An Interval Type 2 Fuzzy Logic Framework for Faster Evolutionary Design

2019 ◽  
Vol 16 (12) ◽  
pp. 5140-5148
Author(s):  
Sarabjeet Singh ◽  
Satvir Singh ◽  
Vijay Kumar Banga
Keyword(s):  
Fuzzy Logic ◽  
Graphics Processing Unit ◽  
Noisy Data ◽  
Parallel Execution ◽  
Rule Base ◽  
Processing Unit ◽  
Data Set ◽  
Interval Type ◽  
Graphics Processing

In this paper, a fast processing and efficient framework has been proposed to get an optimum output from a noisy data set of a system by using interval type-2 fuzzy logic system. Further, the concept of GPGPU (General Purpose Computing on Graphics Processing Unit) is used for fast execution of the fuzzy rule base on Graphics Processing Unit (GPU). Application of Whale Optimization Algorithm (WOA) is used to ascertain optimum output from noisy data set. Which is further integrated with Interval Type-2 (IT2) fuzzy logic system and executed on Graphics Processing Unit for faster execution. The proposed framework is also designed for parallel execution using GPU and the results are compared with the serial program execution. Further, it is clearly observed that the parallel execution rule base evolved provide better accuracy in less time. The proposed framework (IT2FLS) has been validated with classical bench mark problem of Mackey Glass Time Series. For non-stationary time-series data with additive gaussian noise has been implemented with proposed framework and with T1 FLS. Further, it is observed that IT2 FLS provides better rule base for noisy data set.

scholarly journals Speedup of Interval Type 2 Fuzzy Logic Systems Based on GPU for Robot Navigation

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Long Thanh Ngo ◽  
Dzung Dinh Nguyen ◽  
Long The Pham ◽  
Cuong Manh Luong
Keyword(s):  
Fuzzy Logic ◽  
High Performance ◽  
Graphics Processing Unit ◽  
Robot Navigation ◽  
Processing Unit ◽  
Fuzzy Logic Systems ◽  
Logic Systems ◽  
Membership Value ◽  
Interval Type

As the number of rules and sample rate for type 2 fuzzy logic systems (T2FLSs) increases, the speed of calculations becomes a problem. The T2FLS has a large membership value of inherent algorithmic parallelism that modern CPU architectures do not exploit. In the T2FLS, many rules and algorithms can be speedup on a graphics processing unit (GPU) as long as the majority of computation a various stages and components are not dependent on each other. This paper demonstrates how to install interval type 2 fuzzy logic systems (IT2-FLSs) on the GPU and experiments for obstacle avoidance behavior of robot navigation. GPU-based calculations are high-performance solution and free up the CPU. The experimental results show that the performance of the GPU is many times faster than CPU.

Lyapunov approach based design of a gain adaptive interval type-2 fuzzy controller for servo systems

2020 ◽  
pp. 1-19
Author(s):  
Ritu Rani De (Maity) ◽  
Rajani K. Mudi ◽  
Chanchal Dey
Keyword(s):  
Fuzzy Logic ◽  
Stability Analysis ◽  
Fuzzy Controller ◽  
Rule Base ◽  
Performance Study ◽  
Servo Systems ◽  
Lyapunov Approach ◽  
The Stability ◽  
Interval Type

This paper focuses on the development of a stable Mamdani type-2 fuzzy logic based controller for automatic control of servo systems. The stability analysis of the fuzzy controller has been done by employing the concept of Lyapunov. The Lyapunov approach results in the derivation of an original stability analysis that can be used for designing the rule base of our proposed online gain adaptive Interval Type-2 Fuzzy Proportional Derivative controller (IT2-GFPD) for servo systems with assured stability. In this approach a Quadratic positive definite Lyapunov function is used and sufficient stability conditions are satisfied by the adaptive type-2 fuzzy logic control system. Illustrative simulation studies with linear and nonlinear models as well as experimental results on a real-time servo system validate the stability and robustness of the developed intelligent IT2-GFPD. A comparative performance study of IT2-GFPD with other controllers in presence of noise and disturbance also proves the superiority of the proposed controller.

