Abstract: In this paper, a tri–neuron network model with time
delay is investigated. By using the Bendixson-s criterion for high–
dimensional ordinary differential equations and global Hopf bifurcation
theory for functional differential equations, sufficient conditions
for existence of periodic solutions when the time delay is sufficiently
large are established.
Abstract: The rate of production of main products of the Fischer-Tropsch reactions over Fe/HZSM5 bifunctional catalyst in a fixed bed reactor is investigated at a broad range of temperature, pressure, space velocity, H2/CO feed molar ratio and CO2, CH4 and water flow rates. Model discrimination and parameter estimation were performed according to the integral method of kinetic analysis. Due to lack of mechanism development for Fisher – Tropsch Synthesis on bifunctional catalysts, 26 different models were tested and the best model is selected. Comprehensive one and two dimensional heterogeneous reactor models are developed to simulate the performance of fixed-bed Fischer – Tropsch reactors. To reduce computational time for optimization purposes, an Artificial Feed Forward Neural Network (AFFNN) has been used to describe intra particle mass and heat transfer diffusion in the catalyst pellet. It is seen that products' reaction rates have direct relation with H2 partial pressure and reverse relation with CO partial pressure. The results show that the hybrid model has good agreement with rigorous mechanistic model, favoring that the hybrid model is about 25-30 times faster.
Abstract: This paper presents a novel methodology for Maximum Power Point Tracking (MPPT) of a grid-connected 20 kW Photovoltaic (PV) system using neuro-fuzzy network. The proposed method predicts the reference PV voltage guarantying optimal power transfer between the PV generator and the main utility grid. The neuro-fuzzy network is composed of a fuzzy rule-based classifier and three Radial Basis Function Neural Networks (RBFNN). Inputs of the network (irradiance and temperature) are classified before they are fed into the appropriated RBFNN for either training or estimation process while the output is the reference voltage. The main advantage of the proposed methodology, comparing to a conventional single neural network-based approach, is the distinct generalization ability regarding to the nonlinear and dynamic behavior of a PV generator. In fact, the neuro-fuzzy network is a neural network based multi-model machine learning that defines a set of local models emulating the complex and non-linear behavior of a PV generator under a wide range of operating conditions. Simulation results under several rapid irradiance variations proved that the proposed MPPT method fulfilled the highest efficiency comparing to a conventional single neural network.
Abstract: On-line handwritten scripts are usually dealt with pen tip traces from pen-down to pen-up positions. Time evaluation of the pen coordinates is also considered along with trajectory information. However, the data obtained needs a lot of preprocessing including filtering, smoothing, slant removing and size normalization before recognition process. Instead of doing such lengthy preprocessing, this paper presents a simple approach to extract the useful character information. This work evaluates the use of the counter- propagation neural network (CPN) and presents feature extraction mechanism in full detail to work with on-line handwriting recognition. The obtained recognition rates were 60% to 94% using the CPN for different sets of character samples. This paper also describes a performance study in which a recognition mechanism with multiple thresholds is evaluated for counter-propagation architecture. The results indicate that the application of multiple thresholds has significant effect on recognition mechanism. The method is applicable for off-line character recognition as well. The technique is tested for upper-case English alphabets for a number of different styles from different peoples.
Abstract: The leisure boatbuilding industry has tight profit margins that demand that boats are created to a high quality but with low cost. This requirement means reduced design times combined with increased use of design for production can lead to large benefits. The evolutionary nature of the boatbuilding industry can lead to a large usage of previous vessels in new designs. With the increase in automated tools for concurrent engineering within structural design it is important that these tools can reuse this information while subsequently feeding this to designers. The ability to accurately gather this materials and parts data is also a key component to these tools. This paper therefore aims to develop an architecture made up of neural networks and databases to feed information effectively to the designers based on previous design experience.
