Abstract: This paper presents an Artificial Neural Network based approach for short-term load forecasting and exactly for two days ahead. Two seasons have been discussed for Iraqi power system, namely summer and winter; the hourly load demand is the most important input variables for ANN based load forecasting. The recorded daily load profile with a lead time of 1-48 hours for July and December of the year 2012 was obtained from the operation and control center that belongs to the Ministry of Iraqi electricity.
The results of the comparison show that the neural network gives a good prediction for the load forecasting and for two days ahead.
Abstract: Software Effort Estimation is the process of estimating the effort required to develop software. By estimating the effort, the cost and schedule required to estimate the software can be determined. Accurate Estimate helps the developer to allocate the resource accordingly in order to avoid cost overrun and schedule overrun. Several methods are available in order to estimate the effort among which soft computing based method plays a prominent role. Software cost estimation deals with lot of uncertainty among all soft computing methods neural network is good in handling uncertainty. In this paper Radial Basis Function Network is compared with the back propagation network and the results are validated using six data sets and it is found that RBFN is best suitable to estimate the effort. The Results are validated using two tests the error test and the statistical test.
Abstract: This paper is intended to develop an artificial neural network (ANN) based model of material removal rate (MRR) in the turning of ferrous and nonferrous material in a Indian small-scale industry. MRR of the formulated model was proved with the testing data and artificial neural network (ANN) model was developed for the analysis and prediction of the relationship between inputs and output parameters during the turning of ferrous and nonferrous materials. The input parameters of this model are operator, work-piece, cutting process, cutting tool, machine and the environment.
The ANN model consists of a three layered feedforward back propagation neural network. The network is trained with pairs of independent/dependent datasets generated when machining ferrous and nonferrous material. A very good performance of the neural network, in terms of contract with experimental data, was achieved. The model may be used for the testing and forecast of the complex relationship between dependent and the independent parameters in turning operations.
Abstract: Sea level rise threatens to increase the impact of future
storms and hurricanes on coastal communities. Accurate sea level
change prediction and supplement is an important task in determining
constructions and human activities in coastal and oceanic areas. In
this study, support vector machines (SVM) is proposed to predict
daily tidal levels along the Jeddah Coast, Saudi Arabia. The optimal
parameter values of kernel function are determined using a genetic
algorithm. The SVM results are compared with the field data and
with back propagation (BP). Among the models, the SVM is superior
to BPNN and has better generalization performance.
Abstract: Many inherited diseases and non-hereditary disorders are common in the development of renal cystic diseases. Polycystic kidney disease (PKD) is a disorder developed within the kidneys in which grouping of cysts filled with water like fluid. PKD is responsible for 5-10% of end-stage renal failure treated by dialysis or transplantation. New experimental models, application of molecular biology techniques have provided new insights into the pathogenesis of PKD. Researchers are showing keen interest for developing an automated system by applying computer aided techniques for the diagnosis of diseases. In this paper a multilayered feed forward neural network with one hidden layer is constructed, trained and tested by applying back propagation learning rule for the diagnosis of PKD based on physical symptoms and test results of urinalysis collected from the individual patients. The data collected from 50 patients are used to train and test the network. Among these samples, 75% of the data used for training and remaining 25% of the data are used for testing purpose. Further, this trained network is used to implement for new samples. The output results in normality and abnormality of the patient.
Abstract: Learning the gradient of neuron's activity function
like the weight of links causes a new specification which is
flexibility. In flexible neural networks because of supervising and
controlling the operation of neurons, all the burden of the learning is
not dedicated to the weight of links, therefore in each period of
learning of each neuron, in fact the gradient of their activity function,
cooperate in order to achieve the goal of learning thus the number of
learning will be decreased considerably.
Furthermore, learning neurons parameters immunes them against
changing in their inputs and factors which cause such changing.
Likewise initial selecting of weights, type of activity function,
selecting the initial gradient of activity function and selecting a fixed
amount which is multiplied by gradient of error to calculate the
weight changes and gradient of activity function, has a direct affect
in convergence of network for learning.
Abstract: Reliable water level forecasts are particularly
important for warning against dangerous flood and inundation. The
current study aims at investigating the suitability of the adaptive
network based fuzzy inference system for continuous water level
modeling. A hybrid learning algorithm, which combines the least
square method and the back propagation algorithm, is used to
identify the parameters of the network. For this study, water levels
data are available for a hydrological year of 2002 with a sampling
interval of 1-hour. The number of antecedent water level that should
be included in the input variables is determined by two statistical
methods, i.e. autocorrelation function and partial autocorrelation
function between the variables. Forecasting was done for 1-hour until
12-hour ahead in order to compare the models generalization at
higher horizons. The results demonstrate that the adaptive networkbased
fuzzy inference system model can be applied successfully and
provide high accuracy and reliability for river water level estimation.
