Rule-based joint fuzzy and probabilistic networks
نویسندگان
چکیده مقاله:
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 proposed method of this paper can take into account both types of uncertainty with a new and different approach. In this method, we avoid fuzzy transformations to probabilities and vice versa, and fuzzy uncertainties and probabilities are considered jointly. For this purpose, in the original graphical model, first, the type of uncertainty of each node is identified, and accordingly two separate fuzzy and probabilistic networks are constructed. In these networks, relations between nodes are expressed in terms of a set of rules. In each network, fuzzy and probabilistic inference is individually constructed and ultimately the values obtained from each network are combined. This method has been tested on a real problem of localization in wireless sensor networks. In this case, a sensor with uncertain location should be able to predict its location from the received power of its adjacent sensors. In the given scenario, 60 sensors with uncertain locations and 121 sensors with a specific location are considered. Meanwhile, the average location error of sensors has been used to evaluate the methods. The simulation results show the efficiency of the proposed method well.
منابع مشابه
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...
متن کاملUsing 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,...
متن کاملRule Extraction from Linguistic Rule Networks and from Fuzzy Neural Networks: Propositional versus Fuzzy Rules
This paper explores different techniques for extracting propositional rules from linguistic rule neural networks and fuzzy rules from fuzzy neural networks. The applicability and suitability of different types of rules to different problems is analyzed. Hierarchical rule structures are considered where the higher the level is the smaller the number of rules which become more vague and more appr...
متن کاملFuzzy rule-based systems
Relations between similarity-based systems, evaluating similarity to some prototypes, and fuzzy rule-based systems, aggregating values of membership functions, are investigated. Similarity measures based on information theory and probabilistic distance functions lead to a new type of membership functions applicable to symbolic data. Fuzzy membership functions on the other hand lead to a new typ...
متن کاملFuzzy Evolutionary Probabilistic Neural Networks
One of the most frequently used models for classification tasks is the Probabilistic Neural Network. Several improvements of the Probabilistic Neural Network have been proposed such as the Evolutionary Probabilistic Neural Network that employs the Particle Swarm Optimization stochastic algorithm for the proper selection of its spread (smoothing) parameters and the prior probabilities. To furthe...
متن کاملمنابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ذخیره در منابع من قبلا به منابع من ذحیره شده{@ msg_add @}
عنوان ژورنال
دوره 17 شماره 3
صفحات 135- 149
تاریخ انتشار 2020-06-01
با دنبال کردن یک ژورنال هنگامی که شماره جدید این ژورنال منتشر می شود به شما از طریق ایمیل اطلاع داده می شود.
میزبانی شده توسط پلتفرم ابری doprax.com
copyright © 2015-2023