14,842 research outputs found
Using fuzzy logic to integrate neural networks and knowledge-based systems
Outlined here is a novel hybrid architecture that uses fuzzy logic to integrate neural networks and knowledge-based systems. The author's approach offers important synergistic benefits to neural nets, approximate reasoning, and symbolic processing. Fuzzy inference rules extend symbolic systems with approximate reasoning capabilities, which are used for integrating and interpreting the outputs of neural networks. The symbolic system captures meta-level information about neural networks and defines its interaction with neural networks through a set of control tasks. Fuzzy action rules provide a robust mechanism for recognizing the situations in which neural networks require certain control actions. The neural nets, on the other hand, offer flexible classification and adaptive learning capabilities, which are crucial for dynamic and noisy environments. By combining neural nets and symbolic systems at their system levels through the use of fuzzy logic, the author's approach alleviates current difficulties in reconciling differences between low-level data processing mechanisms of neural nets and artificial intelligence systems
Directional genetic differentiation and asymmetric migration
Understanding the population structure and patterns of gene flow within
species is of fundamental importance to the study of evolution. In the fields
of population and evolutionary genetics, measures of genetic differentiation
are commonly used to gather this information. One potential caveat is that
these measures assume gene flow to be symmetric. However, asymmetric gene flow
is common in nature, especially in systems driven by physical processes such as
wind or water currents. Since information about levels of asymmetric gene flow
among populations is essential for the correct interpretation of the
distribution of contemporary genetic diversity within species, this should not
be overlooked. To obtain information on asymmetric migration patterns from
genetic data, complex models based on maximum likelihood or Bayesian approaches
generally need to be employed, often at great computational cost. Here, a new
simpler and more efficient approach for understanding gene flow patterns is
presented. This approach allows the estimation of directional components of
genetic divergence between pairs of populations at low computational effort,
using any of the classical or modern measures of genetic differentiation. These
directional measures of genetic differentiation can further be used to
calculate directional relative migration and to detect asymmetries in gene flow
patterns. This can be done in a user-friendly web application called
divMigrate-online introduced in this paper. Using simulated data sets with
known gene flow regimes, we demonstrate that the method is capable of resolving
complex migration patterns under a range of study designs.Comment: 25 pages, 8 (+3) figures, 1 tabl
XRound : A reversible template language and its application in model-based security analysis
Successful analysis of the models used in Model-Driven Development requires the ability to synthesise the results of analysis and automatically integrate these results with the models themselves. This paper presents a reversible template language called XRound which supports round-trip transformations between models and the logic used to encode system properties. A template processor that supports the language is described, and the use of the template language is illustrated by its application in an analysis workbench, designed to support analysis of security properties of UML and MOF-based models. As a result of using reversible templates, it is possible to seamlessly and automatically integrate the results of a security analysis with a model. (C) 2008 Elsevier B.V. All rights reserved
A scalable multi-core architecture with heterogeneous memory structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs)
Neuromorphic computing systems comprise networks of neurons that use
asynchronous events for both computation and communication. This type of
representation offers several advantages in terms of bandwidth and power
consumption in neuromorphic electronic systems. However, managing the traffic
of asynchronous events in large scale systems is a daunting task, both in terms
of circuit complexity and memory requirements. Here we present a novel routing
methodology that employs both hierarchical and mesh routing strategies and
combines heterogeneous memory structures for minimizing both memory
requirements and latency, while maximizing programming flexibility to support a
wide range of event-based neural network architectures, through parameter
configuration. We validated the proposed scheme in a prototype multi-core
neuromorphic processor chip that employs hybrid analog/digital circuits for
emulating synapse and neuron dynamics together with asynchronous digital
circuits for managing the address-event traffic. We present a theoretical
analysis of the proposed connectivity scheme, describe the methods and circuits
used to implement such scheme, and characterize the prototype chip. Finally, we
demonstrate the use of the neuromorphic processor with a convolutional neural
network for the real-time classification of visual symbols being flashed to a
dynamic vision sensor (DVS) at high speed.Comment: 17 pages, 14 figure
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