scholarly journals Three-Dimensional Fuzzy Control of Ultrasonic Cleaning

2021 ◽  
Vol 15 (3) ◽  
pp. 169-176
Author(s):  
Volodymyr Morkun ◽  
Olha Kravchenko
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Three Dimensional ◽  
Rule Base ◽  
Ultrasonic Cleaning ◽  
Distributed Parameters ◽  
Input And Output ◽  
Proposed Model ◽  
Interval Type

Abstract Consideration of ultrasonic cleaning as a process with distributed parameters enables reduction of power consumption. This approach is based on establishment of control over the process depending on fixed values of ultrasonic responses in set points. The initial intensity of radiators is determined using a three-dimensional (3D) interval type-2 fuzzy logic controller essentially created for processes with distributed parameters, as well as complex expert evaluation of the input data. The interval membership functions for the input and output data consider the space heterogeneity of ultrasonic cleaning. A rule base is formed, which is 2D and not dependent upon the number of input and output parameters. A model illustrating ultrasonic cleaning with a 3D interval type-2 fuzzy logic controller is designed. Comparative analysis of the output parameters of the proposed model and the traditional method indicates an increase in the energy efficiency by 41.17% due to application of only those ultrasonic radiators that are located next to the contamination.

A PSO-Based Framework for Designing Fuzzy Systems from Noisy Data Set

2012 ◽  
pp. 210-228
Author(s):  
Satvir Singh ◽  
J. S. Saini ◽  
Arun Khosla
Keyword(s):  
Noisy Data ◽  
Search Space ◽  
Fuzzy Logic System ◽  
Human Reasoning ◽  
Data Set ◽  
Swarm Optimization ◽  
Design Type ◽  
Interval Type

In most of Fuzzy Logic System (FLS) designs, human reasoning is encoded into programs to make decisions and/or control systems. Designing an optimal FLS is equivalent to an optimization problem, in which efforts are made to locate a point in fitness search-space where the performance is better than that of other locations. The number of parameters to be tuned in designing an FLS is quite large. Also, fitness search space is highly non-linear, deceptive, non-differentiable, and multi-modal in nature. Noisy data, from which to construct the FLS, may make the design problem even more difficult. This chapter presents a framework to design Type-1 (T1) and Interval Type-2 (IT2) FLSs (Liang and Mendel, 2000c, Mendel, 2001, 2007, Mendel et al., 2006) using Particle Swarm Optimization (PSO) (Eberhart and Kennedy, 1995, Kennedy and Eberhart, 1995). This framework includes the use of PSO based Nature Inspired (NI) Toolbox discussed in the chapter titled, “Nature-Inspired Toolbox to Design and Optimize Systems.”

scholarly journals Visualisation of Large-Scale Call-Centre Data

2020 ◽  
Author(s):  
◽  
Dylan G Rees
Keyword(s):  
Large Scale ◽  
Graphics Processing Unit ◽  
Large Data ◽  
Mean Value ◽  
Data Sets ◽  
Call Centre ◽  
Processing Unit ◽  
Data Set ◽  
Domain Experts ◽  
Graphics Processing

