Abstract: The overall objective of this paper is to retrieve soil
surfaces parameters namely, roughness and soil moisture related to
the dielectric constant by inverting the radar backscattered signal
from natural soil surfaces.
Because the classical description of roughness using statistical
parameters like the correlation length doesn't lead to satisfactory
results to predict radar backscattering, we used a multi-scale
roughness description using the wavelet transform and the Mallat
algorithm. In this description, the surface is considered as a
superposition of a finite number of one-dimensional Gaussian
processes each having a spatial scale. A second step in this study
consisted in adapting a direct model simulating radar backscattering
namely the small perturbation model to this multi-scale surface
description. We investigated the impact of this description on radar
backscattering through a sensitivity analysis of backscattering
coefficient to the multi-scale roughness parameters.
To perform the inversion of the small perturbation multi-scale
scattering model (MLS SPM) we used a multi-layer neural network
architecture trained by backpropagation learning rule. The inversion
leads to satisfactory results with a relative uncertainty of 8%.
Abstract: This article presents the development of a neural
network cognitive model for the classification and detection of
different frequency signals. The basic structure of the implemented
neural network was inspired on the perception process that humans
generally make in order to visually distinguish between high and low
frequency signals. It is based on the dynamic neural network concept,
with delays. A special two-layer feedforward neural net structure was
successfully implemented, trained and validated, to achieve
minimum target error. Training confirmed that this neural net
structure descents and converges to a human perception classification
solution, even when far away from the target.
Abstract: Signature represents an individual characteristic of a
person which can be used for his / her validation. For such application
proper modeling is essential. Here we propose an offline signature
recognition and verification scheme which is based on extraction of
several features including one hybrid set from the input signature
and compare them with the already trained forms. Feature points
are classified using statistical parameters like mean and variance.
The scanned signature is normalized in slant using a very simple
algorithm with an intention to make the system robust which is
found to be very helpful. The slant correction is further aided by the
use of an Artificial Neural Network (ANN). The suggested scheme
discriminates between originals and forged signatures from simple
and random forgeries. The primary objective is to reduce the two
crucial parameters-False Acceptance Rate (FAR) and False Rejection
Rate (FRR) with lesser training time with an intension to make the
system dynamic using a cluster of ANNs forming a multiple classifier
system.
Abstract: Crude oil blending is an important unit operation in
petroleum refining industry. A good model for the blending system is
beneficial for supervision operation, prediction of the export
petroleum quality and realizing model-based optimal control. Since
the blending cannot follow the ideal mixing rule in practice, we
propose a static neural network to approximate the blending
properties. By the dead-zone approach, we propose a new robust
learning algorithm and give theoretical analysis. Real data of crude
oil blending is applied to illustrate the neuro modeling approach.
Abstract: In a pilot plant scale of a fluidized bed reactor, a
reduction reaction of sodium sulfate by natural gas has been
investigated. Natural gas is applied in this study as a reductant. Feed
density, feed mass flow rate, natural gas and air flow rate
(independent parameters)and temperature of bed and CO
concentration in inlet and outlet of reactor (dependent parameters)
were monitored and recorded at steady state. The residence time was
adjusted close to value of traditional reaction [1]. An artificial neural
network (ANN) was established to study dependency of yield and
carbon gradient on operating parameters. Resultant 97% accuracy of
applied ANN is a good prove that natural gas can be used as a
reducing agent. Predicted ANN model for relation between other
sources carbon gradient (accuracy 74%) indicates there is not a
meaningful relation between other sources carbon variation and
reduction process which means carbon in granule does not have
significant effect on the reaction yield.
Abstract: Young patients suffering from Cerebral Palsy are
facing difficult choices concerning heavy surgeries. Diagnosis settled
by surgeons can be complex and on the other hand decision for
patient about getting or not such a surgery involves important
reflection effort. Proposed software combining prediction for
surgeries and post surgery kinematic values, and from 3D model
representing the patient is an innovative tool helpful for both patients
and medicine professionals. Beginning with analysis and
classification of kinematics values from Data Base extracted from
gait analysis in 3 separated clusters, it is possible to determine close
similarity between patients. Prediction surgery best adapted to
improve a patient gait is then determined by operating a suitable
preconditioned neural network. Finally, patient 3D modeling based
on kinematic values analysis, is animated thanks to post surgery
kinematic vectors characterizing the closest patient selected from
patients clustering.
Abstract: Simultaneous determination of multicomponents of phenol, resorcinol and catechol with a chemometric technique a PCranking artificial neural network (PCranking-ANN) algorithm is reported in this study. Based on the data correlation coefficient method, 3 representative PCs are selected from the scores of original UV spectral data (35 PCs) as the original input patterns for ANN to build a neural network model. The results obtained by iterating 8000 .The RMSEP for phenol, resorcinol and catechol with PCranking- ANN were 0.6680, 0.0766 and 0.1033, respectively. Calibration matrices were 0.50-21.0, 0.50-15.1 and 0.50-20.0 μg ml-1 for phenol, resorcinol and catechol, respectively. The proposed method was successfully applied for the determination of phenol, resorcinol and catechol in synthetic and water samples.
