جستجوی مقالات مرتبط با کلیدواژه « artificial neural network » در نشریات گروه « فناوری اطلاعات »

Using chaff to deflect missile guidance radar or missile seeker is a common and effective defense method in military vessels. To deal with this defensive method, focus on specific characteristics of the target and chaff signals. These features should be able to perform properly in different operating conditions of the radar or different environmental conditions that change the behavior of the radar’s return signals. But there is no feature that can distinguish the target from the target with appropriate accuracy in all conditions. In this article, a structure is presented for detecting chaff and target in a radar and has been able to improve the accuracy of target detection in presence of chaff. Also, to improve the performance of the radar with a cognitive approach, its transmitted waveform is optimally selected and changed at each stage. For this purpose, a feedback neural network with LSTM layers has been used. The general structure of the proposed method uses pre-processing on the received radar signals and extracts symmetry characteristics, Doppler spread and AGCD from it to contain the information for separating the target and chaff. Then, to remove the effect of noise on the features. Finally, these features are used to correctly distinguish the target from the chaff in a feed-forward neural network with fully connected layers. At the end, the effectiveness of this method is compared to the previous methods. It can be seen that the performance of the proposed system has made a significant improvement in accuracy of detection.

A few artificial neural networks have been proposed so far for multivariate time series prediction and they used simple general-purpose neural networks. Therefore, they cannot achieve high prediction accuracy. In this paper, we propose an artificial neural network called DBMTSP (Dependency Based Multivariate Time Series Prediction) to predict the next element of a time series in the multivariate case. Compared to the existing methods, DBMTSP considers both intra-time-series dependencies and inter-time-series dependencies efficiently to achieve more accurate predictions. We propose a hierarchical encoder in DBMTSP to discover inter-time-series dependencies. The proposed hierarchical encoder is able to encode secondary time series into a single parameter that represents dependencies that exist between the main time series and the secondary time series. The hierarchical encoder has a scalable design such that it can accept a large number of secondary time-series. We have trained DBMTSP using 32760 data matrices. We evaluated DBMTSP using 8190 test data matrices. Our evaluations show that DBMTSP surpasses the existing methods in term of prediction accuracy.

With the emergence of the World Wide Web, Electronic Commerce (E-commerce) has been growing rapidly in the past two decades. Intelligent agents play the main role in making the negotiation between different entities automatically.  Automated negotiation allows resolving opponent agents' mutual concerns to reach an agreement without the risk of losing individual profits. However, due to the unknown information about the opponent's strategies, automated negotiation is difficult. The main challenge is how to reveal the optimal information about the opponent's strategy during the negotiation process to propose the best counter-offer. In this paper, we design a buyer agent which can automatically negotiate with the opponent using artificial intelligence techniques and machine learning methods. The proposed buyer agent is designed to learn the opponent's strategies during the negotiation process using four methods "Bayesian Learning", "Kernel Density Estimation", "Multilayer Perceptron Neural Network", and "Nonlinear Regression". Experimental results show that the use of machine learning methods increases the negotiation efficiency, which is measured and evaluated by parameters such as the rate agreement (RA), average buyer utility (ABU), average seller utility (ASU), average rounds (AR). Rate agreement and average buyer utility have increased from 58% to 74% and 90% to 94%, respectively, and average rounds have decreased from 10% to 0.04%.

Web Services provides a solution to web application integration. Due to the significance of trust in choosing the proper web service, a novel optimal configuration of neural networks by multi-objective genetic algorithm and ensemble-classifier approach is used to evaluate the trust of single web services. For evaluating trust in the single web services, first, a set of neural networks were trained by the settings their parameters through the multi-objective genetic algorithm. Next, the best combination of neural networks was selected to make an ensemble classifier. This method was evaluated with single WS dataset considered eight criteria. Three measurements, accuracy, time and ROC curve were considered to assess the efficiency. Ultimately, the results show that the proposed approach can achieve a trade-off between time and accuracy by the multi-objective genetic algorithm. Also using ensemble-classifiers approach increases the reliability of the model. Consequently, the proposed method promote the detection accuracy.