Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : ANN

Reservoir Operation by Artificial Neural Network Model ( Mosul Dam –Iraq, as a Case Study)

Thair S.K; Ayad S. M; Hasan H.M

Engineering and Technology Journal, 2015, Volume 33, Issue 7A, Pages 1697-1714
DOI: 10.30684/etj.2015.106873

Reservoir operation forecasting plays an important role in managing water resources systems. Artificial Neural Network (ANN) model was applied for Mosul-Dam reservoir which is located on Tigris River, which the objectives of water resources development and flood control. Feed-forward multi-layer perceptions (MLPs) are used and trained with the back-propagation algorithm, as they have a high capability of data mapping. The data set has a period of 23 years from 1990 to 2012..The Input data were inflow (It), evaporation (Et), rainfall (Rt), reservoir storage (St) and outflow (Ot). The best convergence after more than 1000 trials was achieved for the combination of inflow (It), inflow (It-1), inflow (It-2), evaporation (Et), reservoir storage (St), rainfall (Rt), outflow (Ot-1) and outflow (Ot-2) with error tolerance, learning rate, momentum rate, number of cycles and number of hidden layers as 0.001, 1, 0.9,50000 and 9 respectively. The coefficient of determination (R2) and MAPE were (0.972) and (17.15) respectively. The results of ANN models for the training, testing and validation were compared with the observed data. The predicted values from the neural networks matched the measured values very well. The application of ANN technique and the predicted equation by using the connection weights and the threshold levels, assist the reservoir operation decision and future updating, also it is an important Model for finding the missing data. The ANN technique can accurately predict the monthly Outflow.

A Proposed System for Sound Retrieval Using MAS and ANN

Abeer Tariq; Ikhlas khalaf; shatha habeeb

Engineering and Technology Journal, 2012, Volume 30, Issue 14, Pages 2480-2492

As the use of sounds for computer interfaces, electronic equipment and
multimedia contents, has increased, the role of sound design tools has become
more important. In sound retrieval, picking one sound out from huge data is
troublesome for users because of the difficulty of simultaneously listening to plural
sounds and sometimes there are difficulties with speech and sound recognition.
Consequently, an efficient retrieval method is required for sound databases.
This research proposes a system aim to deal with sound retrieval in both two
cases: authenticity and normal. In the first case, authenticity, two algorithms has
been develop one for building the authentication database and the second deal with
user sound sample to retrieve the matched authenticated samples. In the second
case normal we develop algorithm to deal with user sound sample to retrieve all the
matched samples. Many techniques used in this proposed system such as Artificial
Neural Network (ANN), Data Encryption Standard (DES), Multi Agent System
(MAS) and Fourier transformation (FT). Using these combinations of advanced
and adaptive techniques supports the system to be reliable, secure and parallel.

ANN Modified Design Model to Adjust Field Current of D.C. Motor

Ahlam Luaibi Shuraiji; Suad Khairi Mohammed; Alia Jasim Mohammed

Engineering and Technology Journal, 2010, Volume 28, Issue 11, Pages 2132-2142

This work is concerned with designing an adjusted field current of D.C.
motors to obtain constant speed, based on ANN. The design is employed by using
training model with supervised manner with back-propagation algorithm.
MATLAB neural network tool box is used for training purpose.
The feed-forward neural network (FFNN) and learning capabilities offers a
promising way to solve the problem of system non-linearity, parameter variations
on unexpected load excisions associated with D.C. motor drive system.
The proposed ANN controller model is implemented with a control dc motor
drive system in the laboratory. The laboratory test results validate the efficacy of
the based controller model for a high performance dc motor drive.