Query Optimization Using Fuzzy Logic in Integrated Database

2016 ◽  
Vol 4 (3) ◽  
pp. 637
Author(s):  
Susana Susana ◽  
Suharjito Suharjito
Keyword(s):  
Fuzzy Logic ◽  
Response Time ◽  
Query Optimization ◽  
Membership Functions ◽  
Inference System ◽  
Query Response Time ◽  
Data Processing Method ◽  
Significant Difference ◽  
Inference Systems ◽  
Logic Inference

<p>Query optimization in integrated database can’t be separated from data processing method.  In order to have faster query response time, a method to optimize queries is required.  One of many methods that can be used for query optimization is using fuzzy logic with Tsukamoto inference system.  Value set on each variable is defined membership functions and Tsukamoto inference system used in determining these rules or the terms of query results, then apply it into query method or query line structure.  The application of fuzzy logic inference systems with Tsukamoto can accelerate query response time, and will have more significant difference when the amount of selected data is greater.</p>

ANFIS for Predicting Surface Roughness in Turning Operation Performed on CNC Lathe

2011 ◽  
Vol 110-116 ◽  
pp. 1793-1798
Author(s):  
M.A. Vinod Kumar
Keyword(s):  
Surface Roughness ◽  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Cutting Parameters ◽  
Membership Functions ◽  
Ic Engine ◽  
Inference System ◽  
Cnc Lathe ◽  
Testing Data ◽  
Inference Systems

For mass production, mainly automation is used, in which cutting parameters are set to obtain required surface roughness. The parts like IC Engine piston, cylinders require very smooth surface finish. The same is the case of sleeves, collets etc., of machine parts. These are made by automatic machining operations. To get approximate value of required surface roughness, the cutting parameters that are to be set with help of Adaptive Neuro Fuzzy Inference System (ANFIS) that is designed by using Fuzzy Logic Toolbox. The Fuzzy Logic Toolbox is a collection of functions built on the MATLAB numeric computing environment. It provides tools to create and edit fuzzy inference systems (FIS) within the framework of MATLAB. ANFIS constructs a relation between given parameters (input data and output data), when it is trained with experimentally predetermined values. It consists of different functions, of which bell and triangular membership functions are used for our purpose. The comparison of accuracy of predicted values for both membership functions are performed using testing data. The training and testing data was obtained performing operation on CNC lathe for 50 work pieces of which 40 were used for training ANFIS and the remaining 10 were used for comparing the accuracy of both Bell and Triangular membership functions. The detailed analysis and procedure is presented.

scholarly journals Underground Risk Index Assessment and Prediction Using a Simplified Hierarchical Fuzzy Logic Model and Kalman Filter

Processes ◽  
2018 ◽  
Vol 6 (8) ◽  
pp. 103 ◽  
Author(s):  
Muhammad Fayaz ◽  
Israr Ullah ◽  
Do-Hyeun Kim
Keyword(s):  
Fuzzy Logic ◽  
Kalman Filter ◽  
Fuzzy Inference ◽  
Risk Index ◽  
Significant Risk ◽  
Assessment Method ◽  
Logic Model ◽  
Membership Functions ◽  
Inference System ◽  
Fuzzy Logic Model

Normally, most of the accidents that occur in underground facilities are not instantaneous; rather, hazards build up gradually behind the scenes and are invisible due to the inherent structure of these facilities. An efficient inference system is highly desirable to monitor these facilities to avoid such accidents beforehand. A fuzzy inference system is a significant risk assessment method, but there are three critical challenges associated with fuzzy inference-based systems, i.e., rules determination, membership functions (MFs) distribution determination, and rules reduction to deal with the problem of dimensionality. In this paper, a simplified hierarchical fuzzy logic (SHFL) model has been suggested to assess underground risk while addressing the associated challenges. For rule determination, two new rule-designing and determination methods are introduced, namely average rules-based (ARB) and max rules-based (MRB). To determine efficient membership functions (MFs), a module named the heuristic-based membership functions allocation (HBMFA) module has been added to the conventional Mamdani fuzzy logic method. For rule reduction, a hierarchical fuzzy logic model with a distinct configuration has been proposed. In the simplified hierarchical fuzzy logic (SHFL) model, we have also tried to minimize rules as well as the number of levels of the hierarchical structure fuzzy logic model. After risk index assessment, the risk index prediction is carried out using a Kalman filter. The prediction of the risk index is significant because it could help caretakers to take preventive measures in time and prevent underground accidents. The results indicate that the suggested technique is an excellent choice for risk index assessment and prediction.

