68,649 research outputs found
A neuro-fuzzy architecture for real-time applications
Neural networks and fuzzy expert systems perform the same task of functional mapping using entirely different approaches. Each approach has certain unique features. The ability to learn specific input-output mappings from large input/output data possibly corrupted by noise and the ability to adapt or continue learning are some important features of neural networks. Fuzzy expert systems are known for their ability to deal with fuzzy information and incomplete/imprecise data in a structured, logical way. Since both of these techniques implement the same task (that of functional mapping--we regard 'inferencing' as one specific category under this class), a fusion of the two concepts that retains their unique features while overcoming their individual drawbacks will have excellent applications in the real world. In this paper, we arrive at a new architecture by fusing the two concepts. The architecture has the trainability/adaptibility (based on input/output observations) property of the neural networks and the architectural features that are unique to fuzzy expert systems. It also does not require specific information such as fuzzy rules, defuzzification procedure used, etc., though any such information can be integrated into the architecture. We show that this architecture can provide better performance than is possible from a single two or three layer feedforward neural network. Further, we show that this new architecture can be used as an efficient vehicle for hardware implementation of complex fuzzy expert systems for real-time applications. A numerical example is provided to show the potential of this approach
Automated implementation of rule-based expert systems with neural networks for time-critical applications
In fault diagnosis, control and real-time monitoring, both timing and accuracy are critical for operators or machines to reach proper solutions or appropriate actions. Expert systems are becoming more popular in the manufacturing community for dealing with such problems. In recent years, neural networks have revived and their applications have spread to many areas of science and engineering. A method of using neural networks to implement rule-based expert systems for time-critical applications is discussed here. This method can convert a given rule-based system into a neural network with fixed weights and thresholds. The rules governing the translation are presented along with some examples. We also present the results of automated machine implementation of such networks from the given rule-base. This significantly simplifies the translation process to neural network expert systems from conventional rule-based systems. Results comparing the performance of the proposed approach based on neural networks vs. the classical approach are given. The possibility of very large scale integration (VLSI) realization of such neural network expert systems is also discussed
Two-Locus Likelihoods under Variable Population Size and Fine-Scale Recombination Rate Estimation
Two-locus sampling probabilities have played a central role in devising an
efficient composite likelihood method for estimating fine-scale recombination
rates. Due to mathematical and computational challenges, these sampling
probabilities are typically computed under the unrealistic assumption of a
constant population size, and simulation studies have shown that resulting
recombination rate estimates can be severely biased in certain cases of
historical population size changes. To alleviate this problem, we develop here
new methods to compute the sampling probability for variable population size
functions that are piecewise constant. Our main theoretical result, implemented
in a new software package called LDpop, is a novel formula for the sampling
probability that can be evaluated by numerically exponentiating a large but
sparse matrix. This formula can handle moderate sample sizes () and
demographic size histories with a large number of epochs (). In addition, LDpop implements an approximate formula for the sampling
probability that is reasonably accurate and scales to hundreds in sample size
(). Finally, LDpop includes an importance sampler for the posterior
distribution of two-locus genealogies, based on a new result for the optimal
proposal distribution in the variable-size setting. Using our methods, we study
how a sharp population bottleneck followed by rapid growth affects the
correlation between partially linked sites. Then, through an extensive
simulation study, we show that accounting for population size changes under
such a demographic model leads to substantial improvements in fine-scale
recombination rate estimation. LDpop is freely available for download at
https://github.com/popgenmethods/ldpopComment: 32 pages, 13 figure
A new approach for designing self-organizing systems and application to adaptive control
