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 ($n \leq 50$) and demographic size histories with a large number of epochs ($\mathcal{D} \geq 64$). In addition, LDpop implements an approximate formula for the sampling probability that is reasonably accurate and scales to hundreds in sample size ($n \geq 256$). 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$^{-1}$), 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 e+e−e^+e^- Collisions

In most supersymmetric theories, charginos $\tilde{\chi}^\pm_{1,2}$, mixtures of charged color-neutral gauginos and higgsinos, belong to the class of the lightest supersymmetric particles. They are easy to observe at $e^+e^-$ colliders. By measuring the total cross sections and the left-right asymmetries with polarized electron beams in $e^+e^-\to\tilde{\chi}_i^-\tilde{\chi}_j^+ [i,j=1,2]$, 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 $M_2$, the modulus $|\mu|$ and $\cos \Phi_\mu$ of the higgsino mass parameter, and $\tan\beta = v_2/v_1$, the ratio of the vacuum expectation values of the two neutral Higgs doublet fields. The solutions are unique; the CP-violating phase $\Phi_\mu$ 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