Fuzzy Logic-Based Inference Systems

2015 ◽  
pp. 209-239
Keyword(s):  
Fuzzy Logic ◽  
Knowledge Representation ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Mathematical Formulation ◽  
Cognitive Map ◽  
Fuzzy Cognitive Map ◽  
Adaptive Networks ◽  
Inference System ◽  
Inference Systems

This chapter presents the mathematical formulation of the fuzzy logic-based inference systems, used as means to infer about the response of ill-conditioned systems, based on the field knowledge representation in the fuzzy world. Particular approaches are explored, e.g., Fuzzy Inference System (FIS), Adaptive Networks-based FIS (ANFIS), Intuitionistic FIS (IFIS) and Fuzzy Cognitive Map (FCM), surfacing their potentialities in modeling applications, such as those in the field of learning, examined in the chapters of Part III that follow.

scholarly journals Sustainability Ranking of Desalination Plants Using Mamdani Fuzzy Logic Inference Systems

2020 ◽  
Vol 12 (2) ◽  
pp. 631 ◽  
Author(s):  
Rabee Rustum ◽  
Anu Mary John Kurichiyanil ◽  
Shaun Forrest ◽  
Corrado Sommariva ◽  
Adebayo J. Adeloye ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
United Arab Emirates ◽  
Water Desalination ◽  
Fuzzy Logic Inference ◽  
Sustainability Analysis ◽  
Sustainability Research ◽  
Using Data ◽  
Inference Systems ◽  
Desalination Plants ◽  
Logic Inference

As water desalination continues to expand globally, desalination plants are continually under pressure to meet the requirements of sustainable development. However, the majority of desalination sustainability research has focused on new desalination projects, with limited research on sustainability performance of existing desalination plants. This is particularly important while considering countries with limited resources for freshwater such as the United Arab Emirates (UAE) as it is heavily reliant on existing desalination infrastructure. In this regard, the current research deals with the sustainability analysis of desalination processes using a generic sustainability ranking framework based on Mamdani Fuzzy Logic Inference Systems. The fuzzy-based models were validated using data from two typical desalination plants in the UAE. The promising results obtained from the fuzzy ranking framework suggest this more in-depth sustainability analysis should be beneficial due to its flexibility and adaptability in meeting the requirements of desalination sustainability.

scholarly journals Application of Soft Computing Models with Input Vectors of Snow Cover Area in Addition to Hydro-Climatic Data to Predict the Sediment Loads

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1481 ◽  
Author(s):  
Waqas Hussan ◽  
Muhammad Khurram Shahzad ◽  
Frank Seidel ◽  
Franz Nestmann
Keyword(s):  
Fuzzy Logic ◽  
Snow Cover ◽  
Sediment Load ◽  
Snow Cover Area ◽  
Inference System ◽  
Sediment Yields ◽  
Fuzzy Logic Inference ◽  
Sediment Loads ◽  
Neuro Fuzzy ◽  
Logic Inference

The accurate estimate of sediment load is important for management of the river ecosystem, designing of water infrastructures, and planning of reservoir operations. The direct measurement of sediment is the most credible method to estimate the sediments. However, this requires a lot of time and resources. Because of these two constraints, most often, it is not possible to continuously measure the daily sediments for most of the gauging sites. Nowadays, data-based sediment prediction models are famous for bridging the data gaps in the estimation of sediment loads. In data-driven sediment predictions models, the selection of input vectors is critical in determining the best structure of models for the accurate estimation of sediment yields. In this study, time series inputs of snow cover area, basin effective rainfall, mean basin average temperature, and mean basin evapotranspiration in addition to the flows were assessed for the prediction of sediment loads. The input vectors were assessed with artificial neural network (ANN), adaptive neuro-fuzzy logic inference system with grid partition (ANFIS-GP), adaptive neuro-fuzzy logic inference system with subtractive clustering (ANFIS-SC), adaptive neuro-fuzzy logic inference system with fuzzy c-means clustering (ANFIS-FCM), multiple adaptive regression splines (MARS), and sediment rating curve (SRC) models for the Gilgit River, the tributary of the Indus River in Pakistan. The comparison of different input vectors showed improvements in the prediction of sediments by using the snow cover area in addition to flows, effective rainfall, temperature, and evapotranspiration. Overall, the ANN model performed better than all other models. However, as regards sediment load peak time series, the sediment loads predicted using the ANN, ANFIS-FCM, and MARS models were found to be closer to the measured sediment loads. The ANFIS-FCM performed better in the estimation of peak sediment yields with a relative accuracy of 81.31% in comparison to the ANN and MARS models with 80.17% and 80.16% of relative accuracies, respectively. The developed multiple linear regression equation of all models show an R2 value of 0.85 and 0.74 during the training and testing period, respectively.

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