Abstract: The objective of this paper is to develop a neural
network-based residual generator to detect the fault in the actuators
for a specific communication satellite in its attitude control system
(ACS). First, a dynamic multilayer perceptron network with dynamic
neurons is used, those neurons correspond a second order linear
Infinite Impulse Response (IIR) filter and a nonlinear activation
function with adjustable parameters. Second, the parameters from the
network are adjusted to minimize a performance index specified by
the output estimated error, with the given input-output data collected
from the specific ACS. Then, the proposed dynamic neural network
is trained and applied for detecting the faults injected to the wheel,
which is the main actuator in the normal mode for the communication
satellite. Then the performance and capabilities of the proposed
network were tested and compared with a conventional model-based
observer residual, showing the differences between these two
methods, and indicating the benefit of the proposed algorithm to
know the real status of the momentum wheel. Finally, the application
of the methods in a satellite ground station is discussed.
Abstract: In this paper, based on almost periodic functional hull theory and M-matrix theory, some sufficient conditions are established for the existence and uniqueness of positive almost periodic solution for a class of BAM neural networks with time-varying delays. An example is given to illustrate the main results.
Abstract: Fuel cells have become one of the major areas of
research in the academia and the industry. The goal of most fish
farmers is to maximize production and profits while holding labor
and management efforts to the minimum. Risk of fish kills, disease
outbreaks, poor water quality in most pond culture operations,
aeration offers the most immediate and practical solution to water
quality problems encountered at higher stocking and feeding rates.
Many units of aeration system are electrical units so using a
continuous, high reliability, affordable, and environmentally friendly
power sources is necessary. Aeration of water by using PEM fuel cell
power is not only a new application of the renewable energy, but
also, it provides an affordable method to promote biodiversity in
stagnant ponds and lakes. This paper presents a new design and
control of PEM fuel cell powered a diffused air aeration system for a
shrimp farm in Mersa Matruh in Egypt. Also Artificial intelligence
(AI) techniques control is used to control the fuel cell output power
by control input gases flow rate. Moreover the mathematical
modeling and simulation of PEM fuel cell is introduced. A
comparison study is applied between the performance of fuzzy logic
control (FLC) and neural network control (NNC). The results show
the effectiveness of NNC over FLC.
Abstract: In this paper an algorithm based on the adaptive
neuro-fuzzy controller is provided to enhance the tipover stability of
mobile manipulators when they are subjected to predefined
trajectories for the end-effector and the vehicle. The controller
creates proper configurations for the manipulator to prevent the robot
from being overturned. The optimal configuration and thus the most
favorable control are obtained through soft computing approaches
including a combination of genetic algorithm, neural networks, and
fuzzy logic. The proposed algorithm, in this paper, is that a look-up
table is designed by employing the obtained values from the genetic
algorithm in order to minimize the performance index and by using
this data base, rule bases are designed for the ANFIS controller and
will be exerted on the actuators to enhance the tipover stability of the
mobile manipulator. A numerical example is presented to
demonstrate the effectiveness of the proposed algorithm.
Abstract: Gabor-based face representation has achieved enormous success in face recognition. This paper addresses a novel algorithm for face recognition using neural networks trained by Gabor features. The system is commenced on convolving a face image with a series of Gabor filter coefficients at different scales and orientations. Two novel contributions of this paper are: scaling of rms contrast and introduction of fuzzily skewed filter. The neural network employed for face recognition is based on the multilayer perceptron (MLP) architecture with backpropagation algorithm and incorporates the convolution filter response of Gabor jet. The effectiveness of the algorithm has been justified over a face database with images captured at different illumination conditions.
Abstract: Classification of electroencephalogram (EEG) signals
extracted during mental tasks is a technique that is actively pursued
for Brain Computer Interfaces (BCI) designs. In this paper, we
compared the classification performances of univariateautoregressive
(AR) and multivariate autoregressive (MAR) models
for representing EEG signals that were extracted during different
mental tasks. Multilayer Perceptron (MLP) neural network (NN)
trained by the backpropagation (BP) algorithm was used to classify
these features into the different categories representing the mental
tasks. Classification performances were also compared across
different mental task combinations and 2 sets of hidden units (HU): 2
to 10 HU in steps of 2 and 20 to 100 HU in steps of 20. Five different
mental tasks from 4 subjects were used in the experimental study and
combinations of 2 different mental tasks were studied for each
subject. Three different feature extraction methods with 6th order
were used to extract features from these EEG signals: AR
coefficients computed with Burg-s algorithm (ARBG), AR
coefficients computed with stepwise least square algorithm (ARLS)
and MAR coefficients computed with stepwise least square
algorithm. The best results were obtained with 20 to 100 HU using
ARBG. It is concluded that i) it is important to choose the suitable
mental tasks for different individuals for a successful BCI design, ii)
higher HU are more suitable and iii) ARBG is the most suitable
feature extraction method.
