Rough Set-Based Neuro-Fuzzy System

2011 ◽  
pp. 1396-1403
Author(s):  
Kai Keng Ang ◽  
Chai Quek
Keyword(s):  
Rough Set ◽  
Fuzzy System ◽  
Fuzzy Model ◽  
Fuzzy Rules ◽  
Membership Functions ◽  
Linguistic Terms ◽  
Neural Learning ◽  
Neuro Fuzzy ◽  
Fuzzy Neural ◽  
Modeling Data

Neuro-fuzzy hybridization is the oldest and most popular methodology in soft computing (Mitra & Hayashi, 2000). Neuro-fuzzy hybridization is known as Fuzzy Neural Networks, or Neuro-Fuzzy Systems (NFS) in the literature (Lin & Lee, 1996; Mitra & Hayashi, 2000). NFS is capable of abstracting a fuzzy model from given numerical examples using neural learning techniques to formulate accurate predictions on unseen samples. The fuzzy model incorporates the human-like style of fuzzy reasoning through a linguistic model that comprises of if-then fuzzy rules and linguistic terms described by membership functions. Hence, the main strength of NFS in modeling data is universal approximation (Tikk, Kóczy, & Gedeon, 2003) with the ability to solicit interpretable if-then fuzzy rules (Guillaume, 2001). However, modeling data using NFS involves the contradictory requirements of interpretability versus accuracy. Prevailingly, NFS that focused on accuracy employed optimization which resulted in membership functions that derailed from human-interpretable linguistic terms, or employed large number of if-then fuzzy rules on high-dimensional data that exceeded human level interpretation. This article presents a novel hybrid intelligent Rough set-based Neuro-Fuzzy System (RNFS). RNFS synergizes the sound concept of knowledge reduction from rough set theory with NFS. RNFS reinforces the strength of NFS by employing rough set-based techniques to perform attribute and rule reductions, thereby improving the interpretability without compromising the accuracy of the abstracted fuzzy model.

Fuzzy Modeling by Occupancy Degree and Optimal Partition of Projection Using Rough Set Theory

1998 ◽  
Vol 2 (2) ◽  
pp. 54-61
Author(s):  
Youngwan Cho ◽  
◽  
Kichul Lee ◽  
Mignon Park
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Fuzzy System ◽  
Rough Set Theory ◽  
Fuzzy Rule ◽  
Membership Functions ◽  
Linguistic Information ◽  
Input And Output ◽  
Decision Attributes ◽  
Modeling Data

The rough set theory suggested by Pawlak represents the degree of consistency between conditions and decision attributes of data pairs that have no linguistic information. In this paper, by using this representation feature, we define a measure called the occupancy degree that represents the consistency degree of a premise and consequent variables in fuzzy rules describing experimental data pairs. We also propose a method by which we partition the projected data on input space and find an optimal fuzzy rule table and membership functions of input and output variables from data without preliminary linguistic information. We examine the validity of the proposed method by modeling data pairs randomly generated by a fuzzy system.

scholarly journals Driver's Behavior Modeling Using Fuzzy Logic

2010 ◽  
Vol 2010 ◽  
pp. 1-29 ◽  
Author(s):  
Sehraneh Ghaemi ◽  
Sohrab Khanmohammadi ◽  
Mohammadali Tinati
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Fuzzy System ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Fuzzy Rules ◽  
Behavior Modeling ◽  
Linguistic Terms ◽  
Steering Angle ◽  
Hierarchical Fuzzy System

In this study, we propose a hierarchical fuzzy system for human in a driver-vehicle-environment system to model takeover by different drivers. The driver's behavior is affected by the environment. The climate, road and car conditions are included in fuzzy modeling. For obtaining fuzzy rules, experts' opinions are benefited by means of questionnaires on effects of parameters such as climate, road and car conditions on driving capabilities. Also the precision, age and driving individuality are used to model the driver's behavior. Three different positions are considered for driving and decision making. A fuzzy model calledModel Iis presented for modeling the change of steering angle and speed control by considering time distances with existing cars in these three positions, the information about the speed and direction of car, and the steering angle of car. Also we obtained two other models based on fuzzy rules calledModel IIandModel IIIby using Sugeno fuzzy inference.Model IIandModel IIIhave less linguistic terms thanModel Ifor the steering angle and direction of car. The results of three models are compared for a driver who drives based on driving laws.

