scholarly journals An Exploratory Study of Hierarchical Fuzzy Systems Approach in A Recommendation System

2019 ◽  
Vol 14 (2) ◽  
pp. 174-186
Author(s):  
Tajul Rosli Razak ◽  
Iman Hazwam Abd Halim ◽  
Muhammad Nabil Fikri Jamaludin ◽  
Mohammad Hafiz Ismail ◽  
Shukor Sanim Mohd Fauzi
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Systems ◽  
Recommendation System ◽  
Systems Approach ◽  
Fuzzy Logic Systems ◽  
Input Variables ◽  
Hierarchical Fuzzy System ◽  
Hierarchical Fuzzy Systems ◽  
Insight Into ◽  
Logic System

Recommendation system, also known as a recommender system, is a tool to help the user in providing asuggestion of a specific dilemma. Recently, the interest in developing a recommendation system in manyfields has increased. Fuzzy Logic system (FLSs) is one of the approaches that can be used to model therecommendation systems as it can deal with uncertainty and imprecise information. However, one of thefundamental issues in FLS is the problem of the curse of dimensionality. That is, the number of rules inFLSs is increasing exponentially with the number of input variables. One effective way to overcome thisproblem is by using Hierarchical Fuzzy System (HFSs). This paper aims to explore the use of HFSs forRecommendation system. Specifically, we are interested in exploring and comparing the HFS and FLS forthe Career path recommendation system (CPRS) based on four key criteria, namely topology, the numberof rules, the rules structures and interpretability. The findings suggested that the HFS has advantagesover FLS towards improving the interpretability models, in the context of a recommendation systemexample. This study contributes to providing an insight into the development of interpretable HFSs in theRecommendation systems. Keywords: Fuzzy Logic Systems, Hierarchical Fuzzy Systems, Recommendation Systems

Analysis and Application of a Kind of Hierarchical Fuzzy Systems

2011 ◽  
Vol 219-220 ◽  
pp. 1097-1100 ◽  
Author(s):  
Jie Wang ◽  
Xiao Dong Zhu
Keyword(s):  
Structural Analysis ◽  
Fuzzy System ◽  
Fuzzy Systems ◽  
Weight Coefficient ◽  
Oil Drilling ◽  
Analytic Hierarchical Process ◽  
Input Variables ◽  
Hierarchical Fuzzy System ◽  
Hierarchical Fuzzy Systems ◽  
Forewarning System

In this paper a kind of hierarchical fuzzy systems was introduced. The characteristics and structural relation of this hierarchical fuzzy system were analyzed. The sensitivity between the input variables and the output variables and the position of variables in the hierarchical fuzzy system were given according to the importance of variables. The weight coefficient of variables was confirmed applying the methods of analytic hierarchical process (AHP). Then the structural analysis and the weight coefficient were applied to the forewarning system of oil drilling.

scholarly journals On Fuzzy Sequences, Fixed Points and Periodicity in Iterated Fuzzy Maps

2011 ◽  
Vol 6 (4) ◽  
pp. 749 ◽  
Author(s):  
Horia Nicolai Teodorescu
Keyword(s):  
Fuzzy Logic ◽  
Fixed Points ◽  
Fuzzy Logic Control ◽  
Fuzzy Systems ◽  
Fuzzy Logic System ◽  
Fuzzy Logic Systems ◽  
Logic Control ◽  
Logic Systems ◽  
Logic System

I exemplify various elementary cases of fuzzy sequences and results related to the iteration of fuzzy mappings and to fuzzy logic systems (FLS). Several types of fuzzy logic system iterations are exemplified in relationship with oscillations in FLS and with the problem of stability in fuzzy logic control. I establish several conditions for fixed points and periodicity of the iterations based on fuzzy systems.

scholarly journals Model for selecting a route for the transport of hazardous materials using a fuzzy logic system

2021 ◽  
Vol 69 (2) ◽  
pp. 355-390
Author(s):  
Teodora Milošević ◽  
Dragan Pamučar ◽  
Prasenjit Chatterjee
Keyword(s):  
Fuzzy Logic ◽  
Hazardous Materials ◽  
Fuzzy Logic System ◽  
Membership Functions ◽  
Fuzzy Logic Systems ◽  
Input Variables ◽  
Weighting Coefficients ◽  
Consistency Method ◽  
Selection Of ◽  
Logic System

