Genetic algorithms are one of the effective algorithms for hard optimization problems. They can escape from the local minima, however, the amount of their computation is often large. To decrease the amount of the comp...
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Genetic algorithms are one of the effective algorithms for hard optimization problems. They can escape from the local minima, however, the amount of their computation is often large. To decrease the amount of the computation and enhance the algorithms, the uniform design is combined into the genetic algorithm. The new genetic operator has the local-search property similar to that in traditional optimization techniques and needs a minimal amount of computation in certain meaning. Thus the new genetic algorithm can generate a diversity of population and explore the search space effectively. Moreover, the new algorithm is globally convergent. The numerical results also show the effectiveness of the new algorithm with its less computation, higher convergent speed for all test functions.
The problem of identification/tracking of rapidly fading communication channels is considered. When the channel coefficients vary rapidly with time the most frequently used weighted least squares (WLS) and least mean ...
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The problem of identification/tracking of rapidly fading communication channels is considered. When the channel coefficients vary rapidly with time the most frequently used weighted least squares (WLS) and least mean squares (LMS) algorithms are not capable of tracking the changes satisfactorily. To obtain good estimation results one has to use more specialized adaptive filters, such as the basis function (BF) algorithms which are based on explicit models of parameter changes. Unfortunately, estimators of this kind are numerically very demanding. The paper introduces a new class of recursive algorithms which combine low computational requirements, typical of WLS and LMS filters, with very good tracking capabilities, typical of BF filters.
When system parameters vary rapidly with time the weighted least squares filters are not capable of following the changes satisfactorily-some more elaborate estimation schemes, based on the method of basis functions, ...
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When system parameters vary rapidly with time the weighted least squares filters are not capable of following the changes satisfactorily-some more elaborate estimation schemes, based on the method of basis functions, have to be used instead. The basis functions estimators have increased tracking capabilities but are computationally very demanding. The paper introduces a new class of adaptive filters, based on the concept of postfiltering, which have improved parameter tracking capabilities, typical of the basis functions algorithms, but, at the same time, have pretty low computational requirements, typical of the weighted least squares algorithms.
Genetic algorithms are one of the effective algorithms for hard optimization problems. They can escape from the local minima, however, the amount of their computation is often large. To decrease the amount of the comp...
详细信息
Genetic algorithms are one of the effective algorithms for hard optimization problems. They can escape from the local minima, however, the amount of their computation is often large. To decrease the amount of the computation and enhance the algorithms, the uniform design is combined into the genetic algorithm. The new genetic operator has the local-search property similar to that in traditional optimization techniques and needs a minimal amount of computation in certain meanings. Thus the new genetic algorithm can generate a diversity of population and explore the search space effectively. Moreover, the new algorithm is globally convergent. The numerical results also show the effectiveness of the new algorithm with its less computation, and higher convergent speed for all test functions.
The main objective of this contribution is a new development of the continuous-time generalised predictive control (CGPC) resulting in a Youla-based CGPC design procedure for both minimumphase and nonminimum-phase unc...
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Banyan networks are commonly used as interconnection structures in ATM switches. This paper is concerned with the replication technique which was applied to the standard banyan networks. We apply this technique to the...
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Banyan networks are commonly used as interconnection structures in ATM switches. This paper is concerned with the replication technique which was applied to the standard banyan networks. We apply this technique to the plane interconnected parallel network (PIPN) which is a switch introduced previously as a better banyan-based interconnection structure. The normalized throughput of unbuffered and buffered replicated PIPN is analyzed analytically under uniform traffic model. We apply the simulation technique to verify the analytical results under the uniform traffic model and to study the performance of different heterogeneous traffic models. The performance is shown to increase significantly when the replicated PIPN is used which supports the idea of using this switch as a new high-performance ATM switch.
The paper presents a new approach to reconstruction of audio signals. First, a general reconstruction formula is derived allowing one to estimate unknown samples from the known (neighboring) ones. Then it is shown tha...
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The paper presents a new approach to reconstruction of audio signals. First, a general reconstruction formula is derived allowing one to estimate unknown samples from the known (neighboring) ones. Then it is shown that the unknown excitation appearing in the analytical reconstruction formula can be replaced with the natural excitation waveform extracted from the available portions of the signal.
In this paper, an adaptive decentralized controller is presented to attenuate the transversal vibration of a flexible cablestayed bridge induced by seismic excitation, in which only local sensor information has been u...
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The main objective of this contribution is a new development of the continuous-time generalised predictive control (CGPC) resulting in a Youla-based CGPC design procedure for both minimumphase and nonminimum-phase unc...
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The main objective of this contribution is a new development of the continuous-time generalised predictive control (CGPC) resulting in a Youla-based CGPC design procedure for both minimumphase and nonminimum-phase uncertain SISO systems. The derived design procedure consists of two steps. In the first step nominal stability and nominal performance of the control system are established by using a fully analytical design methodology, based on a collection of closed-loop prototype characteristics with definite time-domain specifications. In the second step, the primary structure of the CGPC controller is suitably enriched by `Youla' blocks guaranteeing that the resulting CGPC control system has the required robustness properties, i.e. robust stability and robust performance.
Static task scheduling in distributed computing systems is a very complex problem and known to be NP-hard. This problem is even harder when the module execution times become probabilistic. In this paper we study the e...
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Static task scheduling in distributed computing systems is a very complex problem and known to be NP-hard. This problem is even harder when the module execution times become probabilistic. In this paper we study the effect of probabilistic module execution times on the performance of task-scheduling algorithms. We show that in static task scheduling, for probabilistic module execution times, and in the existence of some factors there is no need to use an expensive task-scheduling algorithm. Given any two static task-scheduling algorithms that use deterministic module execution times in assigning task modules to the distributed system, the performance of these two algorithms will not remain the same when these module execution times become probabilistic rather than deterministic. We also study the effects of some factors an our results.
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