The recent work on automata whose variables and parameters are real numbers (e.g., Blum, Shub, and Smale, 1989; Koiran, 1993; Bournez and Cosnard, 1996; Siegelmann, 1996; Moore, 1996) has focused largely on questions about computational complexity and tractability. It is also revealing to examine the metric relations that such systems induce on automata via the natural metrics on their paramete...

In this paper, the concept of fuzzy automata normed linear structure spaces is introduced and suitable examples are provided. ;The ;concepts of fuzzy automata $alpha$-open sphere, fuzzy automata $mathscr{N}$-locally compact spaces, fuzzy automata $mathscr{N}$-Hausdorff spaces are also discussed. Some properties related with to fuzzy automata normed linear structure spaces and fuzzy automata $ma...

This paper provides an Angluin-style learning algorithm for a class of register automata supporting the notion of fresh data values. More specifically, we introduce session automata which are well suited for modeling protocols in which sessions using fresh values are of major interest, like in security protocols or ad-hoc networks. We show that session automata (i) have an expressiveness partly...

In this paper, we propose an efficient approach to design optimization of analog circuits that is based on the reinforcement learning method. In this work, Multi-Objective Learning Automata (MOLA) is used to design a two-stage CMOS operational amplifier (op-amp) in 0.25μm technology. The aim is optimizing power consumption and area so as to achieve minimum Total Optimality Index (TOI), as a new...

One of the biggest limitations of BP algorithm is its low rate of convergence. The Variable Learning Rate (VLR) algorithm represents one of the well-known techniques that enhance the performance of the BP. Because the VLR parameters have important influence on its performance, we use learning automata (LA) to adjust them. The proposed algorithm named Adaptive Variable Learning Rate (AVLR) algor...

One of the main challenges in wireless sensor network is energy problem and life cycle of nodes in networks. Several methods can be used for increasing life cycle of nodes. One of these methods is load balancing in nodes while transmitting data from source to destination. Directed diffusion algorithm is one of declared methods in wireless sensor networks which is data-oriented algorithm. Direct...

One of the main challenges in wireless sensor network is energy problem and life cycle of nodes in networks. Several methods can be used for increasing life cycle of nodes. One of these methods is load balancing in nodes while transmitting data from source to destination. Directed diffusion algorithm is one of declared methods in wireless sensor networks which is data-oriented algorithm. Direct...

Cassel, S. 2015. Learning Component Behavior from Tests. Theory and Algorithms for Automata with Data. Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology 1311. 70 pp. Uppsala: Acta Universitatis Upsaliensis. ISBN 978-91-554-9395-0. Formal models are often used to describe the behavior of a computer program or component. Behavioral models have man...

Links are established between three widely used modeling frameworks for reactive systems: the ioco theory of Tretmans, the interface automata of De Alfaro and Henzinger, and Mealy machines. It is shown that, by exploiting these links, any tool for active learning of Mealy machines can be used for learning I/O automata that are deterministic and output determined. The main idea is to place a tra...

In this paper, we propose a distributed learning automata-based algorithm to solve the multicast routing problem in wireless mobile ad-hoc networks. The proposed algorithm estimates the expected relative mobility of each host, by sampling its movement parameters in various epochs, to realistically predict its motion behavior, and takes advantage of the Steiner connected dominating set to form t...