Performance Based Comparison Between Various Z-N Tuninng PID And Fuzzy Logic PID Controller In Position Control System Of Dc Motor

Performance Based Comparison Between Various Z-N Tuninng PID And Fuzzy Logic PID Controller In Position Control System Of Dc Motor 

G.SUDHA1 Assistant Professor / Electronics & Instrumentation Engineering Department Vivekanandha College of Technology for Women, Tiruchengode. E Mail: kavisudhamariyaa@gmail.com 

DR.R.ANITA2 Professor& Head, Electrical & Electronics Engineering Department IRTT, Erode. E Mail: anitha_irtt@yahoo.co.in 

Abstract

 The objective of this paper is to compare the time specification performance between conventional controller and Fuzzy Logic controller in position control system of a DC motor. The scope of this research is to apply direct control technique in position control system. Two types of controller namely PID and fuzzy logic PID controller will be used to control the output response. This paper was written to reflect on the work done on the implementation of a fuzzy logic PID controller. The fuzzy controller was used to control the position of a motor which can be considered for a general basis in any project design containing logic control. Motor parameters were taken from a datasheet with respect to a real motor and a simulated model was developed using Matlab Simulink Toolbox. The fuzzy control was also designed using the Fuzzy Control Toolbox provided within Matlab, with each rule consisting of fuzzy sets conditioned to provide appropriate response times with regards to the limitations of our chosen motor. The Fuzzy Inference Engine chosen for our control was the Mamdani Minimum Inference engine. The results of the control provided response times suitable for our application. 

Key words: PID, fuzzy logic, position control system, DC motor, Z-N method  

Original Source URL : http://airccse.org/journal/ijsc/papers/3312ijsc05.pdf

http://airccse.org/journal/ijsc/current2012.html

A ROUGH SET BASED FUZZY INFERENCE SYSTEM FOR MINING TEMPORAL MEDICAL DATABASES

A ROUGH SET BASED FUZZY INFERENCE SYSTEM FOR MINING TEMPORAL MEDICAL DATABASES 

U Keerthika1 R Sethukkarasi2 and A Kannan3 1 PG Student, Department of Computer Science and Engineering, R.M.K. Engineering College, Kavaraipettai, Tamil Nadu, India keerthi.umapathy@gmail.com 2 Research Scholar, Department of Information Science and Technology, Anna University, Chennai, Tamil Nadu, India sethumaaran@yahoo.co.in 3 Professor, Department of Information Science and Technology, Anna University, Chennai, Tamil Nadu, India kannan@annauniv.edu 

ABSTRACT 

The main objective of this research work is to construct a Fuzzy Temporal Rule Based Classifier that uses fuzzy rough set and temporal logic in order to mine temporal patterns in medical databases. The lower approximation concepts and fuzzy decision table with the fuzzy features are used to obtain fuzzy decision classes for building the classifier. The goals are pre-processing for feature selection, construction of classifier, and rule induction based on increment rough set approach. The features are selected using Hybrid Genetic Algorithm. Moreover the elementary sets are obtained from lower approximations are categorized into the decision classes. Based on the decision classes a discernibility vector is constructed to define the temporal consistency degree among the objects. Now the Rule Based Classifier is transformed into a temporal rule based fuzzy inference system by incorporating the Allen’s temporal algebra to induce rules. It is proposed to use incremental rough set to update rule induction in dynamic databases. Ultimately these rules are categorized as rules with range values to perform prediction effectively. The efficiency of the approach is compared with other classifiers in order to assess the accuracy of the fuzzy temporal rule based classifier. Experiments have been carried out on the diabetic dataset and the simulation results obtained prove that the proposed temporal rule-based classifier on clinical diabetic dataset stays as an evidence for predicting the severity of the disease and precision in decision support system. 

