Artificial Neural Networks: New Research

243 Pages·2017·9.63 MB·English

by  Gayle Cain

COMPUTER SCIENCE, TECHNOLOGY AND APPLICATIONS

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COMPUTER SCIENCE, TECHNOLOGY AND APPLICATIONS

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EW ESEARCH

GAYLE CAIN
EDITOR

New York
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Library of Congress Cataloging-in-Publication Data

Names: Cain, Gayle, editor.
Title: Artificial neural networks : new research / editors, Gayle Cain.
Description: Hauppauge, New York, USA : Nova Science Publishers, Inc., [2016]
| Series: Computer science, technology and applications | Includes index.
Identifiers: LCCN 2016035621 (print) | LCCN 2016044235 (ebook) | ISBN 9781634859646 | ISBN 9781634859790
(ebook) | ISBN 9781634859790
Subjects: LCSH: Neural networks (Computer science)
Classification: LCC QA76.87 .A785 2016 (print) | LCC QA76.87 (ebook) | DDC 006.3/2--dc23
LC record available at https://lccn.loc.gov/2016035621

Published by Nova Science Publishers, Inc. † New York
CONTENTS

Preface vii
Chapter 1 Applications of Artificial Neural Networks in Chemical
Engineering 1
Ivan M. Savic, Dragoljub G. Gajic and Ivana M. Savic-Gajic
Chapter 2 Applications of Artificial Neural Networks in Chemistry and
Chemical Engineering 25
Aderval S. Luna, Eduardo R. A. Lima and
Kese Pontes Freitas Alberton
Chapter 3 Applications of Artificial Neural Networks to Energy and Buildings 45
Cinzia Buratti, Domenico Palladino and
Francesco Cristarella Orestano
Chapter 4 Applications of Artificial Neural Network to Predict Biodiesel Fuel
Properties from Fatty Acid Constituents 81
Solomon O. Giwa
Chapter 5 Applications of ANN Methods for Solar Radiation Estimation 107
Gilles Notton, Kahina Dahmani, Rabah Dizene,
Marie-Laure Nivet, Cyril Voyant and Christophe Paoli
Chapter 6 The Use of In Silico Methods to Design and
Evaluate Skin UV Filters 135
Snezana Agatonovic-Kustrin and David W. Morton
Chapter 7 Modeling the Milling Tool Wear by Using a Multilayer Perceptron
Artificial Neural Network from Milling Run Experimental Data 157
P. J. García Nieto and E. García-Gonzalo
Chapter 8 Parameter Extraction of Small-Signal and Noise Models of
Microwave Transistors Based on Artificial Neural Networks 175
Zlatica Marinković, Vladica Đorđević, Nenad Ivković,
Olivera Pronić-Rančić, Vera Marković and Alina Caddemi
vi Contents
Chapter 9 Applying Artificial Neural Networks to Deep Learning and
Predictive Analysis in Semantic TCM Telemedicine Systems 211
Wilfred W. K. Lin and Allan K. Y. Wong
Index 221
PREFACE

