Fuzzy expert system for controlling swamp terrain intelligent air-cushion tracked vehicle

Journal of Engineered Fibers and Fabrics

Hossain²,

Choudhury

et al. 2016

The present study is intended to develop an intelligent model for the prediction of color strength of cotton knitted fabrics using fuzzy knowledge based expert system (FKBES). The factors chosen for developing the prediction model are dye concentration, dyeing time and process temperature. Besides, such factors are nonlinear and have mutual interactions among them; so it is not easy to create an exact correlation between the inputs variables and color strength using mathematical or statistical methods. In contrast, artificial neural network and neural-fuzzy models require massive amounts of experimental data for model parameters optimization which are challenging to collect from the dyeing industries. In this context, fuzzy knowledge based expert system is the most efficient modeling tool which performs exceptionally well in a non-linear complex domain with lowest amount of trial data like human experts. In this study, laboratory scale experiments were conducted for three types of cotton knitted fabrics to verify the developed fuzzy model. It was found that actual and predicted values of color strength of the knitted fabrics were in good agreement with each other with less than 5% absolute error.

Section: Structure Of Fuzzy Expert Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fuzzy Knowledge Based Expert System for Prediction of Color Strength of Cotton Knitted Fabrics

Journal of Engineered Fibers and Fabrics

Hossain²,

Choudhury

et al. 2016

“…In this context, the fuzzy logic expert system is the most viable alternative to conventional prediction methods, as fuzzy logic performs remarkably well in non-linear and complex systems with minimum experimental data [5,14]. Conversely, some of the limitations of ANN, mathematical and statistical regression models can be overcome by the fuzzy logic expert system, which can successfully convert the knowledge of a dyer/colorist into a set of expert system rules.…”

Section: Introductionmentioning

confidence: 99%

Color Strength Modeling of Viscose/Lycra Blended Fabrics Using a Fuzzy Logic Approach

Journal of Engineered Fibers and Fabrics

Hossain²,

Choudhury

2015

The aim of this study was to model the color strength of viscose/lycra (95:5) blended knitted fabrics using a fuzzy logic approach where color strength is a function of dye concentration, salt concentration, and alkali concentration. Dye concentration, salt concentration, and alkali concentration are the most important factors affecting color strength of viscose/lycra blended knitted fabrics. Moreover, these factors behave nonlinearly and interact: hence, it is very difficult to develop an exact functional relationship between the input variables and color strength using mathematical models, statistical models, or empirical models. Conversely, artificial neural network models are trained using large amounts of experimental data which is a time consuming process. One possible approach to deal with such a complex process is by using a fuzzy logic expert system (FLES), which perform remarkably well in non-linear and complex systems with minimum experimental data. In this study a laboratory scale experiment was conducted to validate the developed fuzzy model. The model was assessed by analyzing various numerical error criteria. The mean relative error was found to be 3.80%, the correlation coefficient was 0.992, and goodness of fit was 0.986 from the actual and predicted color strengths of the fabrics. The results show that the model developed performed well.

“…This in turn would affect the accuracy of the developed models in the prediction of humidity level. To overcome this issue, researchers explore the use of artificial intelligence such as machine learning, neural network and genetic algorithms, which have been used successfully in different areas 9,10. On the other hand, fuzzy logic expert system (FLES) may play an important role since it uses expert knowledge to predict the particular system, it is flexible and it correctly estimates the unknown values of the modelled data, often with improved performance and it has high-level expression capability 11,12.…”

Section: Introductionmentioning

confidence: 99%

“…To overcome this issue, researchers explore the use of artificial intelligence such as machine learning, neural network and genetic algorithms, which have been used successfully in different areas. 9,10 On the other hand, fuzzy logic expert system (FLES) may play an important role since it uses expert knowledge to predict the particular system, it is flexible and it correctly estimates the unknown values of the modelled data, often with improved performance and it has highlevel expression capability. 11,12 Moreover, fuzzy logic is less used at present time in studies on humidity prediction related to building, industries, laboratories and other environmental system compared with those related to automation applications.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy logic-based model for prediction of building wall humidity level

Indoor and Built Environment

Nazir

Ramesh

et al. 2012

Self Cite

This paper presents the viability of using fuzzy logic expert system for the prediction of building wall’s relative humidity. In comparison with traditional logic model, fuzzy logic is more efficient in linking the multiple units to a single output and is invaluable supplements to classical hard computing techniques. The purpose of this work was to investigate the relationship between wall material composition and climate condition, as well as to evaluate the viability of using fuzzy logic expert system in predicting the building wall’s humidity level. The experimental data was taken at different time of the day for different material composition. A fuzzy logic expert system model based on Mamdani approach was subsequently developed to predict the building wall’s humidity level. Verification of the developed fuzzy logic model was carried out through various numerical error criteria. The study revealed that the developed fuzzy system was able to predict the humidity level in the wall material within high accuracy with relative error of less than the acceptable limits of 10% and a goodness of fit very close to unity.