected industrial process will have a great impact on both academia and industry.