Abstract: This paper presents a subjective job scheduler based
on a 3-layer Backpropagation Neural Network (BPNN) and a greedy
alignment procedure in order formulates a real-life situation. The
BPNN estimates critical values of jobs based on the given subjective
criteria. The scheduler is formulated in such a way that, at each time
period, the most critical job is selected from the job queue and is
transferred into a single machine before the next periodic job arrives.
If the selected job is one of the oldest jobs in the queue and its
deadline is less than that of the arrival time of the current job, then
there is an update of the deadline of the job is assigned in order to
prevent the critical job from its elimination. The proposed
satisfiability criteria indicates that the satisfaction of the scheduler
with respect to performance of the BPNN, validity of the jobs and the
feasibility of the scheduler.
Abstract: Since the presentation of the backpropagation algorithm, a vast variety of improvements of the technique for training a feed forward neural networks have been proposed. This article focuses on two classes of acceleration techniques, one is known as Local Adaptive Techniques that are based on weightspecific only, such as the temporal behavior of the partial derivative of the current weight. The other, known as Dynamic Adaptation Methods, which dynamically adapts the momentum factors, α, and learning rate, η, with respect to the iteration number or gradient. Some of most popular learning algorithms are described. These techniques have been implemented and tested on several problems and measured in terms of gradient and error function evaluation, and percentage of success. Numerical evidence shows that these techniques improve the convergence of the Backpropagation algorithm.
Abstract: Accurate demand forecasting is one of the most key
issues in inventory management of spare parts. The problem of
modeling future consumption becomes especially difficult for lumpy
patterns, which characterized by intervals in which there is no
demand and, periods with actual demand occurrences with large
variation in demand levels. However, many of the forecasting
methods may perform poorly when demand for an item is lumpy.
In this study based on the characteristic of lumpy demand patterns
of spare parts a hybrid forecasting approach has been developed,
which use a multi-layered perceptron neural network and a
traditional recursive method for forecasting future demands. In the
described approach the multi-layered perceptron are adapted to
forecast occurrences of non-zero demands, and then a conventional
recursive method is used to estimate the quantity of non-zero
demands. In order to evaluate the performance of the proposed
approach, their forecasts were compared to those obtained by using
Syntetos & Boylan approximation, recently employed multi-layered
perceptron neural network, generalized regression neural network
and elman recurrent neural network in this area. The models were
applied to forecast future demand of spare parts of Arak
Petrochemical Company in Iran, using 30 types of real data sets. The
results indicate that the forecasts obtained by using our proposed
mode are superior to those obtained by using other methods.
Abstract: This paper presents an approach for the determination of the optimal cutting parameters (spindle speed, feed rate, depth of cut and engagement) leading to minimum surface roughness in face milling of high silicon stainless steel by coupling neural network (NN) and Electromagnetism-like Algorithm (EM). In this regard, the advantages of statistical experimental design technique, experimental measurements, artificial neural network, and Electromagnetism-like optimization method are exploited in an integrated manner. To this end, numerous experiments on this stainless steel were conducted to obtain surface roughness values. A predictive model for surface roughness is created by using a back propogation neural network, then the optimization problem was solved by using EM optimization. Additional experiments were performed to validate optimum surface roughness value predicted by EM algorithm. It is clearly seen that a good agreement is observed between the predicted values by EM coupled with feed forward neural network and experimental measurements. The obtained results show that the EM algorithm coupled with back propogation neural network is an efficient and accurate method in approaching the global minimum of surface roughness in face milling.
Abstract: SVM ( Support Vector Machine ) is a new method in the artificial neural network ( ANN ). In the steel making, how to use computer to predict the end point of BOF accuracy is a great problem. A lot of method and theory have been claimed, but most of the results is not satisfied. Now the hot topic in the BOF end point predicting is to use optical way the predict the end point in the BOF. And we found that there exist some regular in the characteristic curve of the flame from the mouse of pudding. And we can use SVM to predict end point of the BOF, just single spectrum intensity should be required as the input parameter. Moreover, its compatibility for the input space is better than the BP network.
Abstract: The conjugate gradient optimization algorithm is combined with the modified back propagation algorithm to yield a computationally efficient algorithm for training multilayer perceptron (MLP) networks (CGFR/AG). The computational efficiency is enhanced by adaptively modifying initial search direction as described in the following steps: (1) Modification on standard back propagation algorithm by introducing a gain variation term in the activation function, (2) Calculation of the gradient descent of error with respect to the weights and gains values and (3) the determination of a new search direction by using information calculated in step (2). The performance of the proposed method is demonstrated by comparing accuracy and computation time with the conjugate gradient algorithm used in MATLAB neural network toolbox. The results show that the computational efficiency of the proposed method was better than the standard conjugate gradient algorithm.
Abstract: A Novel fuzzy neural network combining with support vector learning mechanism called support-vector-based fuzzy neural networks (SVBFNN) is proposed. The SVBFNN combine the capability of minimizing the empirical risk (training error) and expected risk (testing error) of support vector learning in high dimensional data spaces and the efficient human-like reasoning of FNN.