KEYWORDS 

Fuzzy Rough Sets, Lower approximations, Rule Based Classifier, Allen’s Temporal Algebra  

ORIGINAL SOURCE URL : http://airccse.org/journal/ijsc/papers/3312ijsc04.pdf

http://airccse.org/journal/ijsc/current2012.html

International Journal on Soft Computing ( IJSC )

                                          

International Journal on Soft Computing ( IJSC )

ISSN: 2229 - 6735 [Online] ; 2229 - 7103 [Print]

http://airccse.org/journal/ijsc/ijsc.html

Scope & Topics

Soft computing is likely to play an important role in science and engineering in the future. The successful applications of  soft computing and the rapid growth suggest that the impact of soft computing will be felt increasingly in coming years. Soft Computing encourages the integration of soft computing techniques and tools into both everyday and advanced applications. This journal serves as a platform that fosters new applications for all scientists and engineers engaged in research and development in this fast growing field

Topics considered include but are not limited to:

Fuzzy Systems Neural Networks Machine learning Probabilistic Reasoning Evolutionary Computing Pattern recognition Hybrid intelligent systems, Software agents Morphic Computing Image processing, E-commerce, e-medicine

Rough Sets Symbolic machine learning, Wavelet Signal or Image Processing Vision Recognition Biomedical Engineering Telecommunications Reactive Distributed AI Nano & Micro-systems Data Visualization

Paper Submission

Authors are invited to submit papers for this journal through E.Mail: ijscjournal@airccse.org  or through Submission System. Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal.

Important Dates

• Submission Deadline : December 08, 2018
• Notification                   : January 08, 2019
• Final Manuscript Due : January 16, 2019
• Publication Date          : Determined by the Editor-in-Chief

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NEURAL NETWORK BASED SUPERVISED SELF ORGANIZING MAPS FOR FACE RECOGNITION

NEURAL NETWORK BASED SUPERVISED SELF ORGANIZING MAPS FOR FACE RECOGNITION 

A.S.Raja1 and V. JosephRaj2 

1Research Scholar, Sathyabama University, Jeppiar Nagar, Chennai,Tamil Nadu, India csehod@capeitech.org, 2 Professor, Kamaraj College, Thoothukudi, Tamil Nadu, India v.jose08@gmail.com 

ABSTRACT 

The word biometrics refers to the use of physiological or biological characteristics of human to recognize and verify the identity of an individual. Face is one of the human biometrics for passive identification with uniqueness and stability. In this manuscript we present a new face based biometric system based on neural networks supervised self organizing maps (SOM). We name our method named SOM-F. We show that the proposed SOM-F method improves the performance and robustness of recognition. We apply the proposed method to a variety of datasets and show the results. 

KEYWORDS 

Biometrics, Face, Supervised Self Organizing Maps (SOM). 

ORIGINAL SOURCE URL : http://airccse.org/journal/ijsc/papers/3312ijsc03.pdf

http://airccse.org/journal/ijsc/current2012.html

A COMPARISON OF PARTICLE SWARM OPTIMIZATION AND DIFFERENTIAL EVOLUTION

A COMPARISON OF PARTICLE SWARM OPTIMIZATION AND DIFFERENTIAL EVOLUTION 

Vu Truong Vu 

Ho Chi Minh City University of Transport, Faculty of Civil Engineering No.2, D3 Street, Ward 25, Binh Thanh District, HCMC, Viet Nam vutruongvu@gmail.com 

ABSTRACT 

Two modern optimization methods including Particle Swarm Optimization and Differential Evolution are compared on twelve constrained nonlinear test functions. Generally, the results show that Differential Evolution is better than Particle Swarm Optimization in terms of high-quality solutions, running time and robustness. 

