Special Issue on Advances in Fuzzy Inference and its Related Techniques

2013 ◽  
Vol 17 (1) ◽  
pp. 43-43
Author(s):  
Kiyohiko Uehara
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Fuzzy Inference ◽  
Financial Institution ◽  
Fuzzy Rule ◽  
Large Data ◽  
Industrial Applications ◽  
Editorial Office ◽  
Special Issue ◽  
Inference Systems

Fuzzy inference provides a way to describe system behavior using humanly understandable rules. Based on this advantage, fuzzy inference has been applied in a wide variety of fields, including control, prediction, and pattern recognition. It has also had a corresponding impact on industrial applications. The four articles included in this special issue cover the advances made in fuzzy inference and related techniques. The first paper proposes a method for fuzzy rule interpolation on the basis of the generalized mean. This method makes it possible to perform nonlinear mapping of convex fuzzy sets even with sparse fuzzy rules. The second paper proposes a fuzzy clustering algorithm, landmark fuzzy neighborhood DBSCAN (landmark FN-DBSCAN). This algorithm is quite efficient in the clustering of large data sets, particularly compared to conventional density-based algorithms. Fuzzy clustering can be used to construct fuzzy rule bases. The third paper applies fuzzy inference to ultrasonic human brain imaging based on YURAGI synthesis. In this method, the thickness of bones is calculated effectively from synthesized waves using fuzzy inference. The fourth paper applies adaptive neurofuzzy inference systems (ANFIS) to financial institution failure prediction. These systems function effectively in the handling of the finance data of Thai firms with high nonlinearity. As a guest editor, I really appreciate the efforts of the contributors and reviewers. I am very grateful to staffs in JACIII editorial office for their kind support.

Evaluate Fabric Wrinkle Grade Based on Subtractive Clustering Adaptive Network Fuzzy Inference Systems

2011 ◽  
Vol 332-334 ◽  
pp. 1505-1510
Author(s):  
Xiao Bo Yang
Keyword(s):  
Network Inference ◽  
Clustering Algorithm ◽  
Fuzzy Inference ◽  
Real Data ◽  
Subtractive Clustering ◽  
Fuzzy Inference Systems ◽  
Adaptive Network ◽  
Inference System ◽  
Fuzzy Neural ◽  
Inference Systems

In this paper, a new method of subtractive clustering adaptive network fuzzy inference systems is proposed to assess degree of wrinkle in the fabric. The clustering center can be gotten through subtractive clustering algorithm, which is the base to set up adaptive network inference systems. Firstly, subtractive clustering algorithm is used to confirm the structure of fuzzy neural network, then, fuzzy inference system is used to process pattern recognition. Finally, four kinds of fabric wrinkle feature parameters are used to verify the results on real fabric. The results show the applicability of the proposed method to real data.

Application of Hard C-Means and Fuzzy C-Means in Data Fusion

2012 ◽  
Vol 190-191 ◽  
pp. 265-268
Author(s):  
Ai Hong Tang ◽  
Lian Cai ◽  
You Mei Zhang
Keyword(s):  
Data Fusion ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Large Data ◽  
Fcm Algorithm ◽  
Fuzzy C Means ◽  
Data Similarity ◽  
Fuzzy Clustering Algorithm ◽  
Fuzzy C Means Algorithm

This article describes two kinds of Fuzzy clustering algorithm based on partition,Fuzzy C-means algorithm is on the basis of the hard C-means algorithm, and get a big improvement, making large data similarity as far as possible together. As a result of Simulation, FCM algorithm has more reasonable than HCM method on convergence, data fusion, and so on.

scholarly journals Special Issue on Intelligent Computation in Design and Manufacturing

2018 ◽  
Vol 12 (3) ◽  
pp. 273-274 ◽  
Author(s):  
Roberto Teti ◽  
Pascal Le Masson ◽  
Mitsutaka Matsumoto ◽  
AMM Sharif Ullah
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Process Monitoring ◽  
Manufacturing Process ◽  
Fuzzy Inference ◽  
Special Issue ◽  
Fuzzy Inference Systems ◽  
Design And Manufacturing ◽  
Inference Systems

To solve problems underlying design and manufacturing we often rely on methodologies of computational intelligence such as machine learning, artificial neural networks, fuzzy logic, fuzzy inference systems and smart optimization algorithms. In this Special Issue of the International Journal of Automation Technology, original articles are presented with reference to the engagement of intelligent computation in diverse application areas of design and manufacturing, including manufacturing process monitoring, manufacturing systems management, scheduling, design theory and methodology. The six research papers in this Special Issue propose the use of intelligent computation methodologies to deal with various topics related to manufacturing and design. In particular, the first three papers focus on manufacturing process monitoring with reference to different manufacturing technologies, including tool wear monitoring in drilling of composite materials, sensor monitoring in CNC turning and residual stress prediction in welding. Diverse intelligent approaches such as artificial neural networks and adaptive neuro-fuzzy inference systems are proposed to support manufacturing process monitoring. The fourth paper deals with the manufacturing system level, proposing the employment of a solution algorithm combining metaheuristics and operation simulation for scheduling of production processes. The fifth paper aims at developing tools to guide the manufacturers to manage the technology investment and cost saving target for customer satisfaction based on the application of internet of things. The last paper proposes a methodology to support the introduction of customer requirements in product and service design via a decision support system which exploits artificial intelligence algorithms (machine learning) based on inductive inference, allowing knowledge related to product/service to be mapped, structured and managed to design the service and product semantic model. The editors deeply appreciate all the authors and anonymous reviewers for their effort and excellent work to make this Special Issue unique. We hope that future research on intelligent computation in manufacturing and design will advance manufacturing technology and systems as well as design methodologies.