Abstract: Most neural network (NN) models of human category learning use a gradient-based learning method, which assumes that locally-optimal changes are made to model parameters on each learning trial. This method tends to under predict variability in individual-level cognitive processes. In addition many recent models of human category learning have been criticized for not being able to replicate rapid changes in categorization accuracy and attention processes observed in empirical studies. In this paper we introduce stochastic learning algorithms for NN models of human category learning and show that use of the algorithms can result in (a) rapid changes in accuracy and attention allocation, and (b) different learning trajectories and more realistic variability at the individual-level.
Abstract: In this paper, we consider the uniform asymptotic stability, global asymptotic stability and global exponential stability of the equilibrium point of discrete Hopfield neural networks with delays. Some new stability criteria for system are derived by using the Lyapunov functional method and the linear matrix inequality approach, for estimating the upper bound of Lyapunov functional derivative.
Abstract: The aim of the article is extending and developing
econometrics and network structure based methods which are able to
distinguish price manipulation in Tehran stock exchange. The
principal goal of the present study is to offer model for
approximating price manipulation in Tehran stock exchange. In order
to do so by applying separation method a sample consisting of 397
companies accepted at Tehran stock exchange were selected and
information related to their price and volume of trades during years
2001 until 2009 were collected and then through performing runs
test, skewness test and duration correlative test the selected
companies were divided into 2 sets of manipulated and non
manipulated companies. In the next stage by investigating
cumulative return process and volume of trades in manipulated
companies, the date of starting price manipulation was specified and
in this way the logit model, artificial neural network, multiple
discriminant analysis and by using information related to size of
company, clarity of information, ratio of P/E and liquidity of stock
one year prior price manipulation; a model for forecasting price
manipulation of stocks of companies present in Tehran stock
exchange were designed. At the end the power of forecasting models
were studied by using data of test set. Whereas the power of
forecasting logit model for test set was 92.1%, for artificial neural
network was 94.1% and multi audit analysis model was 90.2%;
therefore all of the 3 aforesaid models has high power to forecast
price manipulation and there is no considerable difference among
forecasting power of these 3 models.
Abstract: In this paper, we focus on the use of knowledge bases
in two different application areas – control of systems with unknown
or strongly nonlinear models (i.e. hardly controllable by the classical
methods), and robot motion planning in eight directions. The first
one deals with fuzzy logic and the paper presents approaches for
setting and aggregating the rules of a knowledge base. Te second one
is concentrated on a case-based reasoning strategy for finding the
path in a planar scene with obstacles.
Abstract: The paper presents a comparative performance of the
models developed to predict 28 days compressive strengths using
neural network techniques for data taken from literature (ANN-I) and
data developed experimentally for SCC containing bottom ash as
partial replacement of fine aggregates (ANN-II). The data used in the
models are arranged in the format of six and eight input parameters
that cover the contents of cement, sand, coarse aggregate, fly ash as
partial replacement of cement, bottom ash as partial replacement of
sand, water and water/powder ratio, superplasticizer dosage and an
output parameter that is 28-days compressive strength and
compressive strengths at 7 days, 28 days, 90 days and 365 days,
respectively for ANN-I and ANN-II. The importance of different
input parameters is also given for predicting the strengths at various
ages using neural network. The model developed from literature data
could be easily extended to the experimental data, with bottom ash as
partial replacement of sand with some modifications.
Abstract: Computer worm detection is commonly performed by
antivirus software tools that rely on prior explicit knowledge of the
worm-s code (detection based on code signatures). We present an
approach for detection of the presence of computer worms based on
Artificial Neural Networks (ANN) using the computer's behavioral
measures. Identification of significant features, which describe the
activity of a worm within a host, is commonly acquired from security
experts. We suggest acquiring these features by applying feature
selection methods. We compare three different feature selection
techniques for the dimensionality reduction and identification of the
most prominent features to capture efficiently the computer behavior
in the context of worm activity. Additionally, we explore three
different temporal representation techniques for the most prominent
features. In order to evaluate the different techniques, several
computers were infected with five different worms and 323 different
features of the infected computers were measured. We evaluated
each technique by preprocessing the dataset according to each one
and training the ANN model with the preprocessed data. We then
evaluated the ability of the model to detect the presence of a new
computer worm, in particular, during heavy user activity on the
infected computers.
