In this paper, a subspace identification method for bilinear systems is used . Wherein a " three-block " and " four-block " subspace algorithms are used. In this algorithms the input signal to the system does not have to be white . Simulation of these algorithms shows that the " four-block " gives fast convergence and the dimensions of the matrices involved are significantly smaller so that the computational complexity is lower as a comparison with " three-block " algorithm .

The Political loyalties of the individual considered as the most important democracies through direct psychological identification in a particular party. The political parties regarded as the important elements and the foundations of the democratic system. They have effective interaction between the voters and the government institutions. The aim of the current research is to identify the quality of Islamic, the Civilian parties, and the most preferred for students. also, the research attempt to identify the level of identification  party that the  university students have, and the difference of identification party  according to the gender (male, female), the difference of of social class (upper, middle, poor). The sample

Abstract :

The Aims of this research is to describe the concept of risk, its type and method of measurement, and to clarify the impact of these risks on the expected cash flow statement and the preparation of the target cash flow statement that takes these risks into consideration. Because the local economic environment is exposed to many risks, Therefore, this list will be predictive, which will help the economic unit to make administrative decisions, especially decisions related to operational, investment and financing activities. Therefore, the research problem is based on the fact that most of the local economic units are the list of flows According to the actual basis and not according to the discretionary basis (bud

A three-stage learning algorithm for deep multilayer perceptron (DMLP) with effective weight initialisation based on sparse auto-encoder is proposed in this paper, which aims to overcome difficulties in training deep neural networks with limited training data in high-dimensional feature space. At the first stage, unsupervised learning is adopted using sparse auto-encoder to obtain the initial weights of the feature extraction layers of the DMLP. At the second stage, error back-propagation is used to train the DMLP by fixing the weights obtained at the first stage for its feature extraction layers. At the third stage, all the weights of the DMLP obtained at the second stage are refined by error back-propagation. Network structures an

In this research the results of applying Artificial Neural Networks with modified activation function to
perform the online and offline identification of four Degrees of Freedom (4-DOF) Selective Compliance
Assembly Robot Arm (SCARA) manipulator robot will be described. The proposed model of
identification strategy consists of a feed-forward neural network with a modified activation function that
operates in parallel with the SCARA robot model. Feed-Forward Neural Networks (FFNN) which have
been trained online and offline have been used, without requiring any previous knowledge about the
system to be identified. The activation function that is used in the hidden layer in FFNN is a modified
version of the wavelet func

In this research the results of applying Artificial Neural Networks with modified activation function to perform the online and offline identification of four Degrees of Freedom (4-DOF) Selective Compliance Assembly Robot Arm (SCARA) manipulator robot will be described. The proposed model of identification strategy consists of a feed-forward neural network with a modified activation function that operates in parallel with the SCARA robot model. Feed-Forward Neural Networks (FFNN) which have been trained online and offline have been used, without requiring any previous knowledge about the system to be identified. The activation function that is used in the hidden layer in FFNN is a modified version of the wavelet function. This approach ha

15 local isolates of Pseudomonas were obtained from 35 samples from several sources such as soil, water and some high-fat foods. The ability of isolates to produce lipase was measured by the size of the clarification zone formed around the colonies on the lipase production medium and by measuring the enzymatic activity and specific enzymatic activity, the isolate M3 was found to be the most efficient for production of the enzyme, This isolate was identified by microscopic, morphological, some biochemical tests and genetic diagnosis of 16S gene sequences by using the (PCR) technique, and then comparing the results obtained with the National Center for Biotechnology Inform