Introduction/purpose: The paper presents a model for the selection of a route for the transport of hazardous materials using fuzzy logic systems, as a type of artificial intelligence systems. The system presented in the paper is a system for assistance in the decisionmaking process of the traffic service authorities when choosing one of several possible routes on a particular path when transporting hazardous materials. Methods: The route evaluation is performed on the basis of five criteria. Each input variable is represented by three membership functions, and the output variable is defined by five membership functions. All rules in a fuzzy logic system are determined by applying the method of weight premise aggregation (ATPP), which allows the formation of a database based on experience and intuition. Based on the number of input variables and the number of their membership functions, the basic base of 243 rules is defined. Three experts from the Ministry of Defense were interviewed to determine the weighting coefficients of the membership functions, and the values of the coefficients were determined using the Full Consistency Method (FUCOM). Results: A user program which enables the practical application of this model has been created for the developed fuzzy logic system. Conclusion: The user platform was developed in the Matlab 2008b software package.

COMPARISON OF DEDUCTION THEOREMS IN DIVERSE LOGIC SYSTEMS

2005 ◽  
Vol 01 (01) ◽  
pp. 65-77 ◽  
Author(s):  
GUO-JUN WANG
Keyword(s):  
Mathematical Logic ◽  
Fuzzy Logic ◽  
Weak Form ◽  
Fuzzy Logic System ◽  
Deduction Theorem ◽  
Fuzzy Logic Systems ◽  
Product Logic ◽  
System Product ◽  
Logic Systems ◽  
Logic System

Deduction theorem and its weak forms in classical mathematical logic system, Łukasiewicz logic system, Gödel logic system, product logic system, and the fuzzy logic system ℒ* are discussed and compared. It is pointed out that the weak form of deduction theorem in ℒ* has a clear structure and can be employed to define the concept of consistency degrees of finite theories. Moreover, it is clarified that the negation operator of Gödel type is too strong and is therefore unsuitable for establishing fuzzy logic systems.

Computational Intelligence for Modelling and Control of Multi-Robot Systems

2011 ◽  
pp. 122-135
Author(s):  
M. Mohammadian
Keyword(s):  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Control Systems ◽  
Fuzzy Systems ◽  
Methodological Approach ◽  
Knowledge Bases ◽  
Fuzzy Logic Systems ◽  
Fuzzy Knowledge Bases ◽  
Robot Systems ◽  
Logic Systems

With increased application of fuzzy logic in complex control systems, there is a need for a structured methodological approach in the development of fuzzy logic systems. Current fuzzy logic systems are developed based on individualistic bases and cannot face the challenge of interacting with other (fuzzy) systems in a dynamic environment. In this chapter a method for development of fuzzy systems that can interact with other (fuzzy) systems is proposed. Specifically a method for designing hierarchical self-learning fuzzy logic control systems based on the integration of genetic algorithms and fuzzy logic to provide an integrated knowledge base for intelligent control of mobile robots for collision-avoidance in a common workspace. The robots are considered as point masses moving in a common work space. Genetic algorithms are employed as an adaptive method for learning the fuzzy rules of the control systems as well as learning, the mapping and interaction between fuzzy knowledge bases of different fuzzy logic systems.

Designing Layers in Hierarchical Fuzzy Logic Systems Using Genetic Algorithms

2011 ◽  
pp. 76-87
Author(s):  
Masoud Mohammadian ◽  
Russel Stonier
Keyword(s):  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Interest Rate ◽  
Fuzzy Logic System ◽  
Good Prediction ◽  
Design And Development ◽  
Fuzzy Logic Systems ◽  
Financial Modelling ◽  
Logic Systems ◽  
Logic System

In this paper the design and development of hierarchical fuzzy logic systems is investigated using genetic algorithms. This research study is unique in the way the proposed method is applied to the design and development of hierarchical fuzzy logic systems. The new method proposed determines the number of layers in the hierarchical fuzzy logic system. The proposed method is then applied to financial modelling and prediction. A hierarchical fuzzy logic system is developed to predict quarterly interest rates in Australia. The advantages and disadvantages of using hierarchical fuzzy logic systems for financial modelling is also considered. Good prediction of quarterly interest rate in Australia is obtained using the above method. The number of fuzzy rules used is reduced dramatically and prediction of interest rate is improved.

scholarly journals Investigation of Critical Gap for Pedestrian Crossing Using Fuzzy Logic System

