APPLICATION OF FUZZY C-MEANS CLUSTERING AND PARTICLE SWARM OPTIMIZATIONTO IMPROVE VOICE TRAFFIC FORECASTINGIN FUZZY TIME SERIES


For more Electrical & Computer Engineering Project click here



TABLE OF CONTENTS

TITLE PAGE
TABLE OF CONTENTS
LIST OF ABBREVIATIONS
ABSTRACT

CHAPTER ONE: INTRODUCTION
1.1 Background Information
1.2 Aim and Objectives
1.3 Statement of the Problem
1.4 Methodology
1.5 Significant Contributions
1.6 Thesis Organization

CHAPTER TWO: LITERATURE REVIEW
2.1 Introduction
2.2 Review of Fundamental Concepts
2.2.1 Time Series
2.2.2 Fuzzy Set Theory
2.2.3 Fuzzy Time Series and Fuzzy Logic Relationship
2.2.4 Universe of Discourse
2.2.5 Fuzzy Set Groups
2.2.6 Data Mining and Clustering
2.2.6.1Distance Measure
2.2.7Fuzzy C-Meeans Clustering
2.2.8Cluster Validity Index
2.2.9 Particle Swarm Optimization
2.2.10 Defuzzification Operator
2.2.11Erlang Based Voice Traffic
2.2.12 Performance Measure
2.2.13 Programming Language
2.2.13.1C programming Language
2.2.13.2 C++ Programming Language
2.2.13.3 Java Programming Language
2.2.13.4 C# Programming Language
2.3 Review of Similar Works

CHAPTER THREE: MATERIAL AND METHODS
3.1 Introduction
3.2 Data Collection and Processing
3.3 Fuzzification Module
3.3.1 Coding fuzzy C-Means (FCM) Clustering Algorithm in C#
3.3.2    Applying Time Series Data on Fuzzy C-Means Code
3.3.3    Ranking Clusters in Ascending Order
3.3.4    Fuzzifying Time Series Data
3.4 Defuzzification Module
3.4.1 Establishing Fuzzy Set Groups (FSGs)
3.4.2 Converting Fuzzy Set Groups into “if – then” Rules
3.4.3 Tuning “if – then” Rules Using Particle Swarm Optimization (PSO)
3.4.4 Deriving Forecasts
3.5 Investigating the Effect of Reversed Weights
3.6 Forecasting Test Data Set
3.7 Forecasting Using Chen’s (1996) Fuzzy Time Series Model
3.8 Forecasting Using Cheng et al (2008) Hybrid Model

CHAPTER FOUR: RESULTS AND DISCUSSIONS
4.1 Introduction
4.2 Forecasting Results for Training Data Set
4.3 Forecasting Result for Test Data Set Forecasts
4.4 Validation
4.5 Significance of Forecasting Results

CHAPTER FIVE: CONCLUSION AND RECOMMENDATIONS
5.1 Summary
5.2 Conclusion
5.3 Limitations
5.4 Recommendations for Further Works
REFERENCES



ABSTRACT

Forecasting of voice traffic using an accurate model is important to the telecommunication service provider in planning a sustainable Quality of Service (QoS) for their mobile networks. This work is aimed at forecasting Erlang C – based voice traffic using a hybrid forecasting model that integrates fuzzy C-means clustering (FCM) and particle swarm optimization (PSO) algorithms with fuzzy time series (FTS) forecasting model. Fuzzy C-means (FCM) clustering, which is an algorithm for data classification, is adopted at the fuzzification phase to obtain unequal partitions. Particle swarm optimization (PSO), which is an evolutional search algorithm, is adopted to optimize the defuzzification phase; by tuning weights assigned to fuzzy sets in a rule.This rule is a fuzzy logical relationship induced from a fuzzy set group (FSG). The clustering and optimization algorithms were implemented in programs written in C#. Daily Erlang C traffic observations collected over a three (3) month period from 1 December, 2012 – 28 February, 2013 from Airtel, Abuja region, was used to evaluate the proposed hybrid model.To evaluate the forecasting efficiency of the proposed hybrid model, its statistical performance measures of mean square error (MSE) and mean absolute percentage error (MAPE), were calculated and compared with those of a conventional fuzzy time series (FTS) model and, a fuzzy C-means (FCM) clustering and fuzzy time series (FTS) hybrid model.Statistical results of
MSE   0.9867 and  MAPE   0.47 % were
obtained  during  training  of  the  proposed  hybrid
forecasting model. Compared with the training results of MSE
845.122 and MAPE   13.47 % ,
for Chen‟s (1996) FTS model and;  MSE
856.145 and  MAPE
13.37 % , for Cheng‟s (2008);
the proposed hybrid forecasting model resulted in a relatively higherforecasting accuracy and
precision.  Also,  performancemeasures  of
MSE
59.22 and  MAPE
3.85 % were
obtained
during thetesting phase of the proposed model. Compared with the test results of MSE
1567.4
and  MAPE   23.98 % obtained  for  Cheng‟s
(2008)
FCM/  FTS  hybrid
model,  the
proposed
hybrid forecasting model also showed a relatively higher forecasting accuracy and precision. Finally, it was determined that reversing the weights of the forecasting rules, during training,
resulted to a lesser performance; MSE
42.73 and MAPE
0.88 %. Thus, reversing the weights
of
forecasting
rule
affected
the
forecasting
accuracy.