This current book provides new research on artificial neural networks (ANNs). Topics
discussed include the application of ANNs in chemistry and chemical engineering fields; the
application of ANNs in the prediction of biodiesel fuel properties from fatty acid constituents;
the use of ANNs for solar radiation estimation; the use of in silico methods to design and
evaluate skin UV filters; a practical model based on the multilayer perceptron neural network
(MLP) approach to predict the milling tool flank wear in a regular cut, as well as entry cut
and exit cut, of a milling tool; parameter extraction of small-signal and noise models of
microwave transistors based on ANNs; and the application of ANNs to deep-learning and
predictive analysis in semantic TCM telemedicine systems.
Chapter 1 - Today, the main effort is focused on the optimization of different processes in
order to reduce and provide the optimal consumption of available and limited resources.
Conventional methods such as one-variable-at-a-time approach optimize one factor at a time
instead of all simultaneously. Unlike this method, artificial neural networks provide analysis
of the impact of all process parameters simultaneously on the chosen responses. The
architecture of each network consists of at least three layers depending on the nature of
process which to be analyzed. The optimal conditions obtained after application of artificial
neural networks are significantly improved compared with those obtained using conventional
methods. Therefore artificial neural networks are quite common method in modeling and
optimization of various processes without the full knowledge about them. For example, one
study tried to optimize consumption of electricity in electric arc furnace that is known as one
of the most energy-intensive processes in industry. Chemical content of scrap to be loaded
and melted in the furnace was selected as the input variable while the specific electricity
consumption was the output variable. Other studies modeled the extraction and adsorption
processes. Many process parameters, such as extraction time, nature of solvent, solid to liquid
ratio, extraction temperature, degree of disintegration of plant materials, etc. have impact on
the extraction of bioactive compounds from plant materials. These parameters are commonly
used as input variables, while the yields of bioactive compounds are used as output during
construction of artificial neural network. During the adsorption, the amount of adsorbent and
adsorbate, adsorption time, pH of medium are commonly used as the input variables, while
the amount of adsorbate after treatment is selected as output variable. Based on the literature
review, it can be concluded that the application of artificial neural networks will surely have
an important role in the modeling and optimization of chemical processes in the future.
viii Gayle Cain
Chapter 2 - Problems in chemistry and chemical engineering are composed of complex
systems. Various chemical processes in chemistry and chemical engineering can be described
by different mathematical functions as, for example, linear, quadratic, exponential, hyperbolic
et al. There are many of calculated and experimental descriptors/molecular properties to
describe the chemical behavior of the substances. It is also possible that many variables can
influence the desired response. Usually, chemometrics is widely used as a valuable tool to
deal chemical data, and to solve complex problems. In this context, Artificial Neural
Networks (ANN) is a chemometric tool that may provide accurate results for complex and
non-linear problems that demand high computational costs. The main advantages of ANN
techniques include learning and generalization ability of data, fault tolerance and inherent
contextual information processing in addition to fast computation capacity. Due to the
popularization, there is a substantial interest in ANN techniques, in special in their
applications in various fields. The following types of applications are considered: data
reduction using neural networks, overlapped signal resolution, experimental design and
surface response, modeling, pattern recognition, and multivariate regression.
Chapter 3 - Energy consumption in buildings and indoor thermal comfort nowadays
issues in engineering applications. A deep analysis of these problems generally requires many
resources. Many studies were carried out in order to improve the methodology available for
the evaluation of the energy consumption or indoor thermal conditions; interesting solutions
with a very good feedback found in the Literature are the Artificial Neural Networks (ANNs).
The peculiarity of ANNs is the opportunity of simulating and resolving complex
problems thanks to their architecture, which allows to identify the combination of the
involved parameters even when they are in a large amount.
The Artificial Neural Networks (ANNs) are very common in engineering applications for
simulating the energy performance of buildings, for predicting a particular parameter, or for
evaluating the indoor thermal conditions in specific environments. However, many different
Artificial Neural Networks are available and each of them should be applied in a specific
field.
This chapter examines and describes the ANNs generally used in the engineering field.
Studies of ANNs applied in topics such as energy consumption in buildings, gas emissions
evaluation, indoor and outdoor thermal conditions calculation, renewable energy sources
investigation, and lighting and acoustics applications are reported. After a brief description of
the main characteristics of ANNs, which allows to focus on the main peculiarity and
characteristics of this kind of algorithms, some applications shown in the Literature and
applied to engineering issues are described.
In the first part of the chapter an analysis of the main parameters which influence the
ANN implementation in the examined papers was carried out, then some applications of ANN
in energy and buildings field found in the Literature are described. In particular, the main
studies were described considering five different clusters: in the first group the ANN
applications to buildings and traditional energy plants are showed, in the second one the ANN
implementation for the thermal and energy performance evaluation of renewable energy
sources are reported. In the third and forth clusters the applications found in the Literature for
the indoor thermal parameters investigation and outdoor thermal conditions calculation are
described, while in the last one other topics investigated using ANN models such as lighting
and acoustics issues are considered.