There is tremendous interest in the design of intelligent machines capable of autonomous learning and skillful performance under complex environments. A major task in designing such systems is to make the system plastic and adaptive when presented with new and useful information and stable in response to irrelevant events. A great body of knowledge, based on neuro-physiological concepts, has evolved as a possible solution to this problem. Adaptive resonance theory (ART) is a classical example under this category. The system dynamics of an ART network is described by a set of differential equations with nonlinear functions. An approach for designing self-organizing networks characterized by nonlinear differential equations is proposed
Massive star evolution in close binaries:conditions for homogeneous chemical evolution
We investigate the impact of tidal interactions, before any mass transfer, on
various properties of the stellar models. We study the conditions for obtaining
homogeneous evolution triggered by tidal interactions, and for avoiding any
Roche lobe overflow during the Main-Sequence phase. We consider the case of
rotating stars computed with a strong coupling mediated by an interior magnetic
field. In models without any tidal interaction (single stars and wide
binaries), homogeneous evolution in solid body rotating models is obtained when
two conditions are realized: the initial rotation must be high enough, the loss
of angular momentum by stellar winds should be modest. This last point favors
metal-poor fast rotating stars. In models with tidal interactions, homogeneous
evolution is obtained when rotation imposed by synchronization is high enough
(typically a time-averaged surface velocities during the Main-Sequence phase
above 250 km s), whatever the mass losses. In close binaries, mixing is
stronger at higher than at lower metallicities. Homogeneous evolution is thus
favored at higher metallicities. Roche lobe overflow avoidance is favored at
lower metallicities due to the fact that stars with less metals remain more
compact. We study also the impact of different processes for the angular
momentum transport on the surface abundances and velocities in single and close
binaries. In models where strong internal coupling is assumed, strong surface
enrichments are always associated to high surface velocities in binary or
single star models. In contrast, models computed with mild coupling may produce
strong surface enrichments associated to low surface velocities. Close binary
models may be of interest for explaining homogeneous massive stars, fast
rotating Wolf-Rayet stars, and progenitors of long soft gamma ray bursts, even
at high metallicities.Comment: 21 pages, 13 figures, 3 tables, accepted for publication in Astronomy
and Astrophysic
Determining SUSY Parameters in Chargino Pair-Production in Collisions
In most supersymmetric theories, charginos , mixtures
of charged color-neutral gauginos and higgsinos, belong to the class of the
lightest supersymmetric particles. They are easy to observe at
colliders. By measuring the total cross sections and the left-right asymmetries
with polarized electron beams in , the chargino masses and the gaugino-higgsino mixing angles can be
determined. From these observables the fundamental SUSY parameters can be
derived: the SU(2) gaugino mass , the modulus and
of the higgsino mass parameter, and , the ratio of the
vacuum expectation values of the two neutral Higgs doublet fields. The
solutions are unique; the CP-violating phase can be determined
uniquely by analyzing effects due to the normal polarization of the charginos.Comment: 20 pages, 4 figures, uses axodraw.st
Gravitational Wave Background from Phantom Superinflation
Recently, the early superinflation driven by phantom field has been proposed
and studied. The detection of primordial gravitational wave is an important
means to know the state of very early universe. In this brief report we discuss
in detail the gravitational wave background excited during the phantom
superinflation.Comment: 3 pages, 2 eps figures, to be published in PRD, revised with
published version, refs. adde
Management of Digital Video Broadcasting Services in Open Delivery Platforms
The future of Digital Video Broadcasting (DVB) is moving towards solutions offering an efficient way of carrying interactive IP multimedia services over digital terrestrial broadcasting networks to handheld terminals. One of the most promising technologies is Digital Video Broadcasting-Handheld (DVB-H), at present under standardisation. Services deployed via this type of DVB technologies should enjoy reliability comparable to TV services and high quality standards. However, the market at present does not provide effective and economical solutions for the deployment of such services over multi-domain IP networks, due to their high level of unreliability. This paper focuses on service management, service level agreement (SLA) and network performance requirements of DVB-H services. Experimental results are presented concerning QoS sensitivity to network performance of DVB-H services delivered over a multi-domain IP network. Moreover, a solution for efficient and cost effective service management via QoS monitoring and control and network SLA design is proposed. The solution gives DVB-H operators the possibility of fully managing service QoS without being tied to third party operators
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