Raimy (1999; 2000a; 2000b) proposed a graphical formalism for modeling reduplication, originallymostly focused on phonological overapplication in a derivational framework. This framework is now known as Precedence-based phonology or Multiprecedence p
honology. Raimy's idea is that the segments at the input to the phonology are not totally ordered by precedence. This paper tackles a challenge that arose with Raimy's work, the development of a deterministic serialization algorithm as part of the derivation of surface forms. The Match-Extend algorithm introduced here requires fewer assumptions and sticks tighter to the attested typology. The algorithm also contains no parameter or constraint specific to individual graphs or topologies, unlike previous proposals. Match-Extend requires nothing except knowing the last added set of links.
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
Quantum Computers can be considered the biggest threat against cryptography algorithms especially the public key algorithms. In this seminar, we will discuss Quantum Computers starting from a simple explanation about the physics and mathematics basic
s behind these computers, then explaining why these computers are special in regard of cracking the public key algorithms. Finally, we will describe an example of quantum resistant algorithms.
Mobile Wireless Sensor Network (MWSN) is an emerging technology for attraction of
researchers with its research advantage and various application domains. Due to limited
resources of sensor nodes such as transmission power, communication capability
and
size of memory, data aggregation algorithms are the most practical technique that reduces
large amount of transmission in this network. Security is an important criterion to be
considered because, wireless sensor nodes are deployed in a remote or hostile
environment area that is prone to attacks easily. Therefore, security are essential issue
for MWSN to protect information against attacks.
In this research, we offered an algorithm of secure data aggregation in MWSN based on
pair-wise keys technology and hash function. We studied important parameters such as
execution time, end-to-end delay and number of storied keys. Results showed that
Text Similarity is an important task in several application fields, such as information retrieval, plagiarism detection, machine translation, topic detection, text classification, text summarization and others. Finding similarity between two texts, p
aragraphs or sentences, is based on measuring, directly or indirectly, the similarity between words.
There are two known types of words similarity: lexical and semantic.
The first one handles the words as a stream of characters: words are similar lexically if they share the same characters in the same order.
The second type aims to quantify the degree to which two words are semantically related. As an example they can be, synonyms, represent the same thing or they are used in the same context.
In this article we focus our investigation on measuring the semantic similarity between Arabic sentences using several representations
In this research, a hybrid system was proposed between the
genetic algorithm and the fuzzy Kohonen clustering network ,
where the genetic algorithm is one of the methods of artificial
intelligence is one of the modern methods.
In this paper, it has
merged two techniques of the artificial intelligent, they are the
ants colony optimization algorithm and the genetic algorithm, to
The recurrent reinforcement learning trading system
optimization. The proposed trading system
is based on an ant
colony optimization algorithm and the genetic algorithm to
select an optimal group of technical indicators, and fundamental
indicators.
Conjugate gradient algorithms are important for solving unconstrained optimization
problems, so that we present in this paper conjugate gradient algorithm depending on
improving conjugate coefficient achieving sufficient descent condition and globa
l
convergence by doing hybrid between the two conjugate coefficients [1] and
[2]. Numerical results show the efficiency of the suggested algorithm after its
application on several standard problems and comparing it with other conjugate gradient
algorithms according to number of iterations, function value and norm of gradient vector.
In this research, we developed an algorithm to measure
the quality of processed digital video, with no
information about the video before processing, to know
how much the video was distorted as a result of
processing
This paper presents the possibility of replacing the mathematical
optimizer in the Model Predictive Control Algorithm (MPC) with a
Feedforward Neural Network Optimizer (FNNO). The optimizer
trained offline to reduce the cost function. This maintai
n the system
model of the system, which is essential in MPC to get accepted
accuracy. we solve optimization problem faster than the algorithms
of traditional optimization, which we built, based on digital
computing.