Development of a Self-Organized Neuro-Fuzzy Model for System Identification

2006 ◽  
Vol 129 (4) ◽  
pp. 507-513 ◽  
Author(s):  
S. M. Yang ◽  
C. J. Chen ◽  
Y. Y. Chang ◽  
Y. Z. Tung
Keyword(s):  
Neural Network ◽  
System Identification ◽  
Nonlinear System Identification ◽  
Fuzzy Model ◽  
Smart Structure ◽  
Fuzzy Rules ◽  
Membership Functions ◽  
Learning Capability ◽  
Self Organized ◽  
Neuro Fuzzy

It has been known that it is difficult to establish a fuzzy logic model with effective fuzzy rules and the associated membership functions. Neural network with its learning capability has been incorporated to make the fuzzy model more adaptive and effective. A self-organized neuro-fuzzy model by integrating the Mamdani fuzzy model and the backpropagation neural network is developed in this paper for system identification. The five-layer network adaptively adjusts the membership functions and dynamically optimizes the fuzzy rules. A benchmark test is applied to validate the model accuracy in nonlinear system identification. Experimental verifications on the dynamics of a composite smart structure and on an acoustics system also demonstrate that the neuro-fuzzy model is superior to the neural network and to an adaptive filter in system identification. The model can be established systematically and is shown to be effective in engineering applications.

Application of T-S Fuzzy Neural Network in the Assessment of River Ecosystem Health

2013 ◽  
Vol 726-731 ◽  
pp. 958-962 ◽  
Author(s):  
Zhen Chun Hao ◽  
Xiao Li Liu ◽  
Qin Ju
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Fuzzy System ◽  
Ecosystem Health ◽  
Fuzzy Neural Network ◽  
Evaluation Model ◽  
Fuzzy Model ◽  
River Ecosystem ◽  
Fuzzy Neural

Healthy river ecosystem has been acknowledged as the object of river management, which is crucial for the sustainable development of cities. Simple and practical evaluation methods with great precision are necessary for the evaluation of river ecosystem health. Fuzzy system has been widely used in evaluation and decision making for its simple reasoning and the adoption of experts knowledge. However, much artificial intervention decreases the precision. Neural network has a strong ability of self-leaning while it is not good at expressing rule-based knowledge. The T-S fuzzy neural network model combines the advantages of fuzzy system and neural network. In this paper, the T-S fuzzy neural network model was used to establish a river ecosystem health evaluation model. Results show that the combination of T-S fuzzy model and neural network eliminates the influences of subjective factors and improve the final precisions efficiently.

scholarly journals An Improved and Adaptive Approach in ANFIS to Predict Knee Diseases

2020 ◽  
Vol 15 (2) ◽  
pp. 22-37 ◽  
Author(s):  
Ranjit Kaur ◽  
Kamaldeep Kaur ◽  
Aditya Khamparia ◽  
Divya Anand
Keyword(s):  
Fuzzy System ◽  
Inflammatory Diseases ◽  
Medical Science ◽  
Research Work ◽  
Membership Functions ◽  
Anfis Model ◽  
Comparative Performance ◽  
Medical Practitioners ◽  
Neuro Fuzzy ◽  
Testing Accuracy

Artificial intelligence is emerging as a persuasive tool in the field of medical science. This research work also primarily focuses on the development of a tool to automate the diagnosis of inflammatory diseases of the knee joint. The tool will also assist the physicians and medical practitioners for diagnosis. The diseases considered for this research under inflammatory category are osteoarthritis, rheumatoid arthritis and osteonecrosis. A five-layer adaptive neuro-fuzzy (ANFIS) architecture was used to model the system. The ANFIS system works by mapping input parameters to the input membership functions, input membership functions are mapped to the rules generated by the ANFIS model which are further mapped to the output membership function. A comparative performance analysis of fuzzy system and ANFIS system is also done and results generated shows that the ANFIS system outperformed fuzzy system in terms of testing accuracy, sensitivity and specificity.