KEYWORDS 

Particle Swarm Optimization, Differential Evolution

ORIGINAL SOURCE URL :http://airccse.org/journal/ijsc/papers/3312ijsc02.pdf

http://airccse.org/journal/ijsc/current2012.html

International Journal on Soft Computing ( IJSC )

International Journal on Soft Computing ( IJSC )

ISSN: 2229 - 6735 [Online] ; 2229 - 7103 [Print]

http://airccse.org/journal/ijsc/ijsc.html 

Contact Us

Here's where you can reach us : ijscjournal@yahoo.com or ijscjournal@airccse.org

TOP 10 NEURAL NETWORKS PAPERS: RECOMMENDED READING - SOFT COMPUTING

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DetermenationThe Porosity of CdS Thin Film by SeedFilling Algorithm

DetermenationThe Porosity of CdS Thin Film by SeedFilling Algorithm 

Azmi Tawfik Alrawi 1, Saad Jasim Mohammed2 

1Department of Physics , University of Anbar, Ramadi, Iraq Dr_azmi_alrawi@yahoo.com 2Department of Physics , University of Anbar, Ramadi, Iraq Sa882003@yahoo.com 

ABSTRACT 

In this paper, we have prepared CdS thin films by chemical spray method on the substrates in (200C°) and then we annealed the samples of thin film by oven heat in the temperature (250-400C°) and we took images of the thin film membranes before and after annealing by a scanning electron microscope (SEM) in magnification (1000X). The acquisition image converted to binary image of multithresholdand then applied image processing where used seed filling algorithm to analysis images and study the porosity on the surfaces of thin films and that the annealing under different temperature works to reduce the ratioand number of pores on the surface of the thin membrane. 

KEYWORDS 

Porosity ; Thin Film ; Seed Filling ; Annealing ; Image processing ; CdS Material ; Threshold.

Original Source URL : http://airccse.org/journal/ijsc/papers/3312ijsc01.pdf

HIGH RESOLUTION MRI BRAIN IMAGE SEGMENTATION TECHNIQUE USING HOLDER EXPONENT

HIGH RESOLUTION MRI BRAIN IMAGE SEGMENTATION TECHNIQUE USING HOLDER EXPONENT 

M. Ganesh1 and Dr. V. Palanisamy2

 1ECE, Info Institute of Engineering, Anna University, Chennai, India 2Principal, Info Institute of Engineering, Anna University, Chennai, India  

ABSTRACT 

Image segmentation is a technique to locate certain objects or boundaries within an image. Image segmentation plays a crucial role in many medical imaging applications. There are many algorithms and techniques have been developed to solve image segmentation problems. Spectral pattern is not sufficient in high resolution image for image segmentation due to variability of spectral and structural information. Thus the spatial pattern or texture techniques are used. Thus the concept of Holder Exponent for segmentation of high resolution medical image is an efficient image segmentation technique. The proposed method is implemented in Matlab and verified using various kinds of high resolution medical images. The experimental results shows that the proposed image segmentation system is efficient than the existing segmentation systems. 

KEYWORDS 

Holder Exponent, Gabor Filter, Clustering, Image Transformation, Morphological Operation.  

Original Source URL : http://airccse.org/journal/ijsc/papers/3412ijsc04.pdf

http://airccse.org/journal/ijsc/current2012.html

APPLICATION OF Q-MEASURE IN A REAL TIME FUZZY SYSTEM FOR MANAGING FINANCIAL ASSETS

APPLICATION OF Q-MEASURE IN A REAL TIME FUZZY SYSTEM FOR MANAGING FINANCIAL ASSETS 

Penka Georgieva1 and Ivan Popchev2 

1Burgas Free University, Faculty of Engineering and Computer Science, Burgas, Bulgaria 2Bulgarian Academy of Science, Sofia, Bulgaria 

ABSTRACT 

One of the major problems that a financial manager faces is the enormous amount of financial data. There is a variety of software systems used to support the process of investment decision making. In this paper, a software system for financial asset management is presented. The system is based on fuzzy logic, operates in real time and differs from existing systems for portfolio management in five key aspects. The system is tested on real data from Bulgarian Stock Exchange. 

KEYWORDS 

Fuzzy System, Financial Data, Asset Management, Portfolio Management, Q-measure 

Original Source URL : http://airccse.org/journal/ijsc/papers/3412ijsc03.pdf

http://airccse.org/journal/ijsc/current2012.html