انجمن کامپیوتر ایران

Being able to predict the coming seizure can impressively improve the quality of the patients' lives since they can be warned to avoid doing risky activities via a prediction system. Here, a locally linear neuro fuzzy model is used to predict the EEG time series. Subsequently, this model is utilized in accompany with Singular Spectrum Analysis for prediction. Afterward, an information theoretic criterion is used to select a reliable subset of input variables which contain more information about the target signal. Comparison of three mentioned methods on one hand shows that SSA enables our prediction model to extract the main patterns of the EEG signal and highly improves the prediction accuracy. On the other hand, applying the method of channel selection to the model yields more accurate prediction. It is shown that fusion of some certain signals provides more information about the target and considerably improves the prediction ability.

In this paper, we present a new measure for evaluating similarity changes in a multi agent system. The similarity measure of the agents changes during the learning process. The similarity differences are because of any composition or decomposition of some agent sets. The presented measure, defines the changes of homogeneity of agents by composition and decomposition. The utility of the metrics is demonstrated in the experimental evaluation of multi agent foraging. The results show that while the similarity difference gets a positive value, the performance grow rapidly.

In this paper, a new fuzzy fading memory (FFM) is developed in order to aid a modified input estimation (MIE) technique and enhance its performance in tracking high maneuvering targets. The MIE has been introduced recently and performs well in tracking low and medium maneuvering targets. However, due to some modeling errors, the accuracy of this tracker may be seriously degraded in presence of high maneuvers. To cope with this difficulty, an intelligent approach based on FFM is presented in this paper. Simulation results prove the efficiency of the proposed method in tracking high maneuvering targets.

With ever increasing complexity of software systems, it is now more evident than ever that a fundamental change in software engineering practices is required. In this paper we discuss the nature of change and its implications. The notion of passive autonomy is introduced as a reference to the relative autonomy of business entities, and we claim that realizing the passive autonomy in information systems would results in systems far more adaptable and aligned with business’ needs. In order to realize this autonomy, theuse of autonomous agents as representing real world autonomous entities is suggested. After pointing out the shortcomings of current agent based architectures, a new architecture is proposed, based on indirect, multilateral negotiation. The suitability of this architecture is demonstrated in a simple case of beer game and it is shown that the bullwhip effect is remedied to some extent using this new architectural approach.