scholarly journals Approximate reasoning with fuzzy rule interpolation: background and recent advances

2021 ◽  
Author(s):  
Fangyi Li ◽  
Changjing Shang ◽  
Ying Li ◽  
Jing Yang ◽  
Qiang Shen
Keyword(s):  
Fuzzy Inference ◽  
Fuzzy Rule ◽  
Approximate Reasoning ◽  
Rule Based ◽  
Reasoning Systems ◽  
Significant Interest ◽  
Recent Developments ◽  
Rule Bases ◽  
The Given ◽  
Computing Approach

AbstractApproximate reasoning systems facilitate fuzzy inference through activating fuzzy if–then rules in which attribute values are imprecisely described. Fuzzy rule interpolation (FRI) supports such reasoning with sparse rule bases where certain observations may not match any existing fuzzy rules, through manipulation of rules that bear similarity with an unmatched observation. This differs from classical rule-based inference that requires direct pattern matching between observations and the given rules. FRI techniques have been continuously investigated for decades, resulting in various types of approach. Traditionally, it is typically assumed that all antecedent attributes in the rules are of equal significance in deriving the consequents. Recent studies have shown significant interest in developing enhanced FRI mechanisms where the rule antecedent attributes are associated with relative weights, signifying their different importance levels in influencing the generation of the conclusion, thereby improving the interpolation performance. This survey presents a systematic review of both traditional and recently developed FRI methodologies, categorised accordingly into two major groups: FRI with non-weighted rules and FRI with weighted rules. It introduces, and analyses, a range of commonly used representatives chosen from each of the two categories, offering a comprehensive tutorial for this important soft computing approach to rule-based inference. A comparative analysis of different FRI techniques is provided both within each category and between the two, highlighting the main strengths and limitations while applying such FRI mechanisms to different problems. Furthermore, commonly adopted criteria for FRI algorithm evaluation are outlined, and recent developments on weighted FRI methods are presented in a unified pseudo-code form, easing their understanding and facilitating their comparisons.

Fuzzy Rule Interpolation

2011 ◽  
pp. 728-733 ◽  
Author(s):  
Szilveszter Kovács
Keyword(s):  
Fuzzy Inference ◽  
Fuzzy Rule ◽  
Fuzzy Reasoning ◽  
Fuzzy Relation ◽  
Rule Base ◽  
Rule Based ◽  
Fuzzy Rule Base ◽  
Current Observation ◽  
Rule Bases ◽  
Takagi Sugeno

The “fuzzy dot” (or fuzzy relation) representation of fuzzy rules in fuzzy rule based systems, in case of classical fuzzy reasoning methods (e.g. the Zadeh-Mamdani- Larsen Compositional Rule of Inference (CRI) (Zadeh, 1973) (Mamdani, 1975) (Larsen, 1980) or the Takagi - Sugeno fuzzy inference (Sugeno, 1985) (Takagi & Sugeno, 1985)), are assuming the completeness of the fuzzy rule base. If there are some rules missing i.e. the rule base is “sparse”, observations may exist which hit no rule in the rule base and therefore no conclusion can be obtained. One way of handling the “fuzzy dot” knowledge representation in case of sparse fuzzy rule bases is the application of the Fuzzy Rule Interpolation (FRI) methods, where the derivable rules are deliberately missing. Since FRI methods can provide reasonable (interpolated) conclusions even if none of the existing rules fires under the current observation. From the beginning of 1990s numerous FRI methods have been proposed. The main goal of this article is to give a brief but comprehensive introduction to the existing FRI methods.

