Fast Track FT131015

King Fahd University of Petroleum & Minerals Deanship of Scientific Research

TWIN SUPPORT VECTOR MACHINE SYSTEM IDENTIFICATION

Submitted under

Fast Track FT131015

Principal Investigator, DR. Mujahed M. Al-Dhaifallah Assistant Prof. Consultant Dr. Nisar Kottakkaran Sooppy Assistant Prof.

Mathematics Department Prince Sattam bin Abdulaziz University

Date: Sep 01 2015

TYPE – A FINAL REPORT

King Fahd University of Petroleum & Minerals Deanship of Scientific Research PROJECT INFORMATION

I. PROJECT INFORMATION Project title

TWIN SUPPORT VECTOR MACHINE SYSTEM IDENTIFICATION

Project #

FT131015

Duration (months)

18 months

Progress Report #

Start Date



F I R S T

 SECOND



F O U R T H

 X FIFTH

Report Period

From: 01/March/2014

Principal Investigator

Dr. Mujahed

Co-Investigator(s)

01/03/14

End Date 

T H I R D

To: 1/September/2015

M. Al-Dhaifallah

2. 3. 4.

Consultant(s)

1.Dr.Nisar Kottakkaran Sooppy 2. Other Personnel

8

M.S. Student

9

Engineer

10

Technician

TYPE – A FINAL REPORT

01/09/15

King Fahd University of Petroleum & Minerals Deanship of Scientific Research SUMMARY System identification is the process of developing or improving the mathematical representation of a physical system using experimental data. There are two types of identification depending on the linearity and non-linearity of the system. Non-linear systems identification constitutes one of primary challenges faced by today’s engineering research community. Indeed, many of engineering systems are nonlinear and uncertain. The uncertainty arises for many reasons, including inherent limitations of a particular model of the plant, backlash phenomenon, inertia effects, un-modelled resonance, friction forces, noise in sensor measurements, and the computational noise due to the finite precision of any computer. Notwithstanding efforts at reducing noise sources, the error sources remain and it is necessary to contend with them in precision control systems. This project seeks to address the linear and non-linear Twin Support vector Machine (TSVR) identification problem. The non-linear models to be considered are Hammerstein and Weiner models. The Hammerstein and Weiner configuration are used widely to model many nonlinear systems. In this project we developed new algorithms to identify Auto regressive exogenous model (ARX), Hammerstein and Weiner models based on Twin Support Vector Machine (TSVR). The algorithms were compared to Support Vector Machine (SVM) and least Square Support Vector Machine (LSSVM) using simulation and experimental data.

Key Words:

System identification, Auto regressive exogenous model (ARX),, Hammerstein and Weiner

models , Twin Support Vector Machine (TSVR).

TYPE – A FINAL REPORT -1-

‫‪King Fahd University of Petroleum & Minerals‬‬ ‫‪Deanship of Scientific Research‬‬

‫الملخص‬ ‫التعرف على النظم (‪ )System Identification‬هي عملية الهدف منها الوصول إلى نموذج رياضي عن نظام‬ ‫ما باستخدام البيانات التجريبية واستخراج القوانين والعالقات بين خاصياته‪ .‬هناك نوعين من طرق التعرف على‬ ‫النظم اعتمادا على الخاصيتين الخطية والالخطية‪ .‬يعد التعرف على النظم الالخطية واحد من أهم التحديات‬ ‫البحثية الهندسية اليوم وكذلك فإن العديد منها في الواقع الهندسي هي أنظمة الخطية وغير معينه أو غير مؤكدة‪.‬‬ ‫وعدم التعيين هذا يزداد لمجموعة من األسباب منها القيود المتأصلة المالزمة لنموذج معين من المادة‪ ،‬ظاهرة‬ ‫الحركة اال رتجاعية‪ ،‬تأييرات القوور الذاتي‪ ،‬الود‬