In general, the adaptive network-based fuzzy inference system
provides accurate and reliable water level prediction for 1-hour ahead
where the MAPE=1.15% and correlation=0.98 was achieved. Up to
12-hour ahead prediction, the model still shows relatively good
performance where the error of prediction resulted was less than
9.65%. The information gathered from the preliminary results
provide a useful guidance or reference for flood early warning
system design in which the magnitude and the timing of a potential
extreme flood are indicated.
Abstract: Artificial Intelligence based gaming is an interesting topic in the state-of-art technology. This paper presents an automation of a tradition Omani game, called Al-Hawalees. Its related issues are resolved and implemented using artificial intelligence approach. An AI approach called mini-max procedure is incorporated to make a diverse budges of the on-line gaming. If number of moves increase, time complexity will be increased in terms of propositionally. In order to tackle the time and space complexities, we have employed a back propagation neural network (BPNN) to train in off-line to make a decision for resources required to fulfill the automation of the game. We have utilized Leverberg- Marquardt training in order to get the rapid response during the gaming. A set of optimal moves is determined by the on-line back propagation training fashioned with alpha-beta pruning. The results and analyses reveal that the proposed scheme will be easily incorporated in the on-line scenario with one player against the system.
Abstract: The quality of short term load forecasting can improve the efficiency of planning and operation of electric utilities. Artificial Neural Networks (ANNs) are employed for nonlinear short term load forecasting owing to their powerful nonlinear mapping capabilities. At present, there is no systematic methodology for optimal design and training of an artificial neural network. One has often to resort to the trial and error approach. This paper describes the process of developing three layer feed-forward large neural networks for short-term load forecasting and then presents a heuristic search algorithm for performing an important task of this process, i.e. optimal networks structure design. Particle Swarm Optimization (PSO) is used to develop the optimum large neural network structure and connecting weights for one-day ahead electric load forecasting problem. PSO is a novel random optimization method based on swarm intelligence, which has more powerful ability of global optimization. Employing PSO algorithms on the design and training of ANNs allows the ANN architecture and parameters to be easily optimized. The proposed method is applied to STLF of the local utility. Data are clustered due to the differences in their characteristics. Special days are extracted from the normal training sets and handled separately. In this way, a solution is provided for all load types, including working days and weekends and special days. The experimental results show that the proposed method optimized by PSO can quicken the learning speed of the network and improve the forecasting precision compared with the conventional Back Propagation (BP) method. Moreover, it is not only simple to calculate, but also practical and effective. Also, it provides a greater degree of accuracy in many cases and gives lower percent errors all the time for STLF problem compared to BP method. Thus, it can be applied to automatically design an optimal load forecaster based on historical data.
Abstract: The dynamics of the Autonomous Underwater
Vehicles (AUVs) are highly nonlinear and time varying and the hydrodynamic coefficients of vehicles are difficult to estimate
accurately because of the variations of these coefficients with
different navigation conditions and external disturbances. This study presents the on-line system identification of AUV dynamics to obtain
the coupled nonlinear dynamic model of AUV as a black box. This black box has an input-output relationship based upon on-line
adaptive fuzzy model and adaptive neural fuzzy network (ANFN)
model techniques to overcome the uncertain external disturbance and
the difficulties of modelling the hydrodynamic forces of the AUVs instead of using the mathematical model with hydrodynamic parameters estimation. The models- parameters are adapted according
to the back propagation algorithm based upon the error between the
identified model and the actual output of the plant. The proposed
ANFN model adopts a functional link neural network (FLNN) as the
consequent part of the fuzzy rules. Thus, the consequent part of the
ANFN model is a nonlinear combination of input variables. Fuzzy
control system is applied to guide and control the AUV using both
adaptive models and mathematical model. Simulation results show
the superiority of the proposed adaptive neural fuzzy network
(ANFN) model in tracking of the behavior of the AUV accurately
even in the presence of noise and disturbance.
Abstract: In this paper we present an off line system for the
recognition of the handwritten numeric chains. Our work is divided
in two big parts. The first part is the realization of a recognition
system of the isolated handwritten digits. In this case the study is
based mainly on the evaluation of neural network performances,
trained with the gradient back propagation algorithm. The used
parameters to form the input vector of the neural network are
extracted on the binary images of the digits by several methods: the
distribution sequence, the Barr features and the centred moments of
the different projections and profiles. The second part is the
extension of our system for the reading of the handwritten numeric
chains constituted of a variable number of digits. The vertical
projection is used to segment the numeric chain at isolated digits and
every digit (or segment) will be presented separately to the entry of
the system achieved in the first part (recognition system of the
isolated handwritten digits). The result of the recognition of the
numeric chain will be displayed at the exit of the global system.
