This research deals with the modeling of a Multi-Layers Feed Forward Artificial Neural
Networks (MLFFNN), trained using Gradient Descent algorithm with Momentum factor &
adaptive learning rate, to estimate the output of the neural network correspon
ding to the
optimal Duty Cycle of DC-DC Boost Converter to track the Maximum Power Point of
Photovoltaic Energy Systems. Thus, the DMPPT-ANN “Developed MPPT-ANN”
controller proposed in this research, independent in his work on the use of electrical
measurements output of PV system to determine the duty cycle, and without the need to
use a Proportional-Integrative Controller to control the cycle of the work of the of DC-DC
Boost Converter, and this improves the dynamic performance of the proposed controller to
determine the optimal Duty Cycle accurately and quickly. In this context, this research
discusses the optimal selection of the proposed MLFFNN structure in the research in terms
of determining the optimum number of hidden layers and the optimal number of neurons in
them, evaluating the values of the Mean square error and the resulting Correlation
Coefficient after each training of the neural network. The final network model with the
optimal structure is then adopted to form the DMPPT-ANN Controller to track the MPP
point of the PV system. The simulation results performed in the Matlab / Simulink
environment demonstrated the best performance of the proposed DMPPT-ANN controller
based on the MLFFNN neural network model, by accurately estimating the Duty Cycle and
improving the response speed of the PV system output to MPP access, , as well as finally
eliminating the resulting oscillations in the steady state of the Power response curve of PV
system compared with the use of a number of reference controls: an advanced tracking
controller MPPT-ANN-PI based on ANN network to estimate MPP point voltage with
conventional PI controller, a MPPT-FLC and a conventional MPPT-INC uses the
Incremental Conductance technique INC
In present investigation attempt has been made to study the bearing capacity and settlement
characteristics of footings subjected to central vertical load and resting on layered soil with
the help of model tests and with the application of finite e
lement method (FEM) to
calculate bearing capacity of a strip footing on one-layer and two-layer soil (Sand and
Clay). To investigate the effect of various parameters on soil bearing Capacity a
commercial finite element software, PLAXIS, has been used. Soil profile contains two soil
types including sand and clay. Soil behavior is represented by the elasto-plastic Mohrcoulomb
(MC) -model. For a one-layer case, the bearing capacity also is calculated which
has a good agreement with theoretical equations. For a layered soil, soft-over strong soil,
parametric study was carried out. It is concluded that the bearing capacity of footing
decreases as the height of clayey soil increases whilst the displacement under footing
increases. There is a critical depth where the stronger bottom layer does not affect ultimate
bearing capacity and failure mechanism of footing.
The contribution of our research include building an artificial neural
network in MATLAB program environment and improvement of
maximum loading point algorithm, to compute the most critical
voltage stability margin, for on-line voltage stability a
ssessment,
and a method to approximate the most critical voltage stability
margin accurately. a method to create a (ARTIFICIAL NEURAL
NETWORK) approach.
The aim of the work is to improve the performance of the WLD
descriptor using Gabor filters in a preprocessing stage. The
performance of the improved descriptor will be compared with the
performance of the LBP descriptor(a widely used descriptor i
n FER
researches). This performance will be achieved using the extremely
used expert system SVM besides the expert systems CSD and MLP.
التعرف على تعابير الوجه
النماذج الثنائية المحلية
واصف ويبر المحلي
نماذج غير المحلية الثنائية
واصف ويبر-غيبر المحلي
مرشح غيبر
الشبكة العصبونية متعددة الطبقات
أداة الأشعة الداعمة
مسافة تشي التربيعية
FER-Facial Expression Recognition
LBP-Local Binary Pattern
WLD-Weber Local Descriptor
LGBP-Local Gabor Binary Pattern
WGLD-Weber Gabor Local Descriptor. Gabor filter
MLP-Multi Layer Perceptron
SVM- Support Vector Machine
CSD- Chi squared distance
المزيد..