Fuzzy-Rule Based Adaptive Data Warehouse

2012 ◽  
Vol 3 (1) ◽  
pp. 47-65 ◽  
Author(s):  
Rajdev Tiwari ◽  
Anubhav Tiwari ◽  
Manu Pratap Singh
Keyword(s):  
Decision Making ◽  
Fuzzy Inference ◽  
Knowledge Workers ◽  
Fuzzy Rule ◽  
Data Warehouses ◽  
Large Reservoir ◽  
Inference System ◽  
Rule Based ◽  
Neuro Fuzzy ◽  
New Generation

Data Warehouses (DWs) are aimed to empower the knowledge workers with information and knowledge which helps them in decision making. Technically, the DW is a large reservoir of integrated data that does not provide the intelligence or the knowledge demanded by users. The burden of data analysis and extraction of information and knowledge from integrated data still lies upon the analyst’s shoulder. The overhead of analysts can be taken off by architecting a new generation data warehouses systems those shall be capable of capturing, organizing and representing knowledge along with the data and information in it. This new generation DW may be called as Knowledge Warehouse (KW) shall exhibit decision making capabilities themselves and can also supplement the Decision Support Systems (DSS) in making decisions quickly and effortlessly. This paper proposes and simulates a fuzzy-rule based adaptive knowledge warehouse with capabilities to learn and represent implicit knowledge by means of adaptive neuro fuzzy inference system (ANFIS).

scholarly journals Genetic fuzzy system for prediction of respiratory rate of chicks subject to thermal challenges

2018 ◽  
Vol 22 (6) ◽  
pp. 412-417 ◽  
Author(s):  
Patrícia F. P. Ferraz ◽  
Tadayuki Yanagi Junior ◽  
Yamid F. Hernandez-Julio ◽  
Gabriel A. e S. Ferraz ◽  
Maria A. J. G. Silva ◽  
...  
Keyword(s):  
Respiratory Rate ◽  
Fuzzy Inference System ◽  
Prediction Accuracy ◽  
Fuzzy Inference ◽  
Fuzzy Rule ◽  
System Model ◽  
Inference System ◽  
Rule Based ◽  
Rule Based System ◽  
Genetic Fuzzy System

ABSTRACT The aim of this study was to estimate and compare the respiratory rate (breath min-1) of broiler chicks subjected to different heat intensities and exposure durations for the first week of life using a Fuzzy Inference System and a Genetic Fuzzy Rule Based System. The experiment was conducted in four environmentally controlled wind tunnels and using 210 chicks. The Fuzzy Inference System was structured based on two input variables: duration of thermal exposure (in days) and dry bulb temperature (°C), and the output variable was respiratory rate. The Genetic Fuzzy Rule Based System set the parameters of input and output variables of the Fuzzy Inference System model in order to increase the prediction accuracy of the respiratory rate values. The two systems (Fuzzy Inference System and Genetic Fuzzy Rule Based System) proved to be able to predict the respiratory rate of chicks. The Genetic Fuzzy Rule Based System interacted well with the Fuzzy Inference System model previously developed showing an improvement in the respiratory rate prediction accuracy. The Fuzzy Inference System had mean percentage error of 2.77, and for Fuzzy Inference System and Genetic Fuzzy Rule Based System it was 0.87, thus indicating an improvement in the accuracy of prediction of respiratory rate when using the tool of genetic algorithms.

Introduction to Fuzzy Data Mining Methods

2011 ◽  
pp. 55-95 ◽  
Author(s):  
Balazs Feil ◽  
Janos Abonyi
Keyword(s):  
Data Mining ◽  
Model Performance ◽  
Fuzzy Rule ◽  
Classification Problem ◽  
Data Sets ◽  
Rule Based ◽  
Data Mining Algorithms ◽  
Rule Bases ◽  
Mining Algorithms ◽  
Extraction Model

This chapter aims to give a comprehensive view about the links between fuzzy logic and data mining. It will be shown that knowledge extracted from simple data sets or huge databases can be represented by fuzzy rule-based expert systems. It is highlighted that both model performance and interpretability of the mined fuzzy models are of major importance, and effort is required to keep the resulting rule bases small and comprehensible. Therefore, in the previous years, soft computing based data mining algorithms have been developed for feature selection, feature extraction, model optimization, and model reduction (rule based simplification). Application of these techniques is illustrated using the wine data classification problem. The results illustrate that fuzzy tools can be applied in a synergistic manner through the nine steps of knowledge discovery.