scholarly journals THE USE OF FUZZY RULE-BASED SYSTEMS IN DETERMINING HORIZONTAL PCO PARAMETERS OF GNSS ANTENNAS

2018 ◽  
Vol 24 (3) ◽  
pp. 367-382
Author(s):  
Nassau de Nogueira Nardez ◽  
Cláudia Pereira Krueger ◽  
Rosana Sueli da Motta Jafelice ◽  
Marcio Augusto Reolon Schmidt
Keyword(s):  
Fuzzy Inference ◽  
Fuzzy Rule ◽  
Training Data ◽  
Data Set ◽  
Rule Based ◽  
The North ◽  
The Difference ◽  
Inference Systems ◽  
Antenna Calibration ◽  
Rule Based Systems

Abstract Knowledge concerning Phase Center Offset (PCO) is an important aspect in the calibration of GNSS antennas and has a direct influence on the quality of high precision positioning. Studies show that there is a correlation between meteorological variables when determining the north (N), east (E) and vertical Up (H) components of PCO. This article presents results for the application of Fuzzy Rule-Based Systems (FRBS) for determining the position of these components. The function Adaptive Neuro-Fuzzy Inference Systems (ANFIS) was used to generate FRBS, with the PCO components as output variables. As input data, the environmental variables such as temperature, relative humidity and precipitation were used; along with variables obtained from the antenna calibration process such as Positional Dilution of Precision and the multipath effect. An FRBS was constructed for each planimetric N and E components from the carriers L1 and L2, using a training data set by means of ANFIS. Once the FRBS were defined, the verification data set was applied, the components obtained by the FRBS and Antenna Calibration Base at the Federal University of Paraná were compared. For planimetric components, the difference was less than 1.00 mm, which shows the applicability of the method for horizontal components.

Special Issue on Service Engineering

2018 ◽  
Vol 12 (4) ◽  
pp. 447-448
Author(s):  
Tamio Arai ◽  
Yasushi Umeda ◽  
Fumio Kojima ◽  
Sadayo Hirata ◽  
Tomohiko Sakao
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Process Monitoring ◽  
Manufacturing Process ◽  
Fuzzy Inference ◽  
Special Issue ◽  
Fuzzy Inference Systems ◽  
Design And Manufacturing ◽  
Inference Systems

To solve problems underlying design and manufacturing we often rely on methodologies of computational intelligence such as machine learning, artificial neural networks, fuzzy logic, fuzzy inference systems and smart optimization algorithms. In this Special Issue of the International Journal of Automation Technology, original articles are presented with reference to the engagement of intelligent computation in diverse application areas of design and manufacturing, including manufacturing process monitoring, manufacturing systems management, scheduling, design theory and methodology. The six research papers in this Special Issue propose the use of intelligent computation methodologies to deal with various topics related to manufacturing and design. In particular, the first three papers focus on manufacturing process monitoring with reference to different manufacturing technologies, including tool wear monitoring in drilling of composite materials, sensor monitoring in CNC turning and residual stress prediction in welding. Diverse intelligent approaches such as artificial neural networks and adaptive neuro-fuzzy inference systems are proposed to support manufacturing process monitoring. The fourth paper deals with the manufacturing system level, proposing the employment of a solution algorithm combining metaheuristics and operation simulation for scheduling of production processes. The fifth paper aims at developing tools to guide the manufacturers to manage the technology investment and cost saving target for customer satisfaction based on the application of internet of things. The last paper proposes a methodology to support the introduction of customer requirements in product and service design via a decision support system which exploits artificial intelligence algorithms (machine learning) based on inductive inference, allowing knowledge related to product/service to be mapped, structured and managed to design the service and product semantic model. The editors deeply appreciate all the authors and anonymous reviewers for their effort and excellent work to make this Special Issue unique. We hope that future research on intelligent computation in manufacturing and design will advance manufacturing technology and systems as well as design methodologies.