Prediction of flood abnormalities for improved public safety using a modified adaptive neuro-fuzzy inference system

2006 ◽  
Vol 54 (11-12) ◽  
pp. 11-19 ◽  
Author(s):  
M. Aqil ◽  
I. Kita ◽  
A. Yano ◽  
S. Nishiyama
Keyword(s):  
Decision Support ◽  
Process Control ◽  
Decision Support System ◽  
Real Time ◽  
Support System ◽  
Fuzzy System ◽  
Public Safety ◽  
Fuzzy Model ◽  
Neuro Fuzzy ◽  
Flood Monitoring

It is widely accepted that an efficient flood alarm system may significantly improve public safety and mitigate economical damages caused by inundations. In this paper, a modified adaptive neuro-fuzzy system is proposed to modify the traditional neuro-fuzzy model. This new method employs a rule-correction based algorithm to replace the error back propagation algorithm that is employed by the traditional neuro-fuzzy method in backward pass calculation. The final value obtained during the backward pass calculation using the rule-correction algorithm is then considered as a mapping function of the learning mechanism of the modified neuro-fuzzy system. Effectiveness of the proposed identification technique is demonstrated through a simulation study on the flood series of the Citarum River in Indonesia. The first four-year data (1987 to 1990) was used for model training/calibration, while the other remaining data (1991 to 2002) was used for testing the model. The number of antecedent flows that should be included in the input variables was determined by two statistical methods, i.e. autocorrelation and partial autocorrelation between the variables. Performance accuracy of the model was evaluated in terms of two statistical indices, i.e. mean average percentage error and root mean square error. The algorithm was developed in a decision support system environment in order to enable users to process the data. The decision support system is found to be useful due to its interactive nature, flexibility in approach, and evolving graphical features, and can be adopted for any similar situation to predict the streamflow. The main data processing includes gauging station selection, input generation, lead-time selection/generation, and length of prediction. This program enables users to process the flood data, to train/test the model using various input options, and to visualize results. The program code consists of a set of files, which can be modified as well to match other purposes. This program may also serve as a tool for real-time flood monitoring and process control. The results indicate that the modified neuro-fuzzy model applied to the flood prediction seems to have reached encouraging results for the river basin under examination. The comparison of the modified neuro-fuzzy predictions with the observed data was satisfactory, where the error resulted from the testing period was varied between 2.632% and 5.560%. Thus, this program may also serve as a tool for real-time flood monitoring and process control.

scholarly journals Development Multiple Neuro-Fuzzy System Using Back-propagation Algorithm

2013 ◽  
Vol 6 (2) ◽  
pp. 794-804
Author(s):  
Dr. Imad S. Alshawi ◽  
Haider Khalaf Allamy ◽  
Dr. Rafiqul Zaman Khan
Keyword(s):  
Fuzzy System ◽  
Back Propagation ◽  
Fuzzy Model ◽  
Back Propagation Algorithm ◽  
New Approach ◽  
Training Time ◽  
Propagation Algorithm ◽  
Neuro Fuzzy ◽  
Highly Nonlinear ◽  
Input Variables

When fuzzy systems are highly nonlinear or include a large number of input variables, the number of fuzzy rules constituting the underlying model is usually large. Dealing with a large-size fuzzy model may face many practical problems in terms of training time, ease of updating, generalizing ability and interpretability. Multiple Fuzzy System (MFS) is one of effective methods to reduce the number of rules, increase the speed to obtain good results. This paper is therefore proposes another approach call Multiple Neuro-Fuzzy System (MNFS) which can further enhance the performance of the MFS approach. The new approach is used Back-propagation algorithm in the learning process. The performance of the proposed approach evaluates and compares with MFS by three experiments on nonlinear functions. Simulation results demonstrate the effectiveness of the new approach than MFS with regards to enhancement of the accuracy of the results.  

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