

Title of Thesis
Application Of Fractional Calculus To Engineering :anew
Computational Approach 
Author(s)
Raja Muhammad Asif Zahoor 
Institute/University/Department
Details Department Of Electronic Engineering, Faculty Of
Engineering And Technology / International islamic University,
Islamabad 
Session 2011 
Subject Electronic 
Number of Pages 160 
Keywords (Extracted from title, table of contents and
abstract of thesis) Solution, Computational,
Application, Approach, Mathematical, Calculus, Training, Algorithms,
Numerical, Engineering, Fractional, Derivatives 
Abstract In this dissertation, a new heuristic computational intelligence
technique has been developed for the solution for fractional order systems in engineering.
These systems are provided with generic ordinary linear and nonlinear differential equations involving
integer and noninteger order derivatives.The design scheme consists of two parts, firstly, the strength
of feedforward artificial neural network (ANN) is exploited for approximate mathematical modeling and
secondly, finding the optimal weights for ANN.The exponential function is used as an activation
function due to availability of its fractional derivative.The linear combination of these networks
defines an unsupervised error for the system.The error is reduced by selection of appropriate unknown
weights, obtained by training the networks using heuristic techniques.The stochastic techniques applied
are based on nature inspired heuristics like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO)
algorithm.Such global search techniques are hybridized with efficient local search techniques for rapid convergence.
The local optimizers used are Simulating Annealing (SA) and Pattern Search (PS) techniques.The methodology
is validated by applying to a number of linear and nonlinear fraction differential equations with known solutions.
The well known nonlinear fractional system in engineering based on Riccati differential equations and BagleyTorvik
Equations are also solved with the scheme.
The comparative studies are carried out for training of weights for ANN networks with SA, PS, GA, PSO,
GA hybrid with SA (GASA), GA hybrid with PS (GAPS), PSO hybrid with SA (PSOSA) and PSO hybrid with PS
(PSOPS) algorithms.It is found that the GASA, GAPS, PSOSA and PSOPS hybrid approaches are the best
stochastic optimizers.The comparison of results is made with available exact solution, approximate analytic
solution and standard numerical solvers. It is found that in most of the cases the design scheme has produced the
results in good agreement with state of art numerical solvers.The advantage of our approach over such solvers is
that it provides the solution on continuous time inputs with finite interval instead of predefine discrete grid of inputs.
The other perk up of the scheme in its simplicity of the concept, ease in use, efficiency, and effectiveness.

