PID Control and Fuzzy Logic System to the Obstacle Avoidance in an Autonomous Robot

2021 ◽  
Author(s):  
Dionicio Meza Solano ◽  
Raul Eusebio Grande ◽  
Mariana N. Ibarra Bonilla
Keyword(s):  
Fuzzy Logic ◽  
Obstacle Avoidance ◽  
Pid Control ◽  
Fuzzy Logic System ◽  
Autonomous Robot ◽  
Logic System

A New Hardware Architecture for Fuzzy Logic System Acceleration

2016 ◽  
Vol 12 (2) ◽  
pp. 188-197
Author(s):  
A yahoo.com ◽  
Aumalhuda Gani Abood aumalhuda ◽  
A comp ◽  
Dr. Mohammed A. Jodha ◽  
Dr. Majid A. Alwan
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic System ◽  
Hardware Architecture ◽  
Logic System

Penerapan Logika Samar dalam Peramalan Data Runtun Waktu

2011 ◽  
Vol 3 (2) ◽  
pp. 11-15
Author(s):  
Seng Hansun
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Time Series Analysis ◽  
Fuzzy Sets ◽  
Fuzzy System ◽  
Time Series Data ◽  
Fuzzy Logic System ◽  
Series Data ◽  
Series Analysis ◽  
Logic System

Recently, there are so many soft computing methods been used in time series analysis. One of these methods is fuzzy logic system. In this paper, we will try to implement fuzzy logic system to predict a non-stationary time series data. The data we use here is Mackey-Glass chaotic time series. We also use MATLAB software to predict the time series data, which have been divided into four groups of input-output pairs. These groups then will be used as the input variables of the fuzzy logic system. There are two scenarios been used in this paper, first is by using seven fuzzy sets, and second is by using fifteen fuzzy sets. The result shows that the fuzzy system with fifteen fuzzy sets give a better forecasting result than the fuzzy system with seven fuzzy sets. Index Terms—forecasting, fuzzy logic, Mackey-Glass chaotic, MATLAB, time series analysis

IMPROVING EIGENSPACE-BASED FUZZY LOGIC SYSTEM USING A LINEAR INTERPOLATION SCHEME FOR SPEECH PATTERN RECOGNITION

2013 ◽  
Vol 37 (3) ◽  
pp. 611-620
Author(s):  
Ing-Jr Ding ◽  
Chih-Ta Yen
Keyword(s):  
Pattern Recognition ◽  
Fuzzy Logic ◽  
Recognition Performance ◽  
Linear Interpolation ◽  
Fuzzy Logic System ◽  
Interpolation Scheme ◽  
Speech Pattern ◽  
Eigen Value ◽  
Target Speaker ◽  
Logic System

The Eigen-FLS approach using an eigenspace-based scheme for fast fuzzy logic system (FLS) establishments has been attempted successfully in speech pattern recognition. However, speech pattern recognition by Eigen-FLS will still encounter a dissatisfactory recognition performance when the collected data for eigen value calculations of the FLS eigenspace is scarce. To tackle this issue, this paper proposes two improved-versioned Eigen-FLS methods, incremental MLED Eigen-FLS and EigenMLLR-like Eigen-FLS, both of which use a linear interpolation scheme for properly adjusting the target speaker’s Eigen-FLS model derived from an FLS eigenspace. Developed incremental MLED Eigen-FLS and EigenMLLR-like Eigen-FLS are superior to conventional Eigen-FLS especially in the situation of insufficient data from the target speaker.

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