FPGA Debugging with MATLAB Using a Rule-Based Inference System

2017 ◽  
pp. 106-117
Author(s):  
Habib Ul Hasan Khan ◽  
Diana Göhringer
Keyword(s):  
Inference System ◽  
Rule Based

Rainfall events prediction using rule-based fuzzy inference system

2011 ◽  
Vol 101 (1-2) ◽  
pp. 228-236 ◽  
Author(s):  
Somia A. Asklany ◽  
Khaled Elhelow ◽  
I.K. Youssef ◽  
M. Abd El-wahab
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Rule Based ◽  
Rainfall Events

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.

scholarly journals Multi-source information fusion model in rule-based Gaussian-shaped fuzzy control inference system incorporating Gaussian density function

2015 ◽  
Vol 29 (6) ◽  
pp. 2335-2344 ◽  
Author(s):  
Zairan Li ◽  
Ting He ◽  
Luying Cao ◽  
Tunhua Wu ◽  
Pamela McCauley ◽  
...  
Keyword(s):  
Fuzzy Control ◽  
Density Function ◽  
Information Fusion ◽  
Source Information ◽  
Fusion Model ◽  
Inference System ◽  
Rule Based ◽  
Gaussian Density

Data Visualization in Educational Datasets Using a Rule-Based Inference System

2014 ◽  
Author(s):  
Aniruddha Desai ◽  
Muaz Mian ◽  
David Hazel ◽  
Ankur Teredesai ◽  
Gregory Benner
Keyword(s):  
Data Visualization ◽  
Inference System ◽  
Rule Based

Rule-based fuzzy inference system for estimating the influent COD/N ratio and ammonia load to a sequencing batch reactor

2006 ◽  
Vol 53 (1) ◽  
pp. 199-207 ◽  
Author(s):  
Y.J. Kim ◽  
H. Bae ◽  
J.H. Ko ◽  
K.M. Poo ◽  
S. Kim ◽  
...  
Keyword(s):  
Fuzzy Inference System ◽  
Sequencing Batch Reactor ◽  
Fuzzy Inference ◽  
Batch Reactor ◽  
Rule Generation ◽  
Decision Tree Algorithm ◽  
Inference System ◽  
Rule Based ◽  
Close Relationship ◽  
Rule Set

A fuzzy inference system using sensor measurements was developed to estimate the influent COD/N ratio and ammonia load. The sensors measured ORP, DO and pH. The sensor profiles had a close relationship with the influent COD/N ratio and ammonia load. To confirm this operational knowledge for constructing a rule set, a correlation analysis was conducted. The results showed that a rule generation method based only on operational knowledge did not generate a sufficiently accurate relationship between sensor measurements and target variables. To compensate for this defect, a decision tree algorithm was used as a standardized method for rule generation. Given a set of inputs, this algorithm was used to determine the output variables. However, the generated rules could not estimate the continuous influent COD/N ratio and ammonia load. Fuzzified rules and the fuzzy inference system were developed to overcome this problem. The fuzzy inference system estimated the influent COD/N ratio and ammonia load quite well. When these results were compared to the results from a predictive polynomial neural network model, the fuzzy inference system was more stable.

scholarly journals Microcontroller Implementation of Rule-based Inference System for Smart Home

2014 ◽  
Vol 8 (6) ◽  
pp. 197-204 ◽  
Author(s):  
BonJae Koo ◽  
Young Soo Park ◽  
SungHyun Yang
Keyword(s):  
Smart Home ◽  
Inference System ◽  
Rule Based
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