Mode Choice Behavior Modeling: A Synergy by Hybrid Neural Network and Fuzzy Logic System

2022 ◽  
Author(s):  
Khaled Assi ◽  
Syed Masiur Rahman ◽  
Ibrahim Al-Sghan ◽  
Ayman Hroub ◽  
Nedal Ratrout
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Choice Behavior ◽  
Mode Choice ◽  
Fuzzy Logic System ◽  
Behavior Modeling ◽  
Hybrid Neural Network ◽  
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.

scholarly journals Prediction of COVID-19 Time Series – Case Studies of South Africa and Egypt using Interval Type-2 Fuzzy Logic System

2021 ◽  
Vol 10 (2) ◽  
pp. 627-635
Keyword(s):  
Neural Network ◽  
South Africa ◽  
Fuzzy Logic ◽  
Mean Squared Error ◽  
Fuzzy Logic System ◽  
Percentage Error ◽  
Neural Network Learning ◽  
Interval Type ◽  
Logic System

COVID-19 is a virus known to emanate from Wuhan, China in December 2019. COVID-19 spread widely to nearby countries like Japan and Korea, followed by Europe and America and later to Africa. Particularly, South Africa and Egypt have been worst hit by the virus. Generally, the COVID-19 data is highly uncertain and requires fuzzy logic approaches for the effective handling of these uncertainties. This study therefore presents the prediction of COVID-19 cases in South Africa and Egypt using interval type-2 fuzzy logic system with Takagi-Sugeno-Kang fuzzy inference and neural network learning. The parameters of the model are adapted using gradient descent backpropagation approach. The proposed model is found to outperform type-1 fuzzy logic system and artificial neural network in terms of the root mean squared error, mean absolute percentage error and mean absolute error

scholarly journals Modelling of the Interdependence Between Speed and Traffic Flow Density. A Neuro – Fuzzy Logic Approach

2016 ◽  
Vol 1 (1) ◽  
Author(s):  
Branko Davidović ◽  
Aleksandar Jovanović
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Traffic Flow ◽  
Fuzzy Logic System ◽  
Flow Diagram ◽  
Graphical Form ◽  
Flow Density ◽  
Subtractive Clustering ◽  
Fuzzy Logic Approach ◽  
Logic System

The speed-traffic flow density interdependence diagram has a number of variations, starting with the theoretical model, through various empirical models that were developed and models based on actual research done on traffic flow. The functional interdependence is obtained using the Sugeno fuzzy logic system, where representative values proposed in HCM 2010 have been adopted as parameters of output association functions. Subsequently the neural network is trained based on actual traffic flow data, which by adjusting the association function of the fuzzy logic system yields an output form of the basic traffic flow diagram. It was noticed that this hybrid expert system produces better output results by applying the “subtractive clustering“ method on data that are used for training a neural network. Finally, the model was tested on several input data groups, and the interdependence between speed and traffic flow density is shown in graphical form.

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