Permeability prediction in shaly formations: The fuzzy modeling approach

Geophysics ◽  
2002 ◽  
Vol 67 (3) ◽  
pp. 817-829 ◽  
Author(s):  
Jose Finol ◽  
Xu‐Dong D. Jing
Keyword(s):  
Clustering Algorithm ◽  
Fuzzy Inference ◽  
Fuzzy Rule ◽  
Learning Task ◽  
Well Log ◽  
Rule Based ◽  
Maracaibo Basin ◽  
Core Permeability ◽  
Rule Based Approach

This paper shows how fuzzy rule‐based systems help predict permeability in sedimentary rocks using well‐log responses. The fuzzy rule‐based approach represents a global nonlinear relationship between permeability and a set of input log responses as a smooth concatenation of a finite family of flexible local submodels. The fuzzy inference rules expressing the local input‐output relationships are obtained automatically from a set of observed measurements using a fuzzy clustering algorithm. This approach simplifies the process of constructing fuzzy systems without much computation effort. The benefits of the methodology are demonstrated with a case study in the Lake Maracaibo basin, Venezuela. Special core analyses from three early development wells provide the data for the learning task. Core permeability and well‐log data from a fourth well provide the basis for model validation. Numerical simulation results show that the fuzzy system is an improvement over conventional empirical methods in terms of predictive capability.

scholarly journals Fuzzy modeling applied to optical and surface properties of a ferrocenylsiloxane polyamide solution

2012 ◽  
Vol 10 (1) ◽  
pp. 194-204
Author(s):  
Marius Pislaru ◽  
Silvia Curteanu ◽  
Maria Cazacu
Keyword(s):  
Surface Tension ◽  
Surface Properties ◽  
Self Assembly ◽  
Fuzzy Inference ◽  
A Priori ◽  
Fuzzy Model ◽  
Fuzzy Rule ◽  
Inference System ◽  
Inference Systems ◽  
Available Information

AbstractA fuzzy model was designed to predict changes in surface tension and maximum absorbance due to self-assembly in a DMF solution of poly{1,1′-ferrocene-diamide-[1,3-bis(propylene) tetramethyl-disiloxane} as a function of temperature and concentration. The building of fuzzy rule-based inference systems appears as a grey-box because it allows interpretation of the knowledge contained in the model as well as its improvement with a-priori knowledge. The method provides accurate results and increases the efficiency of utilizing the available information in the model. Small mean squared errors (0.0064 for absorbance and 0.79 for surface tension) and strong correlations between experiment and simulated results (0.93 and 0.97, respectively) were found during model validation. The results showed that it is feasible to apply a Mamdani fuzzy inference system to the estimation of optical and surface properties of a ferrocenylsiloxane polyamide solution.

scholarly journals A FUZZY RULE BASED INFERENCE SYSTEM FOR EARLY DEBT COLLECTION

2018 ◽  
Vol 24 (5) ◽  
pp. 1845-1865 ◽  
Author(s):  
Sezi Cevik Onar ◽  
Basar Oztaysi ◽  
Cengiz Kahraman
Keyword(s):  
Credit Card ◽  
Intelligent Systems ◽  
Data Science ◽  
Fuzzy Inference ◽  
Fuzzy Rule ◽  
Inference System ◽  
Customer Data ◽  
Debt Collection ◽  
Inference Systems ◽  
The Right

Nowadays, unpaid invoices and unpaid credits are becoming more and more common. Large amounts of data regarding these debts are collected and stored by debt collection agencies. Early debt collection processes aim at collecting payments from creditors or debtors before the legal procedure starts. In order to be successful and be able to collect maximum debts, collection agencies need to use their human resources efficiently and communicate with the customers via the most convenient channel that leads to minimum costs. However, achieving these goals need processing, analyzing and evaluating customer data and inferring the right actions instantaneously. In this study, fuzzy inference based intelligent systems are used to empower early debt collection processes using the principles of data science. In the paper, an early debt collection system composed of three different Fuzzy Inference Systems (FIS), one for credit debts, one for credit card debts, and one for invoices, is developed. These systems use different inputs such as amount of loan, wealth of debtor, part history of debtor, amount of other debts, active customer since, credit limit, and criticality to determine the output possibility of repaying the debt. This output is later used to determine the most convenient communication channel and communication activity profile.

scholarly journals FUZZY RULE-BASED MODEL FOR OPTIMAL MANAGEMENT OF MARINE PROTECTED AREAS: A CASE STUDY ON THE MARINE RESERVE CERBÈRE-BANYULS

2017 ◽  
Vol 5 (7) ◽  
pp. 239-251
Author(s):  
Afraa Ghanem ◽  
Ali Zaher
Keyword(s):  
Land Use ◽  
Protected Areas ◽  
Marine Protected Areas ◽  
Fuzzy Inference ◽  
Marine Reserve ◽  
Fuzzy Rule ◽  
Good Efficiency ◽  
Coastal Aquaculture ◽  
Land Use Conflicts ◽  
Inference Systems

Marine spatial planning (MSP) is considered as one of the most appropriate ways to create and manage marine protected areas (MPAs) around the world. However, conservation constraints and the increase in competition for limited space and resources can generate land use conflicts. The purpose of this study is to develop an approach based on fuzzy inference systems FIS in order to solve the problem of land use conflicts in the Marine Reserve Cerbère-Banyuls in France. The advantage of the proposed method is that expert scientific knowledge in coastal aquaculture activities and the GIS data can be incorporated into a geospatial model to create optimal maps for spatial distribution of activities in the MPAs. This method is applied to the reserve Cerbère-Banyuls and it demonstrated a good efficiency.

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