Boolean matrix equations for node identification in fuzzy rule based networks

Boolean matrix equations for node identification in fuzzy rule based networks

Rule-based joint fuzzy and probabilistic networks

One of the important challenges in Graphical models is the problem of dealing with the uncertainties in the problem. Among graphical networks, fuzzy cognitive map is only capable of modeling fuzzy uncertainty and the Bayesian network is only capable of modeling probabilistic uncertainty. In many real issues, we are faced with both fuzzy and probabilistic uncertainties. In these cases, the propo...

Rule Capacity in Fuzzy Boolean Networks

Fuzzy Boolean Networks are Boolean networks with nature like characteristics, such as organization of neurons on cards or areas, random individual connections, structured meshes of links between cards. They also share with natural systems some interesting properties: relative noise immunity, capability of approximate reasoning and learning from sets of experiments. An interesting problem relate...

Fuzzy rule-based networks for control

Charles M. Higgins and Rodney M. Goodman Abstract| We present a method for the learning of fuzzy logic membership functions and rules to approximate a numerical function from a set of examples of the function's independent variables and the resulting function value. This method uses a three-step approach to building a complete function approximation system: rst, learning the membership function...

Matrix formulation of fuzzy rule-based systems

In this paper, a matrix formulation of fuzzy rule based systems is introduced. A gradient descent training algorithm for the determination of the unknown parameters can also be expressed in a matrix form for various adaptive fuzzy networks. When converting a rule-based system to the proposed matrix formulation, only three sets of linear/nonlinear equations are required instead of set of rules a...