Accurate estimating and predicting of hydrological phenomena plays an influential role in the development and management of water resources, preparing of future plans according to different scenarios of climate changes. Evapotranspiration is one of t
he major meteorological components of the hydrologic cycle and from the most complex of them, and the accurate prediction of this parameter is very important for many water resources applications.
So, this research goals to prediction of monthly reference evapotranspiration (ET0) at Homs meteostation, in the middle of Syrian Arab Republic, using Artificial Neural Networks (ANNs), and Fuzzy Inference System (FIS), depending on available climatic data, and comparision between the results of these models.
The used data contained 347 monthly values of Air Temperature (T), Relative Humidity (RH), Wind Speed (WS) and Sunshine Hours (SS) (from October 1974 to December 2004). The monthly reference evapotranspiration data were estimated by the Penman Monteith method, which is the proposed method by Food and Agriculture Organization of the United Nations (FAO) as the standard method for the estimation of ET0, and used as outputs of the models.
The results of this study showed that feed forward back propagation Artificial Neural Networks (FFBP-ANNs) pridected successfully the monthly ET0 using climatic data, with low values of root mean square errors (RMSE), and high values of correlation coefficients (R), and showed that the using of the monthly index as an additional input, improves the accurate of prediction of the artificial neural networks models.
Also, the results showed good ability of Fuzzy Inference Models (FIS) in predicting of monthly reference evapotranspiration. Sunshine hours are the most influential single parameter for ET0 prediction (R= 97.71%, RMSE = 18.08 mm/month) during the test period, sunshine hours and wind speed are the most influential optimal combination of two parameters
(R= 98.55%, RMSE = 12.49 mm/month) during the test period.
The results showed high reliability for each of the artificial neural networks and fuzzy inference system with a little preference for artificial neural networks which can add the monthly index in the input layer, and there for improve the presicion of predictions.
This study recommends the using of artificial intelligence techniques in modeling of complex and nonlinear phenomena which related of water resources.
It is found in this research to adopt a new classifier for diagnosing
Cardiac Arrhythmias depending on detecting the Electrocardiograph (ECG),
where the classifier can identify heart beats and extract its features. Using
these features we can deci
de if the heart beat is healthy or disordered.
Beside detection normal heart beats, the research focused on detection
two diseases:
1. Premature Ventricular Contraction PVC.
2. Premature Atrial Contraction PAC.
The new classifier diagnosed the two diseases with a very high quality
where the accuracy average is 97.56%.
The new classifier is developed depending on algorithms of ANFIS
Adaptive Neural Fuzzy Inference System. System includes two consecutive
neural networks; first one sorts the heart beats to two types: normal and
abnormal were the second diagnose the disease of the disordered heartbeats
only.
This new classifier offered higher levels of efficiency and accuracy in
the comparison with the internationally known classifiers.
Evapotranspiration is an important component of the
hydrologic cycle, and the accurate prediction of this parameter is
very important for many water resources applications. Thus, the
aim of this study is prediction of monthly reference
evapotranspiration using Artificial Neural Networks (ANNs) and
fuzzy inference system (FIS).
An ANFIS controller also designed and a comparison
between proposed controller, ANFIS controller and open loop model
had made with different types of disturbance.
Suspension system is considered one of the most important components of modern
automobiles as it is the responsible for the vehicle’s stability, balance and safety. The
presence of robust controller is very necessary in order to ensure full interac
tion between
suspension components and making accurate decisions at the right time. This paper
proposes to design an Extended Adaptive Neuro Fuzzy Inference System (EANFIS)
controller for suspension system in quarter car model. The proposed controller is used as
decision maker In order to contribute in absorbing shocks caused by bumpy roads, and to
prevent vibrations from reaching the cockpit. Furthermore, it provides stability and
coherence required to reduce the discomfort felt by passengers, which arises from road
roughness, which in turn, improve the road handling. The MATLAB Simulink is used to
simulate the proposed controller with the controlled model and to display the responses of
the controlled model under different types of disturbance. In addition, a comparison
between EANFIS controller, Fuzzy controller and open loop model (passive suspension)
was done with different types of disturbance on order to evaluate the performance of the
proposed model. Controller has shown excelled performance in terms of reducing
displacements, velocity and acceleration.
The entry of computer to many areas, such as medical field, led to develop new
technique that has led to the prosperity of these areas, and helped doctors to detect and
diagnose diseases accurately and credibility, where the experience of the docto
r in addition
to the accuracy of computer lead to access to the credibility of high patient and save
human lives.
A new approach for cardiac diseases detection and classification in ECG signals
images is proposed using Adaptive Neuro Fuzzy Inference System ANFIS.
The proposed approach is applied on database containing (147) ECG images,
each of them accompanied with its medical report. The medical reports were used to
validate the detection and classification.
The proposed method achieved a relatively high accuracy (97%) in detection and
classification processes.
The proposed approach is developed using MATLAB, and based on its libraries,
image processing, neural network and fuzzy logic.
The robotic manipulator's control process involves many
engineering challenges from mechanical design phase to the phase
of programming. The inverse kinematics problem is one of the most
difficult challenges, as it requires determining the angles
of joints
for a desired position of the end-effector, the difficulty of this
problem comes from the none linearity and the possibility of
multiple solutions or lack of solutions in some cases. Many
solutions were proposed to solve the issue of inverse kinematics;
analytically and numerically in addition to the solutions which
based on artificial intelligence. In this research the solution of
inverse kinematics using Adaptive Neuro-Fuzzy Inference System
was discussed and amendments were proposed and indicated their
usefulness.
