Focus on Artificial Neural Networks

425 Pages·2011·7.807 MB·English

by  John A. Flores

MATHEMATICS RESEARCH DEVELOPMENTS

F A N
OCUS ON RTIFICIAL EURAL
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MATHEMATICS RESEARCH DEVELOPMENTS

F A N
OCUS ON RTIFICIAL EURAL
NETWORKS

JOHN A. FLORES
EDITOR

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Focus on artificial neural networks / editor, John A. Flores.
p. cm.
Includes index.
ISBN 978-1-61942-100-4 (eBook)
1. Neural networks (Computer science) I. Flores, John A.
QA76.87.F623 2011
006.3'2--dc23
2011012975

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

Preface vii
Chapter 1 Application of Artificial Neural Networks (ANNs) in Development
of Pharmaceutical Microemulsions 1
Ljiljana Djekic, Svetlana Ibric and Marija Primorac
Chapter 2 Investgations of Application of Artificial Neural Network for Flow
Shop Scheduling Problems 29
T. Radha Ramanan
Chapter 3 Artificial Neural Networks in Environmental Sciences and
Chemical Engineering 55
F. G. Martins, D. J. D. Gonçalves and J. Peres
Chapter 4 Establishing Productivity Indices for Wheat in the Argentine
Pampas by an Artificial Neural Network Approach 75
R. Alvarez and J. De Paepe
Chapter 5 Design of Artificial Neural Network Predictors in Mechanical
Systems Problems 97
İkbal Eski, Eyüp Sabri Topaland Şahin Yildirim
Chapter 6 Massive-Training Artificial Neural Networks for Supervised
Enhancement/Suppression of Lesions/Patterns in Medical Images 129
Kenji Suzuki
Chapter 7 An Inverse Neural Network Model of Disc Brake Performance at
Elevated Temperatures 151
Dragan Aleksendrić
Chapter 8 Artificial Neural Networks; Definition, Properties and Misuses 171
Erkam Guresen and Gulgun Kayakutlu
Chapter 9 Evidences of New Biophysical Properties of Microtubules 191
Rita Pizzi, Giuliano Strini, Silvia Fiorentini, Valeria Pappalardo
and Massimo Pregnolato
vi Contents
Chapter 10 Forecasting Stream Temperature Using Adaptive Neuron-Fuzzy
Logic and Artificial Neural Network Models 209
Goloka Behari Sahoo
Chapter 11 Neural Network Applications in Modern Induction
Machine Control Systems 231
Dinko Vukadinović and Mateo Bašić
Chapter 12 Wavelet Neural Networks: A Recent Strategy For Processing
Complex Signals Applications to Chemistry 257
Juan Manuel Gutiérrez, Roberto Muñoz and Manel del Valle
Chapter 13 Robustness Verification of Artificial Neural Network Predictors in a
Purpose-Built Data Compression Scheme 277
Rajasvaran Logeswaran
Chapter 14 Intelligent Inverse Kinematics Solution for Serial Manipulators
Passing through Singular Configurations with Performance
Prediction Network 299
Ali T. Hasan and H. M. A. A. Al-Assadi
Chapter 15 Using Artificial Neural Networks for Continuously Decreasing
Time Series Data Forecasting 323
Mebruk Mohammed, Kunio Watanabe and Shinji Takeuchi
Chapter 16 Application of Artificial Neural Networks in Enzyme Technology 341
Mohd Basyaruddin Abdul Rahman, Naz Chaibakhsh, Mahiran Basri
and Abu Bakar Salleh
Chapter 17 Development of an ANN Model for Runoff Prediction 355
A. Bandyopadhyay and A. Bhadra
Chapter 18 Artificial Neural Networks Concept: Tools to Simulate, Predict and
Control Processes 375
Abdoul-Fatah Kanta

