Fuzzy entropy and conditioning
Information Sciences: an International Journal
Fuzzy systems theory and its applications
Fuzzy systems theory and its applications
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Fuzzy Systems as Universal Approximators
IEEE Transactions on Computers
Signal processing with alpha-stable distributions and applications
Signal processing with alpha-stable distributions and applications
Fuzzy engineering
Stochastic resonance in noisy threshold neurons
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Probable equivalence, superpower sets, and superconditionals
International Journal of Intelligent Systems
Learning Bayesian Networks
Neural Fuzzy Agents for Profile Learning and Adaptive Object Matching
Presence: Teleoperators and Virtual Environments
Clustering
Modeling gunshot bruises in soft body armor with an adaptive fuzzy system
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement
IEEE Transactions on Fuzzy Systems
The shape of fuzzy sets in adaptive function approximation
IEEE Transactions on Fuzzy Systems
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A fuzzy rule-based system can model prior probabilities in Bayesian inference and thereby approximate posterior probabilities. This fuzzy technique allows users to express prior descriptions in words rather than as closed-form probability density functions. Learning algorithms can tune the expert rules as well as grow them from sample data. The learning laws and closed-form approximations have a tractable form because of the convex-sum structure of additive fuzzy systems. Simulations demonstrate the fuzzy approximation of priors and posteriors for the three most common conjugate priors. An approximate beta prior combines with binomial data to give a new approximate beta posterior. An approximate gamma prior combines with Poisson data to give a new approximate gamma posterior. An approximate normal prior combines with normal data to give a new approximate normal posterior.