Design and implementation of a Mamdani Fuzzy Inference System on an FPGA using VHDL

Oliveira, Davi Nunes; Henn, Gustavo Alves de Lima; Almeida, Otacílio da Mota

doi:10.1109/nafips.2010.5548190

Cited by 9 publications

(5 citation statements)

References 15 publications

(6 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In most of these investigations, authors determined the precision of the system comparing the output implemented in hardware with output simulated in MATLAB & Fuzzy Logic Tool Box. In [7] the implementation of a Mamdani controller using VHDL (Very High Speed Integrated Circuits Hardware Description Language) encoding and modular programming is described, obtaining an error of 0, 7% in the output. The implementation of a Mamdani controller on an FPGA is described in [8], in this paper it is proposed the control of a multiple input system managing to obtain an error of 0, 8% at the output.…”

Section: Introductionmentioning

confidence: 99%

Efficient Design and Implementation of a Multivariate Takagi-Sugeno Fuzzy Controller on an FPGA

Aguilar

Pérez-Patricio

Camas

et al. 2014

2014 International Conference on Mechatronics, Electronics and Automotive Engineering

View full text Add to dashboard Cite

Abstract-This article describes the design and efficient implementation of a Takagi Sugeno multivariable Fuzzy Logic Controller. The application selected is a temperature and humidity controller for a chicken incubator. This design was elaborated using VHDL applying intermediate simulations in order to check for functional verification of all modules integrating the controller. The created circuit was implemented on FPGA Cyclone II EP2C35F672C6 assembled in breadboard Altera DE2. Precision of the model on FPGA was calculated by contrasting the obtained results with the results of simulation using MATLAB & Fuzzy Logic Tool Box.

show abstract

Section: Introductionmentioning

confidence: 99%

Efficient Design and Implementation of a Multivariate Takagi-Sugeno Fuzzy Controller on an FPGA

Aguilar

Pérez-Patricio

Camas

et al. 2014

2014 International Conference on Mechatronics, Electronics and Automotive Engineering

View full text Add to dashboard Cite

show abstract

“…The development from the scratch or adding some function to the existing software will collide with the fast development of software capabilities in software industry, which increase significantly through time to time as the result of tight competition in the market [19], [27][28]. In the context of awareness, person should have the adaptability as their preparation to fit with occurring changes or unexpected circumstances [21], [30][31][32]. The advantages for using fuzzy logic control (FLC) is because the understandability, flexibility, modelling of non-linear and complex function, development of expert system, collaboration with conventional control techniques and based exclusively on natural language…”

Section: Introductionmentioning

confidence: 99%

Decision Making in the Tea Leaves Diseases Detection Using Mamdani Fuzzy Inference Method

Lubis¹,

Prayudani²,

Lubis³

et al. 2018

IJEECS

View full text Add to dashboard Cite

The tea plants (Camellia Sinensis) are small tree species that use leaves and leaf buds to produce tea harvested through a monoculture system. It is an agriculture practice to cultivate one types of crop or livestock, variety or breed on a farm annually. Moreover, the emergence of pests, pathogens and diseases cause serious damages to tea plants significantly to its productivity and quality to optimum worst. All parts of the tea plant such as leaves, stems, roots, flowers and fruits are exposed to these harm lead to loss of yield 7 until 10% per year. The intensity of these attacks vary greatly on particular climate, the degree slope and the plant material used. Therefore, this study analyzes tea leaves as a common part used in recipes to create unique taste and flavor in tea production, especially in agro-industry. The decision making method used is Fuzzy Mamdani Inference as one of model with functional hierarchy with initial input based on established criteria. Fuzzy logic will provide tolerance to the set of value, so that small changes will not result in significant category differences, only affect the membership level on the variable value. Previous method using probabilities have shown 78% tea leaves have been attacked by category C (Gray Blight) while using Mamdani indicated 86% of tea leaves have been infected. In this case, this result pointed out that Fuzzy Mamdani Inferences have more optimal result compare to the previous method.

show abstract

“…There is a wealth of realizations of the inference schemes with a large number of optimization mechanisms, see (Oliveira et al 2010;Apolloni et al 2016;Alcala et al 2009). In a nutshell, though the inference scheme is realized by determining the activation levels of the individual rules (their condition parts) implied by some A.…”

Section: Introductionmentioning

confidence: 99%

The development of granular rule-based systems: a study in structural model compression

Ahmad

Pedrycz

2016

Granul. Comput.

View full text Add to dashboard Cite

In this study, we develop a comprehensive design process of granular fuzzy rule-based systems. These constructs arise as a result of a structural compression of fuzzy rule-based systems in which a subset of originally existing rules is retained. Because of the reduced subset of the originally existing rules, the remaining rules are made more abstract (general) by expressing their conditions in the form of granular fuzzy sets (such as interval-valued fuzzy sets, rough fuzzy sets, probabilistic fuzzy sets, etc.), hence the name of granular fuzzy rule-based systems emerging during the compression of the rule bases. The design of these systems dwells upon an important mechanism of allocation of information granularity using which the granular fuzzy rules are formed. The underlying optimization consists of two phases: structural (being of combinatorial character in which a subset of rules is selected) and parametric (when the conditions of the selected rules are made granular through an optimal allocation of information granularity). We implement the cooperative particle swarm optimization to solve optimization problem. A number of experimental studies are reported; those include fuzzy rule-based systems.

show abstract

Design and implementation of a Mamdani Fuzzy Inference System on an FPGA using VHDL

Cited by 9 publications

References 15 publications

Efficient Design and Implementation of a Multivariate Takagi-Sugeno Fuzzy Controller on an FPGA

Efficient Design and Implementation of a Multivariate Takagi-Sugeno Fuzzy Controller on an FPGA

Decision Making in the Tea Leaves Diseases Detection Using Mamdani Fuzzy Inference Method

The development of granular rule-based systems: a study in structural model compression

Contact Info

Product

Resources

About