Index 399
PREFACE

Chapter 1 – An artificial neural network (ANN) is an intelligent non-linear mapping
system built to loosely simulate the functions of the human brain. An ANN model consists of
many nodes and their connections. Its capacity is characterized by the structure, transfer
function and learning algorithms. Because of their model independence, non-linearity,
flexibility, and superior data fitting and prediction ability, ANNs have gained interest in the
pharmaceutical field in the past decade. The present chapter highlights the potential of ANNs
in the development of pharmaceutical microemulsions. Although microemulsions are
currently of interest to the pharmaceutical scientist as promising drug delivery vehicles, the
formulation of such unique and complex colloidal systems requires a great experimental
effort due to a diverse range of possible colloidal systems as well as coarse dispersions,
beside microemulsions, which may be formed in water–oil–tensides systems, depending on
temperature and physico-chemical properties and concentrations of constituents. The
determination of the region of existence of microemulsions, as the collection of numerous
potential pharmaceutical formulations, requires complex and time consuming phase
behaviour investigations. Therefore, there is a growing interest of researchers for in silico
development of ANN models for prediction and/or optimization of the phase behaviour of
microemulsion-forming systems using as inputs the data extracted from the phase diagrams
already published in the literature or those collected by constructing the phase diagrams using
the limited number of experiments. This chapter will be mainly focused on the recent results
of the investigations conducted to estimate the applicability of ANN in evaluation of the
phase behaviour of microemulsion-forming systems employing the complex mixtures of
novel pharmaceutically acceptable nonionic surfactants.
Chapter 2 – The objective of this chapter is to present the research findings, of the author,
that primarily use Artificial Neural Network (ANN) as a tool to find an improved solution for
the performance measure(s) taken under consideration. The following studies are undertaken
to investigate the applicability of ANN:
A bicriterian approach considering makespan and total flow time as performance
measures to flow shop scheduling problem applying ANN with competitive network structure
is made as a first attempt. With this objective, the architecture is constructed in two stages,
viz. initial learning stage and implementation stage. In the initial learning stage the nodes of
the network learns the scheduling incrementally and implements the same in the
implementation stage. A number of problems are solved for different combinations of jobs
and machines by varying jobs from 5 to 30 in steps of 5 and by varying machines from 5 to
viii John A. Flores
30 in steps of 5. A total of 180 problems are solved by taking 5 problems in each set. The
work is then extended to seek solutions for multicriteria flow shop scheduling considering
makespan, earliness and lateness as performance measures. The result of the ANN is
discussed in comparison with particle swarm optimization (PSO).
The next part of the study is modeled with the back propagation network of ANN and
tested for seeking solutions to makespan as a performance measure. The results of ANN is
sought to be further improved with improvement heuristics, Genetic algorithm (GA) and
Simulated Annealing (SA). The problems are also tested against Taillard‟s benchmark
problems (1993). The work aims at obtaining improved solutions by initializing SA and GA
with a good starting solution provided by ANN. El-Bouri et al. (2005) show that neural
sequences exhibit the potential to lead neighborhood search methods to lower local optima.
This aspect is investigated in the study by making a comparison of the performance of the
perturbation search and a non-perturbation search when starting from ANN initial solutions.
The results show that neural sequences when made a perturbation, exhibit the potential to lead
neighborhood search methods to lower local optima.
Chapter 3 – Artificial neural networks have been used for a long time in a wide range of
fields inside Environmental Sciences and Chemical Engineering. The main reason for this
extensive utilization is the ability of this technique to model easily the complexity of the
systems related with these fields, keeping most of the valuable original information about
each system.
The feedforward artificial neural networks are the most commonly used topology due to
the inherent simple architecture, the diversity of the available training algorithms, and the
good performances. Besides feedforward artificial neural networks, the self organizing maps,
or also called Kohonen neural networks, have as well relevant applications.
In Environmental Sciences, the most relevant applications appear in modelling for both
environmental and biological processes. In Chemical Engineering, artificial neural networks
have been applied mainly in: i) modelling; ii) control; and iii) development of software
sensors.
This chapter compiles several applications that have been published recently concerning
the subjects referred above. A special attention is given to the relevance of the cases, the
procedures/techniques, and the ability to be extrapolated to other applications.