‫الالنموذيي‪ ،‬قو‬

‫االحتااك‪ ،‬الووضا المواحبة‬

‫للقياسات‪ ،‬وكذلك األخطا الحسابية الناتجة عن تحديد الدقة في أيهزة الحاسوب‪ .‬وعلى الرغم من الجهود للحد‬ ‫من موادر الووضا واألخطا إال أن هذه الموادر تظل متوايدة ومن الوروري التعامل معها في أنظمة‬ ‫تحام دقيقة‪.‬‬ ‫هذا المشروع يهدف لدراسة وبحث مشالة التعرف على النظم الخطية والالخطية باستخدام طريقة آلة متجه الدعم‬ ‫المزدوية (‪ .)Twin Support Vector Machine-TSVR‬وسوف يتم األخذ بعين االعتبار نموذيين غير‬ ‫خطين يستخدمان بشال واسع في األنظمة وهما‪ :‬نموذج هامرشتاين وكذلك نموذج وينر‪.‬‬ ‫في هذا العمل البحثي سوف يتم تقديم خوارزميات يديدة للتعرف على النماذج‪Auto Regressive ( :‬‬ ‫‪ )Exogenous Model‬وهامرشتاين وكذلك نموذج وينر‪ ،‬اعتمادا على طريقة آلة متجه الدعم المزدوية‬ ‫(‪ .)TSVR‬الخوارزميات سوف تقارن مع طريقة آلة متجه الدعم (‪)Support Vector Machine-SVM‬‬ ‫باإلضافة إلى طريقة (‪ )Least Square Support Vector Machine-LSSVM‬وهذه المقارنة تاون‬ ‫اعتمادا على برنامج محاكاة وكذلك البيانات التجريبية‪.‬‬

‫‪TYPE – A FINAL REPORT‬‬ ‫‪-2-‬‬

King Fahd University of Petroleum & Minerals Deanship of Scientific Research Table of Contents SUMMARY ........................................................................................................................ 1 ‫ الملخص‬................................................................................................................................ 2 1.0

PROJECT OBJECTIVES..................................................................................... 4

2.0

RESEARCH OUTCOMES .................................................................................. 4

3.0

OBJECTIVES MAPPING .................................................................................... 5

4.0

ADDITIONAL ACHIEVEMENTS .................................................................... 5

5.0

CONTRIBUTING MEMBERS .......................................................................... 6 LIST OF TABLES

Table 1: Objectives Mapping………………………………………………………….5


King Fahd University of Petroleum & Minerals Deanship of Scientific Research 1.0

PROJECT OBJECTIVES The general objective of this project is to develop new identification algorithms using Twin Support Vector

Machine (TSVR). Namely, identification of Auto-Regressive Exogenous (ARX), Hammerstein, and Weiner models is introduced and studied. The algorithms performance is compared to algorithms developed based on to Support Vector Machine (SVM) and Least Square Support Vector Machine (LSSVM) regressions using simulation and experimental data. More specifically, the following elements are targeted: i)

Develop an algorithm to identify linear auto regressive exogenous model based on Twin Support Vector Machine Regression (TSVR)

ii) iii)

Develop an algorithm to identify non-linear Hammerstein model using Twin Support Vector Machine. Develop an algorithm to identify non-linear Weiner model and give new algorithm using Twin Support Vector Machine.

iv)

Implementing developed ARX, Hammerstein and Weiner Model algorithms to identify simulation

and experimental examples..

v)

Conduct a comparative study between TSVR and LSSVR, SVR.

. 2.0

RESEARCH OUTCOMES

Note: Only those publications that are real outcomes of the project and acknowledge the project number should be included. Project outcomes are only those which are direct outcomes of the project after its inception and that reflect clear acknowledgement of the project and its number

2.1.

List of Journal Publications

[J1] Mujahed Al-dhaifallah,Support Vector Machine Method for Identification of Wiener Models, Mathematical Problems in Engineering, Article ID 125868,2015 [J2] Mujahed Al Dhaifallah and K. S. Nisar , Support Vector Machine Identification of Subspace Hammerstein Models ,International Journal of Computer Theory and Engineering, Vol. 7, No. 1, 2015 [J3] Mujahed Al-Dhaifallah, Nisar K.S, Identification of Auto-Regressive Exogenous Models Based on Twin Support Vector Machine Regression, Life Science Journal,2013:10(4).

2.2.