CHAPTER ONE



INTRODUCTION


1.1 BACKGROUND INFORMATION

Since its inception over three decades ago, mobile telecommunication call centres have witnessed exponential growth. Call centres are on the increase owing to the large number of mobile subscribers and the need for telecommunication operators to lower cost of providing services while increasing time access of their services. Understanding voice traffic pattern of a call centre becomes critical to service providers in predicting traffic, planning and budgeting for future changes of their mobile networks. This is important for sustaining a good Quality of Service (QoS).

Forecasting is used to predict, model and simulate the future from past events in virtually all fields of endeavours. In the telecommunication industry, forecasting is a useful tool in planning, budgeting, evaluating and verifying network resources (Eleruja et al, 2012).

Voice traffic is one of the critical measures in mobile telecommunication systems. Since this measure is non – linear and dynamic with time, forecasting Erlang based voice traffic observations using fuzzy time series (FTS) models seems to be more suitable than conventional statistical models. Fuzzy time series (FTS) models take care of uncertainties in observations over time and does not require any restrictive assumptions and too much background knowledge of the data; like in the case of conventional statistical forecasting methods.The use of fuzzy time series (FTS) in forecasting was first introduced by Song and Chissom (1993). This approach comprises two phases; fuzzification and defuzzification. Fuzzification is a technique for conversion of real observations into discrete or linguistic fuzzy sets. Defuzzification is a technique for converting linguistic observations to real values....


For more Electrical & Computer Engineering Project click here


____________________________________________________________________________
This is a Postgraduate Thesis and the complete research material plus questionnaire and references can be obtained at an affordable price of N3,000 within Nigerian or its equivalent in other currencies.


INSTRUCTION ON HOW TO GET THE COMPLETE PROJECT MATERIAL

Kindly pay/transfer a total sum of N3,000 into any of our Bank Accounts listed below:
·         Diamond Bank Account:
A/C Name:      Haastrup Francis
A/C No.:         0096144450

·         GTBank Account:
A/C Name:      Haastrup Francis
A/C No.:         0029938679
After payment, send your desired Project Topic, Depositor’s Name, and your Active E-Mail Address to which the material would be sent for downloading (you can request for a downloading link if you don’t have an active email address) to +2348074521866 or +2348066484965. You can as well give us a direct phone call if you wish to. Projects materials are sent in Microsoft format to your mail within 30 Minutes once payment is confirmed.

--------------------------------------------------------
N/B:    By ordering for our material means you have read and accepted our Terms and Conditions


Terms of Use: This is an academic paper. Students should NOT copy our materials word to word, as we DO NOT encourage Plagiarism. Only use as guide in developing your original research work.

Delivery Assurance
We are trustworthy and can never SCAM you. Our success story is based on the love and fear for God plus constant referrals from our clients who have benefited from our site. We deliver project materials to your Email address within 15-30 Minutes depending on how fast your payment is acknowledged by us.

Quality Assurance
All research projects, Research Term Papers and Essays on this site are well researched, supervised and approved by lecturers who are intellectuals in their various fields of study.
Share:

Search for your topic here

See full list of Project Topics under your Department Here!

Featured Post

Article: How to Write a Research Proposal

Most students and beginning researchers do not fully understand what a research proposal means, nor do they understand ...

Popular Posts