Chapter 4 – The Pampas of Argentina is a vast fertile plain that covers approximately 60
Mha and is considered as one of the most suitable regions for grain production worldwide.
Wheat production represents a main national agricultural activity in this region. Usually,
regression techniques have been used in order to generate wheat yield models, at regional and
subregional scales. In a whole regional analysis, using these techniques, climate and soil
properties explained 64% of the spatial and interannual variability of wheat yield. Recently,
an artificial neural network (ANN) approach was developed for wheat yield estimation in the
region. In this chapter the authors compared the performance of multiple regression methods
with the ANN approach as wheat yield estimation tools and propose developing productivity
indexes by the latter technique. The ANN approach was able to generate a better explicative
model than regression, with a lower RMSE. It could explain 76% of the interannual wheat
yield variability with positive effects of harvest year, soil available water holding capacity,
soil organic carbon, photothermal quotient and the ratio rainfall/crop potential
evapotranspiration. Considering that the input variables required to run the ANN can be
available 40-60 days before crop harvest, the model has a yield forecasting utility. The results
Preface ix
of the ANN model can be used for estimating climate and soil productivity. A climate
productivity index developed assessed the effect of the climate scenario and its changes on
crop yield. A soil productivity index was also elaborated which represents the capacity to
produce a certain amount of harvest grain per hectare, depending on soil characteristics.
These indices are tools for characterizing climatic regions and for identifying productivity
capabilities of soils at regional scale. The methodology developed can be applied in other
cropping areas of the World and for different crops.
Chapter 5 – Due to nonlinearity of the mechanical systems, it is necessary to use adaptive
predictors for analysing system parameters. Neural networks could be used as an alternative
to overcome such problems. In this chapter, two approaches of mechanical systems are
presented for CAD-CAM systems and vehicle suspension systems. In the first approach,
surface roughness prediction studies on end milling operations are usually based on three
main parameters composed of cutting speed, feed rate and depth of cut. The step-over ratio is
usually neglected without investigating it. The aim of this study is to discover the role of the
step-over ratio in surface roughness prediction studies in flat end milling operations. In
realising this, machining experiments are performed under various cutting conditions by using
sample specimens. The surface roughnesses of these specimens are measured. Two Artificial
neural networks (ANN) structures were constructed. First of them was arranged with
considering, and the second without considering the step-over ratio. ANN structures were
trained and tested by using the measured data for predicting surface roughness. Average RMS
error of the ANN model with considering step-over ratio is 0.04 and without considering
stepover ratio is 0.26. The first model proved capable of prediction of average surface
roughness (Ra) with a good accuracy and the second model revealed remarkable deviations
from the experimental values.
Other approach is consisted of analyzes effects of vibrations on comfort and road holding
capability of vehicles as observed in the variations of suspension springs, road roughness etc.
Also, design of non-linear experimental car suspension system for ride qualities using neural
networks is presented. Proposed active suspension system has been found more effective in
vibration isolation of car body than linear active suspension system. Proposed neural network
predictor could be used in vehicle‟s suspension vibration analysis.
The results of both approaches improved that ANN structure has superior performance at
adapting large disturbances of mechanical systems.
Chapter 6 – Medical imaging is an indispensable tool for patients‟ healthcare in modern
medicine. Machine learning plays an important role in the medical imaging field, including
medical image processing, medical image analysis, computer-aided diagnosis, organ/lesion
segmentation, lesion classification, functional brain mapping, and image-guided therapy,
because objects in medical images such as lesions, structures, and anatomy often cannot be
modeled accurately by simple equations; thus, tasks in medical imaging require some form of
“learning from examples.” Pattern enhancement (or suppression: enhancement of specific
patterns means suppression of other patterns) is one of the fundamental tasks in medical
image processing and analysis. When a doctor diagnoses lesions in medical images, his/her
tasks are detection, extraction, segmentation, classification, and measurement of lesions. If we
can enhance a specific pattern such as a lesion of interest in a medical image accurately, those
tasks are almost complete. What is left to do is merely thresholding of the enhanced lesion.
For the tasks of detection and measurement, calculation of the centroid of and the area in the
thresholded region may be needed. Thus, enhancement (or suppression) of patterns is one of