Fuzzy Rule Based Models and Approximate Reasoning

Fuzzy Systems ◽  
1998 ◽  
pp. 91-133 ◽  
Author(s):  
Ronald R. Yager ◽  
Dimitar P. Filev
Keyword(s):  
Fuzzy Rule ◽  
Approximate Reasoning ◽  
Rule Based

scholarly journals Estimation of Machining Sustainability Using Fuzzy Rule-Based System

Materials ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 5473
Author(s):  
Asif Iqbal ◽  
Guolong Zhao ◽  
Quentin Cheok ◽  
Ning He
Keyword(s):  
Manufacturing Process ◽  
Fuzzy Rule ◽  
Approximate Reasoning ◽  
Three Dimensions ◽  
Tool Geometry ◽  
Control Parameters ◽  
Machining Processes ◽  
Rule Based ◽  
Rule Based System ◽  
Process Work

Quantification of a highly qualitative term ‘sustainability’, especially from the perspective of manufacturing, is a contemporary issue. An inference mechanism, based on approximate reasoning, is required to tackle the complexities and uncertainties of the manufacturing domain. The work presents development of a fuzzy rule-based system to quantify sustainability of the most widely utilized manufacturing process: machining. The system incorporates the effects of key control parameters of machining on several sustainability measures, as reported in the literature. The measures are categorized under the three dimensions of sustainability and contribute to the sustainability scores of the respective dimensions with different weightages. The dimensions’ scores are added up in different proportions to obtain the holistic sustainability score of the process. The categories of the control parameters incorporated into the system include type of the process, work material, material hardness, tool substrate and coating, tool geometry, cutting fluids, and cutting parameters. The proposed method yields sustainability scores, ranging between 0 and 100 of machining processes against the given values of their prominent control parameters. Finally, the rule-based system is applied to three different machining processes to obtain the measures of their accomplishment levels regarding economic, environmental, and societal dimensions of sustainability. The sustainability score of each process is then obtained by summing up the three accomplishment levels under the respective weightages of the dimensions. The presented approach holds immense potentials of industrial application as it can conveniently indicate the current sustainability level of a manufacturing process, leading the practitioners to decide on its continuation or improvement.

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.

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 Generating of Fuzzy Rule Bases with Gaussian Parameters Optimized via Fuzzy C-Mean and Ordinary Least Square

2019 ◽  
Vol 8 (4) ◽  
pp. 5787-5794
Keyword(s):  
Fuzzy Inference ◽  
Fuzzy Rule ◽  
Least Square ◽  
Cluster Center ◽  
Inference System ◽  
Ordinary Least Square ◽  
Fcm Clustering ◽  
Spread Parameters ◽  
Rule Bases ◽  
Center Parameter

The fuzzy rule bases has a central role in the fuzzy inference system. Generating of rule bases based on input-output data pairs by using Fuzzy C-Mean clustering (FCM) requires parameters setting of the membership function (mf). In the Gaussian mf, the mean and spread parameters must be determined. The outputs of FCM clustering include the cluster center and the partition matrix of each object. The value of cluster center can be used as the center parameter, but the spread parameter is usually determined as a constant value. The research proposes a method to determine spread parameter of Gaussian mf by using Ordinary Least Square (OLS) approach with using the partition matrix as the membership degree of each object in the cluster. The magnitude of cluster centers (n) of 3, 5, and 7 are considered as FCM input to cluster the weekly price of soybeans in East Java, Indonesia on the period of January 2014 to December 2017. Based on each cluster center, the optimal spread value of a Gaussian mf is obtained via OLS. This research succeeded in getting a spread values that could approach almost perfectly each element of the partition matrix

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