Artificial Neural Networks: A Review

Software Engineering Methodology

SOFTWARE ENGINEERING METHODOLOGY
1
Introduction
The term artificial neural network is defined as the computing system which constitutes
animal brains and performs various kinds of tasks. Mainly, artificial neural network processes
data or information by their dynamic state response to external inputs [1]. The objective of
this report is to examine the fundamental concept of an artificial neural network and evaluate
a journal paper based on the artificial neural networks. The title of the journal paper is
“Evolutionary artificial neural networks: a review which will be discussed in this report”. The
identification of this paper is to critically review the concept of artificial neural networks and
describe their working principle. This report is divided into several sections for example
intention and content of the paper, research methods, and findings, issues highlighted by the
researchers and results and discussion.
Intention and content of the paper
This journal paper was written by Shifei Ding and other authors in the year 2013 and it is
completely based on the artificial neural networks. The key purpose of this investigation is to
examine the utilization of evolutionary algorithms to optimize artificial neural networks [4].
As per the author's identification artificial neural networks are adaptive nonlinear information
system which combines the various kinds of a computing system with numbers of
characteristics, for example, self-organizing, self-adapting and so on. It is one of the common
technologies which are used in the field of medical but selection of the structure and
parameter is a very difficult task for the ANN system.
The authors argued that the benefits of the artificial neural networks are represented by the
network architecture and the source code. This journal paper mainly focuses on evolutionary
algorithms in order to optimize ANN networks [2]. According to the authors the artificial
neural network includes a set of processing unit which is also called as neurons and the
architecture of the neural network is connected with the neurons. It is observed that the
learning process in the artificial neural network is mainly implemented by training
programmes because the learning process is achieved by iteratively controlling the
connection weights [3]. From this paper, it has found that the major limitation of the artificial
neural network takes more time while adjusting the architecture with the neurons. It is
identified that the performance of the EAs may not be similar to the advanced source code for
several issues. But such kind of process can be used for processing numerous kinds of issues,

SOFTWARE ENGINEERING METHODOLOGY
2
for example, designing the network architecture, learning rules, and training of the
connection process and so on.
The researchers suggested that the evolution of the connection weights draws into a self-
adapting and global technique for training [4]. There are several problems included in the
field of ANN networks such as encoding system, find the fitness function, evolution
approach, train the network and so on. After reading the research paper it has found that the
EAs processes are utilized to select the effective input variables for neural networks from raw
data which is included in the input features [5].
Research methods
Research design
In this research paper, the authors used a qualitative research design while conduction the
research. Mainly, the qualitative research focuses on the theoretical information about the
artificial neural network and helped the authors for reducing research issues and challenges
occurred while conducting the investigation [6]. Therefore, by using such kind of research
design the author improved the quality of the research.
Research strategy
The authors used various kinds of research methods in this paper such as research design,
research approach, and data collection methods and data analysis techniques. In order to
collect the relevant data and information the authors developed and implemented several
strategies including philosophy of the research, previous studies, observation, experiments
and secondary research method. By using all these research strategies the researchers
produced a hypothesis of the research [7].
Data collection method
A secondary research method adopted by the authors in order to obtain the facts and
information about artificial neural networks. The secondary data about ANN networks
collected from various sources, for example, research papers, online websites, books and
other offline resources. With the help of secondary research method, the authors collected
facts and data about artificial neural networks and achieved aims and objectives of the study.