2020 ◽  
Vol 10 (10) ◽  
pp. 3653 ◽  
Author(s):  
Wafaa Shoukry Saleh ◽  
Maha M A Lashin
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic System ◽  
Transport Infrastructure ◽  
Membership Functions ◽  
Triangular Form ◽  
Critical Gap ◽  
Infrastructure Design ◽  
Input Variables ◽  
Pedestrian Crossing ◽  
Logic System

This paper assesses pedestrian crossing behavior and critical gaps at a two-way midblock crossing location. A critical gap is the shortest gap that a pedestrian accepts when crossing a road. A dataset was collected in 2017 in Edinburgh (UK). The analysis was performed using the fuzzy logic system. The adopted membership function of the fuzzy logic system is of a triangular form since it has a simple and convenient structure. The input variables that are used in the analysis are the number and length of rejected gaps and length of accepted gaps at the crossing location. The output variables are the critical gaps. The results show that assessing critical gap estimation of pedestrians crossing using fuzzy logic is achievable and produces reasonable values that are comparable to values that are reported in the literature. This outcome improves the understanding of pedestrian crossing behavior and could therefore have implications for transport infrastructure design. Further analysis using additional parameters including waiting time and demographic characteristics and alternative forms for membership functions are strongly encouraged.

A comparative study of fuzzy logic systems approach for river discharge prediction

2014 ◽  
Vol 514 ◽  
pp. 85-101 ◽  
Author(s):  
A.W. Jayawardena ◽  
E.D.P. Perera ◽  
Bing Zhu ◽  
J.D. Amarasekara ◽  
V. Vereivalu
Keyword(s):  
Fuzzy Logic ◽  
Comparative Study ◽  
River Discharge ◽  
Systems Approach ◽  
Fuzzy Logic Systems ◽  
Logic Systems

Long lead forecasting of spring peak runoff using Mamdani-type fuzzy logic systems at Hay River, NWT

2015 ◽  
Vol 42 (9) ◽  
pp. 665-674 ◽  
Author(s):  
L. Zhao ◽  
F.E. Hicks ◽  
A. Robinson Fayek
Keyword(s):  
Sensitivity Analysis ◽  
Fuzzy Logic ◽  
Historical Data ◽  
System Development ◽  
System Component ◽  
Fuzzy Logic Systems ◽  
Lead System ◽  
Input Variables ◽  
Peak Runoff ◽  
Error Index

In northern riverside communities, breakup ice jam flooding is an annual threat to properties and human lives. In this study, the peak snowmelt runoff during breakup was assessed as an indicator of breakup flood severity. Due to the sparse network of hydrometeorolocial data in remote northern regions, a Mamdani-type fuzzy logic system (FLS) was developed and tested with the limited historical data. Three input variables were defined from the precipitation, air temperature and daily water level data. All of these variables are known ∼3 to 4 weeks before breakup enabling a long lead-time forecast. The process of system development is demonstrated by a case study of the Town of Hay River, NWT Canada. A series of experiments were designed to select the best system configuration, which also provided a way to conduct a sensitivity analysis for different choices in each system component. It was found that the system shows very good performance on the historical data using the qualitative error index. The results of the sensitivity analysis suggest the system performance is dependent on the choices of fuzzy logic inference operators and defuzzification method. As a long lead system, the short-term meteorological factors that would affect the system output were analyzed and the possible error range was assessed. Preliminary model validation, based on three years of testing, shows promising performance.

Collaborative Control of Multiple Robots Using Genetic Fuzzy Systems Approach

2018 ◽  
Author(s):  
Anoop Sathyan ◽  
Ou Ma
Keyword(s):  
Fuzzy Systems ◽  
Systems Approach ◽  
Target Position ◽  
Multiple Robots ◽  
Fuzzy Logic Systems ◽  
Genetic Fuzzy Systems ◽  
Elastic Strings ◽  
Collaborative Control ◽  
Invaluable Tool ◽  
Extensive Test

This paper introduces a decentralized approach of collaborative control between multiple robots. A dynamic problem is considered to illustrate the effectiveness of this approach. The objective of this problem is to control three robots that are connected to a ball through elastic strings to bring the ball to a pre-defined target position. Since there is no communication between the robots, each robot does not know how the other robots are going to react at any instant. The only information available to the robots are the current and target positions of the ball. Genetic Fuzzy Systems (GFSs) are used to develop controllers for individual robots to tackle this problem. The nonlinearity of fuzzy logic systems coupled with the search capability of Genetic Algorithm (GA) provides an invaluable tool to design controllers for such tasks. The system is first trained through a set of scenarios and then applied to an extensive test set to test the effectiveness of the approach.

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