Abstract: Analysis of blood vessel mechanics in normal and
diseased conditions is essential for disease research, medical device
design and treatment planning. In this work, 3D finite element
models of normal vessel and atherosclerotic vessel with 50% plaque
deposition were developed. The developed models were meshed
using finite number of tetrahedral elements. The developed models
were simulated using actual blood pressure signals. Based on the
transient analysis performed on the developed models, the parameters
such as total displacement, strain energy density and entropy per unit
volume were obtained. Further, the obtained parameters were used to
develop artificial neural network models for analyzing normal and
atherosclerotic blood vessels. In this paper, the objectives of the
study, methodology and significant observations are presented.
Abstract: Computer networks are essential part in computerbased
information systems. The performance of these networks has a
great influence on the whole information system. Measuring the
usability criteria and customers satisfaction on small computer
network is very important. In this article, an effective approach for
measuring the usability of business network in an information system
is introduced. The usability process for networking provides us with a
flexible and a cost-effective way to assess the usability of a network
and its products. In addition, the proposed approach can be used to
certify network product usability late in the development cycle.
Furthermore, it can be used to help in developing usable interfaces
very early in the cycle and to give a way to measure, track, and
improve usability. Moreover, a new approach for fast information
processing over computer networks is presented. The entire data are
collected together in a long vector and then tested as a one input
pattern. Proposed fast time delay neural networks (FTDNNs) use
cross correlation in the frequency domain between the tested data and
the input weights of neural networks. It is proved mathematically and
practically that the number of computation steps required for the
presented time delay neural networks is less than that needed by
conventional time delay neural networks (CTDNNs). Simulation
results using MATLAB confirm the theoretical computations.
Abstract: In recent years with the rapid development of Internet and the Web, more and more web applications have been deployed in many fields and organizations such as finance, military, and government. Together with that, hackers have found more subtle ways to attack web applications. According to international statistics, SQL Injection is one of the most popular vulnerabilities of web applications. The consequences of this type of attacks are quite dangerous, such as sensitive information could be stolen or authentication systems might be by-passed. To mitigate the situation, several techniques have been adopted. In this research, a security solution is proposed using Artificial Neural Network to protect web applications against this type of attacks. The solution has been experimented on sample datasets and has given promising result. The solution has also been developed in a prototypic web application firewall called ANNbWAF.
Abstract: This paper presents Simulation and experimental
study aimed at investigating the effectiveness of an adaptive artificial
neural network stabilizer on enhancing the damping torque of a
synchronous generator. For this purpose, a power system comprising
a synchronous generator feeding a large power system through a
short tie line is considered. The proposed adaptive neuro-control
system consists of two multi-layered feed forward neural networks,
which work as a plant model identifier and a controller. It generates
supplementary control signals to be utilized by conventional
controllers. The details of the interfacing circuits, sensors and
transducers, which have been designed and built for use in tests, are
presented. The synchronous generator is tested to investigate the
effect of tuning a Power System Stabilizer (PSS) on its dynamic
stability. The obtained simulation and experimental results verify the
basic theoretical concepts.
Abstract: Evolutionary Programming (EP) represents a
methodology of Evolutionary Algorithms (EA) in which mutation is
considered as a main reproduction operator. This paper presents a
novel EP approach for Artificial Neural Networks (ANN) learning.
The proposed strategy consists of two components: the self-adaptive,
which contains phenotype information and the dynamic, which is
described by genotype. Self-adaptation is achieved by the addition of
a value, called the network weight, which depends on a total number
of hidden layers and an average number of neurons in hidden layers.
