Rule Capacity in Fuzzy Boolean Networks

Automatic Implementation and Simulation of Dynamic Qualitative Systems using Fuzzy Boolean Networks and Fuzzy Rule-Based Cognitive Maps

This paper presents the overview of an ongoing project which goal is to obtain and simulate the dynamics of qualitative systems through the combination of the properties of Fuzzy Boolean Networks and Fuzzy Rule Based Cognitive Maps.

Using a Fuzzy Rule-based Algorithm to Improve Routing in MPLS Networks

Today, the use of wireless and intelligent networks are widely used in many fields such as information technology and networking. There are several types of these networks that MPLS networks are one of these types. However, in MPLS networks there are issues and problems in the design and implementation discussion, for example security, throughput, losses, power consumption and so on. Basically,...

INTEGRATED ADAPTIVE FUZZY CLUSTERING (IAFC) NEURAL NETWORKS USING FUZZY LEARNING RULES

The proposed IAFC neural networks have both stability and plasticity because theyuse a control structure similar to that of the ART-1(Adaptive Resonance Theory) neural network.The unsupervised IAFC neural network is the unsupervised neural network which uses the fuzzyleaky learning rule. This fuzzy leaky learning rule controls the updating amounts by fuzzymembership values. The supervised IAFC ...

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...