By definition, an input/output (or I/O) automaton is closed if there are no input actions in its action signature, and open otherwise. The dynamic I/O automata (DIOA) model is a special kind of the I/O automata one in which automata can be created and destroyed dynamically, and automata action signatures can change from state to state. In this paper, we draw attention tothe possibility that a DIOA model of a system is open in the sense of the above definition although it seems to the specifier to be closed. This can be the reason why some expected properties cannot be proved to hold for the system. We, therefore, introduce a restriction operator for DIOA to help obtain a closed model in which the expected system properties could be verified.
COBISS.SI-ID: 14173206
This paper proposes a gradient-descent based unit selection optimization algorithm for the optimization of unit-cost function weights and for improving the overall performance of the unit-selection algorithm, as used in a corpus-based text-to-speech synthesis system. Complex multidimensional and fuzzy-logic based unit-cost functions are used in the presented unit-selection algorithm. The weights used by these unit-cost functions are usually defined by heuristics or by listening tests. This can be very laborious and time consuming, and does not necessarily result in an optimal performance of the unit-selection algorithm because of multidimensional unit-cost function space, within which different database candidates' features are evaluated. The authors proposed unit-selection optimization process that consists of several steps. It is fully automatic, flexible, and fast enough to enable the development of a corpus-based text-to-speech (TTS) system that uses many different voices, without any heuristics or listening tests. The obtained results “suggest” those values that the unit-selection cost-function weights should have in order to obtain smoother transitions between selected unit candidates, after the unit-selection process.
COBISS.SI-ID: 15277078
Many real-world optimization problems are largescale in nature. In order to solve these problems, an optimization algorithm is required that is able to apply a global search regardless of the problemsć particularities. This paper proposes a self-adaptive differential evolution algorithm, called jDElscop, for solving large-scale optimization problems with continuous variables. The proposed algorithm employs three strategies and a population size reduction mechanism. The performance of the jDElscop algorithm is evaluated on a set of benchmark problems provided for the Special Issue on the Scalability of Evolutionary Algorithms and other Metaheuristics for Large Scale Continuous Optimization Problems. Nonparametric statistical procedures were performed formultiple comparisons between the proposed algorithm and three wellknown algorithms from literature. The results show that the jDElscop algorithm can deal with large-scale continuous optimization effectively. It also behaves significantly better than other three algorithms used in the comparison, in most cases.
COBISS.SI-ID: 14398230