In this paper, we introduce open cellular Learning automata and then study its convergence behavior. It is shown that for a class of rules called commutative rules, the open cellular Learning automata in stationary external environments converges to a stable and compatible configuration. The numerical results also confirm the theory.  

Background and Objectives: Sonar data processing is used to identify and track targets whose echoes are unsteady. So that they aren’t trusty identified in typical tracking methods. Recently, RLA have effectively cured the accuracy of undersea objective detection compared to conventional sonar objective cognition procedures, which have robustness and low accuracy. Methods: In this research, a combination of classifiers has been used to improve the accuracy of sonar data classification in complex problems such as identifying marine targets. These classifiers each form their pattern on the data and store a model. Finally, a weighted vote is performed by the LA algorithm among these classifiers, and the classifier that gets the most votes is the classifier that has had the greatest impact on improving performance parameters.Results: The results of SVM, RF, DT, XGboost, ensemble method, R-EFMD, T-EFMD, R-LFMD, T-LFMD, ANN, CNN, TIFR-DCNN+SA, and joint models have been compared with the proposed model. Considering that the objectives and databases are different, we benchmarked the average detection rate. In this comparison, Precision, Recall, F1_Score, and Accuracy parameters have been considered and investigated in order to show the superior performance of the proposed method with other methods.Conclusion: The results obtained with the analytical parameters of Precision, Recall, F1_Score, and Accuracy compared to the latest similar research have been examined and compared, and the values are 87.71%, 88.53%, 87.8%, and 87.4% respectively for each of These parameters are obtained in the proposed method.

Active database systems (ADBS) can react to the occurrence of predefined events automatically by definition a collection of active rules. One of the most important modules of ADBS is the rule scheduler, which has considerable impact on performance and efficiency of ADBS. The job of rule scheduler is the selection of a rule for execution from the set of ready for execution rules. In this paper, we propose a new approach based on Learning automata to improve the rule scheduling performance in terms of average response time, response time variance, and throughput. Learning automata have been used to obtain better estimations for rule execution probabilities. The results of experimentations show that the performance of the proposed method outperforms the most effective existing rule scheduling method.

In this paper, an adaptive random search method, based on continuous action-set Learning automata, is studied for solving stochastic optimization problems in which only the noise-corrupted value of a function at any chosen point in the parameter space is available. First, a new continuous action-set Learning automaton is introduced and its convergence properties are studied. Then, applications of this new continuous action-set Learning automata to the minimization of a penalized Shubert function and pattern classification are presented.

Social networks are one of the types of complex networks. Identifying communities in social networks is an effective way to use their information, for which several algorithms have been presented so far. In this paper, novel algorithms are designed, in which a Learning automaton is attached to each node; The number of actions of Learning automata is fixed and equal to the estimate of the number of network communities. At each step, each of the Learning automata chooses an action from its set of actions. Choosing any of these actions means assigning the label of that community to the node. The action chosen by each automaton is evaluated based on the chosen actions of its neighbors ((local attention) and/or communities detected by the entire method (global screening). The result of the evaluation leads to generate rewards or punish signal for the automata. By receiving a reward, the probability of re-choosing the chosen action by the automaton, or the community label, increases, and otherwise, by receiving a fine, the probability of this action decreases. By repeating the algorithm, the optimal action is determined as long as no change occurs in the selected label of the corresponding automata of each node with more iterations, and as a result, the optimal communities are determined as the output of the algorithm. The comparison of the results of the experiments shows the effectiveness of the proposed methods in comparison with the previous methods.