Classification of Events in Switch Machines Using Bayes, Fuzzy Logic System and Neural Network

2014 ◽  
pp. 81-91 ◽  
Author(s):  
Eduardo Aguiar ◽  
Fernando Nogueira ◽  
Renan Amaral ◽  
Diego Fabri ◽  
Sérgio Rossignoli ◽  
...  
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Fuzzy Logic System ◽  
Logic System

Navigational path analysis of mobile robots using an adaptive neuro-fuzzy inference system controller in a dynamic environment

2010 ◽  
Vol 224 (6) ◽  
pp. 1369-1381 ◽  
Author(s):  
D R Parhi ◽  
M K Singh
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Mobile Robots ◽  
Fuzzy Inference ◽  
Dynamic Environment ◽  
Fuzzy Logic System ◽  
Inference System ◽  
The Neural Network ◽  
Neuro Fuzzy ◽  
Logic System

This article focuses on the navigational path analysis of mobile robots using the adaptive neuro-fuzzy inference system (ANFIS) in a cluttered dynamic environment. In the ANFIS controller, after the input layer there is a fuzzy layer and the rest of the layers are neural network layers. The adaptive neuro-fuzzy hybrid system combines the advantages of the fuzzy logic system, which deals with explicit knowledge that can be explained and understood, and those of the neural network, which deals with implicit knowledge that can be acquired by learning. The inputs to the fuzzy logic layer include the front obstacle distance, the left obstacle distance, the right obstacle distance, and target steering. A learning algorithm based on the neural network technique has been developed to tune the parameters of fuzzy membership functions, which smooth the trajectory generated by the fuzzy logic system. Using the developed ANFIS controller, the mobile robots are able to avoid static and dynamic obstacles and reach the target successfully in cluttered environments. The experimental results agree well with the simulation results; this proves the authenticity of the theory developed.

Analysis and Modelling of Hierarchical Fuzzy Logic Systems

2011 ◽  
pp. 208-217
Author(s):  
Masoud Mohammadian
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Interest Rates ◽  
Fuzzy Logic System ◽  
New Method ◽  
Design And Development ◽  
Fuzzy Logic Systems ◽  
Neural Network System ◽  
Logic Systems ◽  
Logic System

In this article the design and development of a hierarchical fuzzy logic system is investigated. A new method using an evolutionary algorithm for design of hierarchical fuzzy logic system for prediction and modelling of interest rates in Australia is developed. The hierarchical system is developed to model and predict three months (quarterly) interest rate fluctuations. This research study is unique in the way proposed method is applied to design and development of fuzzy logic systems. The new method proposed determines the number of layer for hierarchical fuzzy logic system. The advantages and disadvantages of using fuzzy logic systems for financial modelling is also considered. Conclusions on the accuracy of prediction using hierarchical fuzzy logic systems compared to a back-propagation neural network system and a hierarchical neural network are reported.

Classification of Short Circuit GMA Welding using Type-1 and Singleton Fuzzy Logic System

2018 ◽  
Author(s):  
Rafaela Abreu Campos ◽  
Renan Piazzaroli Finotti Amaral ◽  
Ivan Fabio Mota de Menezes ◽  
Leonardo Goliatt da Fonseca ◽  
Moises Luiz Lagares ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Gma Welding ◽  
Short Circuit ◽  
Fuzzy Logic System ◽  
Logic System
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