List of Conference Publications

[C1] Mujahed Al-Dhaifalla, K.S. Nisar, Wiener modeling and identification of a reverse osmosis desalination process using least square support vector machine, IEEE Industrial Technology (ICIT), 2015 Conference , Pages: 559 - 563, DOI: 10.1109/ICIT.2015.7125158 [C2] Mujahed Al-Dhaifalla, K.S.Nisar, Support Vector Machine Identification of a Parallel Cascade Model of Human Ankle Stiffness, Accepted in ASCC 2015, Malasia

2.3.

Others Outcomes

TYPE – A FINAL 4 REPORT

-4-

King Fahd University of Petroleum & Minerals Deanship of Scientific Research 3.0

OBJECTIVES MAPPING Table 1: Objectives Mapping

S. No.

4.0

Project Objective

Achievements

Develop an algorithm to identify linear auto regressive exogenous model based on Twin Support Vector Machine Regression (TSVR)

This objective was achieved as proved from the publications.

Develop an algorithm to identify non-linear Hammerstein model using Twin Support Vector Machine

The objective is achieved as proved from the publications.

Develop an algorithm to identify non-linear Weiner model and give new algorithm using Twin Support Vector Machine.

The objective is achieved as proved from the publications.

Implementing developed ARX, Hammerstein and Weiner Model algorithms to identify simulation and experimental examples.

The objective is achieved and proved in all papers

Conduct a comparative study between TSVR and LSSVR, SVR.

The objective is achieved and proved

Publication that Reflect Objective Achievement [J1] Mujahed Al-Dhaifallah, Nisar K.S, Identification of AutoRegressive Exogenous Models Based on Twin Support Vector Machine Regression, Life Science Journal,10(4),2013 (ISI IF 0.05) [J2] Mujahed Al Dhaifallah and K. S. Nisar , Support Vector Machine Identification of Subspace Hammerstein Models ,International Journal of Computer Theory and Engineering, Vol. 7, No. 1, 2015 [J3] Mujahed Aldhaifallah,Support Vector Machine Method for Identification of Wiener Models, Mathematical Problems in Engineering, Article ID 125868,2015 (ISI IF 1.05) [C1] Mujahed Al-Dhaifalla, K.S. Nisar, Wiener modeling and identification of a reverse osmosis desalination process using least square support vector machine, IEEE Industrial Technology (ICIT), 2015 Conference. [J1], [J2] ,[J3], [C1] and [C2] Mujahed Al-Dhaifalla , K.S.Nisar, Support Vector Machine Identification of a Parallel Cascade Model of Human Ankle Stiffness, Accepted in ASCC 2015, Malasia [J3]

ADDITIONAL ACHIEVEMENTS

All the tasks # 1.1 to 3.4 / Objectives #1- #3 are completely done.


King Fahd University of Petroleum & Minerals Deanship of Scientific Research

5.0

CONTRIBUTING MEMBERS

Please note that only those contributors listed in this Section will be eligible for payments for the report period. Kindly provide a brief write-up addressing the contribution(s) of each investigator in accomplishing the project tasks/activities. Please provide justification(s) for discrepancies (if any) between the approved and actual work contribution(s). S. No.

1.

2.

Personnel *

Dr.Mujahid AlDhaifallah

Dr. Nisar Kottakkaran Sooppy

Employee ID (if applicable)

1990086

74318

Role

Contribution during the report period

Being the Principle Investigator in the project, his job is to closely coordinate with other people in the group (Consultant and the Graduate Assistants) to accomplish the Principal objectives of the project. This includes Investigator tasks of literature survey, formulation of new scheme, selection of appropriate equipment to purchase, ordering process, etc.

Project Consultant

Because of the mutual interest in this research area and the project, the PI and the Consultant are working together very closely on all the tasks, with a view to extending this work much further into other collaborative projects.

3. 4. 5. 6. 7. 8. 9. 10 * Personnel refer to Principal Investigator, Co-Investigators, Consultant(s), Graduate Students (Ph.D/M.S.), Undergraduate Students, Technicians, Secretary, etc.

* Only those members mentioned in the reports including the Employee I.D’s will be eligible for compensation subject to their involvement in other project(s) as per the DSR regulations & guidelines