Abstract: Automatic currency note recognition invariably
depends on the currency note characteristics of a particular country
and the extraction of features directly affects the recognition ability.
Sri Lanka has not been involved in any kind of research or
implementation of this kind. The proposed system “SLCRec" comes
up with a solution focusing on minimizing false rejection of notes.
Sri Lankan currency notes undergo severe changes in image quality
in usage. Hence a special linear transformation function is adapted to
wipe out noise patterns from backgrounds without affecting the
notes- characteristic images and re-appear images of interest. The
transformation maps the original gray scale range into a smaller
range of 0 to 125. Applying Edge detection after the transformation
provided better robustness for noise and fair representation of edges
for new and old damaged notes. A three layer back propagation
neural network is presented with the number of edges detected in row
order of the notes and classification is accepted in four classes of
interest which are 100, 500, 1000 and 2000 rupee notes. The
experiments showed good classification results and proved that the
proposed methodology has the capability of separating classes
properly in varying image conditions.
Abstract: Saturated hydraulic conductivity is one of the soil
hydraulic properties which is widely used in environmental studies
especially subsurface ground water. Since, its direct measurement is
time consuming and therefore costly, indirect methods such as
pedotransfer functions have been developed based on multiple linear
regression equations and neural networks model in order to estimate
saturated hydraulic conductivity from readily available soil
properties e.g. sand, silt, and clay contents, bulk density, and organic
matter. The objective of this study was to develop neural networks
(NNs) model to estimate saturated hydraulic conductivity from
available parameters such as sand and clay contents, bulk density,
van Genuchten retention model parameters (i.e. r
θ , α , and n) as well
as effective porosity. We used two methods to calculate effective
porosity: : (1) eff s FC φ =θ -θ , and (2) inf φ =θ -θ eff s , in which s
θ is
saturated water content, FC θ is water content retained at -33 kPa
matric potential, and inf θ is water content at the inflection point.
Total of 311 soil samples from the UNSODA database was divided
into three groups as 187 for the training, 62 for the validation (to
avoid over training), and 62 for the test of NNs model. A commercial
neural network toolbox of MATLAB software with a multi-layer
perceptron model and back propagation algorithm were used for the
training procedure. The statistical parameters such as correlation
coefficient (R2), and mean square error (MSE) were also used to
evaluate the developed NNs model. The best number of neurons in
the middle layer of NNs model for methods (1) and (2) were
calculated 44 and 6, respectively. The R2 and MSE values of the test
phase were determined for method (1), 0.94 and 0.0016, and for
method (2), 0.98 and 0.00065, respectively, which shows that method
(2) estimates saturated hydraulic conductivity better than method (1).
Abstract: Least Development Countries (LDC) like
Bangladesh, whose 25% revenue earning is achieved from Textile
export, requires producing less defective textile for minimizing
production cost and time. Inspection processes done on these
industries are mostly manual and time consuming. To reduce error
on identifying fabric defects requires more automotive and
accurate inspection process. Considering this lacking, this research
implements a Textile Defect Recognizer which uses computer
vision methodology with the combination of multi-layer neural
networks to identify four classifications of textile defects. The
recognizer, suitable for LDC countries, identifies the fabric defects
within economical cost and produces less error prone inspection
system in real time. In order to generate input set for the neural
network, primarily the recognizer captures digital fabric images by
image acquisition device and converts the RGB images into binary
images by restoration process and local threshold techniques.
Later, the output of the processed image, the area of the faulty
portion, the number of objects of the image and the sharp factor of
the image, are feed backed as an input layer to the neural network
which uses back propagation algorithm to compute the weighted
factors and generates the desired classifications of defects as an
output.
Abstract: Investigation of soil properties like Cation Exchange
Capacity (CEC) plays important roles in study of environmental
reaserches as the spatial and temporal variability of this property
have been led to development of indirect methods in estimation of
this soil characteristic. Pedotransfer functions (PTFs) provide an
alternative by estimating soil parameters from more readily available
soil data. 70 soil samples were collected from different horizons of
15 soil profiles located in the Ziaran region, Qazvin province, Iran.
