geological facies interpretation is essential for reservoir studying. the method of classification and identification seismic traces is a powerful approach for geological facies classification and distinction. use of neural networks as classifiers is increasing in different sciences like seismic. they are computer efficient and ideal for patterns identification. they can simply learn new algori...

In this paper, multi step ahead prediction of monthly sunspot real time series are carried out. This series is highly chaotic in nature [7]. This paper compares performance of proposed Jordan Elman Neural Network with TLRNN (Time lag recurrent neural network), and RNN (Recurrent neural network) for multi-step ahead (1, 6, 12, 18, 24) predictions. It is seen that the proposed neural network mode...

A novel Modified PID (MPID) controller is developed to control and minimize the effect of hysteresis in Pneumatic proportional valves. It consists of four parts: a Proportional-Integral-Derivative (PID) controller, a Feedforward term, an Anti-Windup mechanism, and a Bang-Bang controller. The result is a unique Modified PID (MPID) control scheme that demonstrates better command following and dis...

Step response is widely used as the performance index of controlled systems. Thus, the ideal system would be one that has an output which approaches to the step signal quickly without error or over-shoot. However, if the output of an actual plant converges to the reference signal in a very short period, it can be dangerous to the surrounding environment as well as the operators. Furthermore, th...

This paper proposes a two-step approach for characterizing the reservoir properties of the world’s largest non-associated gas reservoir. This approach integrates geological and petrophysical data and compares them with the field performance analysis to achieve a practical electrofacies clustering. Porosity and permeability prediction is done on the basis of linear functions, succeeding the elec...

Yarn tenacity is one of the most important properties in yarn production. This paper addresses modeling of yarn tenacity as well as optimally determining the amounts of the effective inputs to produce yarn with desired tenacity. The artificial neural network is used as a suitable structure for tenacity modeling of cotton yarn with 30 Ne. As the first step for modeling, the empirical data is col...

We consider the situation where a non-linear physical system is identi ed from input-output data. In case no speci c physical structural knowledge about the system is available, parameterized grey-box models cannot be used. Identi cation in black box type of model structures is then the only alternative, and general approaches like neural nets, neuro-fuzzy models, etc., have to be applied. Howe...

In manufacturing processes, maintaining quality is associated with proper process mean parameters and product quality metrics. The early detection of mean changes is important to reduce the number of defectives or non-conformities in the production. In this work, a diverse ensemble of Artificial Neural Networks (ANNs) with a leader network have been used to achieve this purpose, then a performa...

Neural networks are often used as a powerful discriminating estimator for tasks in system identification. This paper describes a neural-network-based method relies on the Radial Basis Function Network (RBF network), for estimating the variable damping factor C (n) and spring constant K (n) of a weighting platform. Firstly, the RBF network learns key properties of the step response of the weight...

the method of artificial neural network is used as a suitable tool for intelligent interpretation of gravity data in this paper. we have designed a hopfield neural network to estimate the gravity source depth. the designed network was tested by both synthetic and real data. as real data, this artificial neural network was used to estimate the depth of a qanat (an underground channel) located at...