The dynamic component changes its value depending on the fitness
of a chromosome, exposed to mutation. Thus, the mutation step size
is controlled by two components, encapsulated in the algorithm,
which adjust it according to the characteristics of a predefined ANN
architecture and the fitness of a particular chromosome. The
comparative analysis of the proposed approach and the classical EP
(Gaussian mutation) showed, that that the significant acceleration of
the evolution process is achieved by using both phenotype and
genotype information in the mutation strategy.
Abstract: This paper illustrates the use of a combined neural
network model for classification of electrocardiogram (ECG) beats.
We present a trainable neural network ensemble approach to develop
customized electrocardiogram beat classifier in an effort to further
improve the performance of ECG processing and to offer
individualized health care.
We process a three stage technique for detection of premature
ventricular contraction (PVC) from normal beats and other heart
diseases. This method includes a denoising, a feature extraction and a
classification. At first we investigate the application of stationary
wavelet transform (SWT) for noise reduction of the
electrocardiogram (ECG) signals. Then feature extraction module
extracts 10 ECG morphological features and one timing interval
feature. Then a number of multilayer perceptrons (MLPs) neural
networks with different topologies are designed.
The performance of the different combination methods as well as
the efficiency of the whole system is presented. Among them,
Stacked Generalization as a proposed trainable combined neural
network model possesses the highest recognition rate of around 95%.
Therefore, this network proves to be a suitable candidate in ECG
signal diagnosis systems. ECG samples attributing to the different
ECG beat types were extracted from the MIT-BIH arrhythmia
database for the study.
Abstract: Nowadays, hard disk is one of the most popular storage components. In hard disk industry, the hard disk drive must pass various complex processes and tested systems. In each step, there are some failures. To reduce waste from these failures, we must find the root cause of those failures. Conventionall data analysis method is not effective enough to analyze the large capacity of data. In this paper, we proposed the Hough method for straight line detection that helps to detect straight line defect patterns that occurs in hard disk drive. The proposed method will help to increase more speed and accuracy in failure analysis.
Abstract: An appropriate method for fault identification and classification on extra high voltage transmission line using discrete wavelet transform is proposed in this paper. The sharp variations of the generated short circuit transient signals which are recorded at the sending end of the transmission line are adopted to identify the fault. The threshold values involve fault classification and these are done on the basis of the multiresolution analysis. A comparative study of the performance is also presented for Discrete Fourier Transform (DFT) based Artificial Neural Network (ANN) and Discrete Wavelet Transform (DWT). The results prove that the proposed method is an effective and efficient one in obtaining the accurate result within short duration of time by using Daubechies 4 and 9. Simulation of the power system is done using MATLAB.
Abstract: Determination of nano particle size is substantial since
the nano particle size exerts a significant effect on various properties
of nano materials. Accordingly, proposing non-destructive, accurate
and rapid techniques for this aim is of high interest. There are some
conventional techniques to investigate the morphology and grain size
of nano particles such as scanning electron microscopy (SEM),
atomic force microscopy (AFM) and X-ray diffractometry (XRD).
Vibrational spectroscopy is utilized to characterize different
compounds and applied for evaluation of the average particle size
based on relationship between particle size and near infrared spectra
[1,4] , but it has never been applied in quantitative morphological
analysis of nano materials. So far, the potential application of nearinfrared
(NIR) spectroscopy with its ability in rapid analysis of
powdered materials with minimal sample preparation, has been
suggested for particle size determination of powdered
pharmaceuticals. The relationship between particle size and diffuse
reflectance (DR) spectra in near infrared region has been applied to
introduce a method for estimation of particle size. Back propagation
artificial neural network (BP-ANN) as a nonlinear model was applied
to estimate average particle size based on near infrared diffuse
reflectance spectra. Thirty five different nano TiO2 samples with
different particle size were analyzed by DR-FTNIR spectrometry and
the obtained data were processed by BP- ANN.