Then, multivariate regression and neural network model (feedforward
back propagation network) were employed to develop a
pedotransfer function for predicting soil parameter using easily
measurable characteristics of clay and organic carbon. The
performance of the multivariate regression and neural network model
was evaluated using a test data set. In order to evaluate the models,
root mean square error (RMSE) was used. The value of RMSE and
R2 derived by ANN model for CEC were 0.47 and 0.94 respectively,
while these parameters for multivariate regression model were 0.65
and 0.88 respectively. Results showed that artificial neural network
with seven neurons in hidden layer had better performance in
predicting soil cation exchange capacity than multivariate regression.
Abstract: Horizontal wells are proven to be better producers
because they can be extended for a long distance in the pay zone.
Engineers have the technical means to forecast the well productivity
for a given horizontal length. However, experiences have shown that
the actual production rate is often significantly less than that of
forecasted. It is a difficult task, if not impossible to identify the real
reason why a horizontal well is not producing what was forecasted.
Often the source of problem lies in the drilling of horizontal section
such as permeability reduction in the pay zone due to mud invasion
or snaky well patterns created during drilling. Although drillers aim
to drill a constant inclination hole in the pay zone, the more frequent
outcome is a sinusoidal wellbore trajectory. The two factors, which
play an important role in wellbore tortuosity, are the inclination and
side force at bit. A constant inclination horizontal well can only be
drilled if the bit face is maintained perpendicular to longitudinal axis
of bottom hole assembly (BHA) while keeping the side force nil at
the bit. This approach assumes that there exists no formation force at
bit. Hence, an appropriate BHA can be designed if bit side force and
bit tilt are determined accurately. The Artificial Neural Network
(ANN) is superior to existing analytical techniques. In this study, the
neural networks have been employed as a general approximation tool
for estimation of the bit side forces. A number of samples are
analyzed with ANN for parameters of bit side force and the results
are compared with exact analysis. Back Propagation Neural network
(BPN) is used to approximation of bit side forces. Resultant low
relative error value of the test indicates the usability of the BPN in
this area.
Abstract: The paper presents a new hybridization methodology involving Neural, Fuzzy and Rough Computing. A Rough Sets based approximation technique has been proposed based on a certain Neuro – Fuzzy architecture. A New Rough Neuron composition consisting of a combination of a Lower Bound neuron and a Boundary neuron has also been described. The conventional convergence of error in back propagation has been given away for a new framework based on 'Output Excitation Factor' and an inverse input transfer function. The paper also presents a brief comparison of performances, of the existing Rough Neural Networks and ANFIS architecture against the proposed methodology. It can be observed that the rough approximation based neuro-fuzzy architecture is superior to its counterparts.
Abstract: Crypto System Identification is one of the challenging tasks in Crypt analysis. The paper discusses the possibility of employing Neural Networks for identification of Cipher Systems from cipher texts. Cascade Correlation Neural Network and Back Propagation Network have been employed for identification of Cipher Systems. Very large collection of cipher texts were generated using a Block Cipher (Enhanced RC6) and a Stream Cipher (SEAL). Promising results were obtained in terms of accuracy using both the Neural Network models but it was observed that the Cascade Correlation Neural Network Model performed better compared to Back Propagation Network.
Abstract: In this paper, a comparative study of application of
supervised and unsupervised learning algorithms on illumination
invariant face recognition has been carried out. The supervised
learning has been carried out with the help of using a bi-layered
artificial neural network having one input, two hidden and one output
layer. The gradient descent with momentum and adaptive learning
rate back propagation learning algorithm has been used to implement
the supervised learning in a way that both the inputs and
corresponding outputs are provided at the time of training the
network, thus here is an inherent clustering and optimized learning of
weights which provide us with efficient results.. The unsupervised
learning has been implemented with the help of a modified
Counterpropagation network. The Counterpropagation network
involves the process of clustering followed by application of Outstar
rule to obtain the recognized face. The face recognition system has
been developed for recognizing faces which have varying
illumination intensities, where the database images vary in lighting
with respect to angle of illumination with horizontal and vertical
planes. The supervised and unsupervised learning algorithms have
been implemented and have been tested exhaustively, with and
without application of histogram equalization to get efficient results.
Abstract: COSMED K4b2 is a portable electrical device designed to test pulmonary functions. It is ideal for many applications that need the measurement of the cardio-respiratory response either in the field or in the lab is capable with the capability to delivery real time data to a sink node or a PC base station with storing data in the memory at the same time. But the actual sensor outputs and data received may contain some errors, such as impulsive noise which can be related to sensors, low batteries, environment or disturbance in data acquisition process. These abnormal outputs might cause misinterpretations of exercise or living activities to persons being monitored. In our paper we propose an effective and feasible method to detect and identify errors in applications by principal component analysis (PCA) and a back propagation (BP) neural network.