The contact centre industry employs 4% of the entire United King-dom and United States’ working population and generates gigabytes of operational data that require analysis, to provide insight and to improve efficiency. This thesis is the result of a collaboration with QPC Limited who provide data collection and analysis products for call centres. They provided a large data-set featuring almost 5 million calls to be analysed. This thesis utilises novel visualisation techniques to create tools for the exploration of the large, complex call centre data-set and to facilitate unique observations into the data.A survey of information visualisation books is presented, provid-ing a thorough background of the field. Following this, a feature-rich application that visualises large call centre data sets using scatterplots that support millions of points is presented. The application utilises both the CPU and GPU acceleration for processing and filtering and is exhibited with millions of call events.This is expanded upon with the use of glyphs to depict agent behaviour in a call centre. A technique is developed to cluster over-lapping glyphs into a single parent glyph dependant on zoom level and a customizable distance metric. This hierarchical glyph repre-sents the mean value of all child agent glyphs, removing overlap and reducing visual clutter. A novel technique for visualising individually tailored glyphs using a Graphics Processing Unit is also presented, and demonstrated rendering over 100,000 glyphs at interactive frame rates. An open-source code example is provided for reproducibility.Finally, a novel interaction and layout method is introduced for improving the scalability of chord diagrams to visualise call transfers. An exploration of sketch-based methods for showing multiple links and direction is made, and a sketch-based brushing technique for filtering is proposed. Feedback from domain experts in the call centre industry is reported for all applications developed.

scholarly journals Urban Matanuska Flood Prediction using Deep Learning with Sentinel-2 Images

2021 ◽  
Author(s):  
Ramasamy Sankar Ram Chellapa ◽  
Santhana Krishnan Rajan ◽  
Golden Julie Eanoch ◽  
Harold Robinson Yesudhas ◽  
Lakshminarayanan Kumaragurubaran ◽  
...  
Keyword(s):  
Graphics Processing Unit ◽  
Classification Model ◽  
Gradient Boosting ◽  
Processing Unit ◽  
Classification Result ◽  
Data Set ◽  
Flood Prediction ◽  
Spectral Bands ◽  
Graphics Processing ◽  
Sentinel 2

Abstract In this paper, we produce a novel raster dataset depending upon the Sentinel-2 satellite. They envelop over thirteen spectral bands. Our novel data set consists of ten classes within a total of 27000 Geo-referenced and labelled images. Gradient Boosting Model (GBM) used to explore this novel dataset in which the overall prediction and accuracy of 97% is obtained from the support of Graphics Processing Unit (GPU) afforded from Google Colaboratory (Colab). The obtained classification result can provide a gateway for numerous earth observation applications. Here, in this paper, we also elaborate on how this classification model might be applied for a conspicuous change in land cover and how it plays an important role in improving the graphical maps.

Collaborative Parallel Hybrid Metaheuristics on Graphics Processing Unit

2015 ◽  
Vol 14 (01) ◽  
pp. 1550002 ◽  
Author(s):  
Vincent Roberge ◽  
Mohammed Tarbouchi ◽  
Francis Okou
Keyword(s):  
Graphics Processing Units ◽  
Graphics Processing Unit ◽  
Memory Systems ◽  
Parallel Execution ◽  
Island Model ◽  
Processing Unit ◽  
Hybrid Metaheuristics ◽  
Parallel Hybrid ◽  
Time Critical ◽  
Graphics Processing

Metaheuristics are nondeterministic optimization algorithms used to solve complex problems for which classic approaches are unsuitable. Despite their effectiveness, metaheuristics require considerable computational power and cannot easily be used in time critical applications. Fortunately, those algorithms are intrinsically parallel and have been implemented on shared memory systems and more recently on graphics processing units (GPUs). In this paper, we present highly efficient parallel implementations of the particle swarm optimization (PSO), the genetic algorithm (GA) and the simulated annealing (SA) algorithm on GPU using CUDA. Our approach exploits the parallelism at the solution level, follows an island model and allows for speedup up to 346× for different benchmark functions. Most importantly, we also present a strategy that uses the generalized island model to integrate multiple metaheuristics into a parallel hybrid solution adapted to the GPU. Our proposed solution uses OpenMP to heavily exploit the concurrent kernel execution feature of recent NVIDIA GPUs, allowing for the parallel execution of the different metaheuristics in an asynchronous manner. Asynchronous hybrid metaheuristics has been developed for multicore CPU, but never for GPU. The speedup offered by the GPU is far superior and key to the optimization of solutions to complex engineering problems.

Sign in / Sign up

Export Citation Format

Share Document