We propose an approach which, given a state-transition model of a system, constructs, in parallel, an approximate automaton model and a test suite for the system. The approximate model construction relies on a variant of Angluin’s automata learning algorithm, adapted to finite cover automata. A finite cover automaton represents an approximation of the system which only considers sequences of le...

This paper studies the problem of learning weighted automata from a finite labeled training sample. We consider several general families of weighted automata defined in terms of three different measures: the norm of an automaton’s weights, the norm of the function computed by an automaton, or the norm of the corresponding Hankel matrix. We present new data-dependent generalization guarantees fo...

We present new algorithms for inferring an unknown finite-state automaton from its input/output behavior in the absence of a means of r e ~ r t t i n p the machine to a start d a t e . A key technique used is inference of a homing sequence for the unknown automaton. Our infknrnce procedufes experiment with the unknown machine, and from time t o time require a teacher to supply counterexamplea t...

Nowadays, determining faults (or critical situations) in nonstationary environment is a challenging task in complex systems such as Nuclear center, or multi-collaboration such as crisis management. A discrete event system or a fuzzy discrete event system approach with a fuzzy role-base may resolve the ambiguity in a fault diagnosis problem especially in the case of multiple faults (or multiple ...

A signal machine is an abstract geometrical model for computation, proposed as an extension to the one-dimensional cellular automata, in which discrete time and space of cellular automata is replaced with continuous time and space in signal machine. A signal machine is defined as a set of meta-signals and a set of rules. A signal machine starts from an initial configuration which is a set of mo...

The problem of routing virtual circuits according t o dynamic probabilities in virtual circuit packe t switched networks is considered. Queueing network models a r e introduced and performance measures a re defined. A decentralized asynchronous adaptive routing methodology, based on learning automata theory is presented. Every node in the network has a stochastic learning automaton as a router ...

In this paper, two different ways of introducing alternation for lattice-valued (referred to as {L}valued)  regular tree grammars and {L}valued top-down tree automata are compared. One is the way which defines the alternating regular tree grammar, i.e., alternation is governed by the non-terminals of the grammar and the other is the way which combines state with alternation. The first way is ta...

In order for neural networks to learn complex languages or grammars, they must have sufficient computational power or resources to recognize or generate such languages. Though many approaches have been discussed, one obvious approach to enhancing the processing power of a recurrent neural network is to couple it with an external stack memory in effect creating a neural network pushdown automata...

The popular Expectation Maximization technique suffers a major drawback when used to approximate a density function using a mixture of Gaussian components; that is the number of components has to be a priori specified. Also, Expectation Maximization by itself cannot estimate timevarying density functions. In this paper, a novel stochastic technique is introduced to overcome these two limitation...