Linearly many faults in Cayley graphs generated by transposition trees

We prove that when linearly many vertices are deleted in a Cayley graph generated by a transposition tree, the resulting graph has a large connected component containing almost all remaining vertices.     

Enhancing cognitive radio systems with robust reasoning

Cognitive radio systems dynamically reconfigure the algorithms and parameters they use, in order to adapt to the changing environment conditions. However, reaching proper reconfiguration decisions presupposes a way of knowing, with high enough assurance, the capabilities of the alternate configurations, especially in terms of achievable transmission capacity and coverage. The present paper addresses this problem, firstly, by specifying a complete process for extracting estimations of the capabilities of candidate configurations, in terms of transmission capacity and coverage, and, secondly, by enhancing these estimations with the employment of a machine learning technique. The technique is based on the use of Bayesian Networks, in conjunction with an effective learning and adaptation strategy, and aims at extracting and exploiting knowledge and experience, in order to reach robust (i.e. stable and reliable) estimations of the configurations' capabilities. Comprehensive results of the proposed method are presented, in order to validate its functionality. Copyright © 2007 John Wiley & Sons, Ltd.     

Evolving Fault-Tolerant Neural Networks

In this paper, genetic algorithm is used to help improve the tolerance of feedforward neural networks against an open fault. The proposed method does not explicitly add any redundancy to the network, nor does it modify the training algorithm. Experiments show that it may profit the fault tolerance as well as the generalisation ability of neural networks.     

Memory of Distributed Computer Systems: Distinctive Features of Their Architectural-Structural Organization and Use

Distinctive features of architectural-structural organization and use of memory networks are considered with the aim of choosing an optimal configuration in designing distributed computer systems.     

Neutral fitness landscapes in signalling networks

Biological and technological systems process information by means of cascades of signals. Be they interacting genes, spiking neurons or electronic transistors, information travels across these systems, producing, for each set of external conditions, an appropriate response. In technology, circuits performing specific complex tasks are designed by humans. In biology, however, design has to be ruled out, confronting us with the question of how these systems could have arisen by accumulation of small changes. The key factor is the genotype-phenotype map. With the exception of RNA folding, not much is known about the exact nature of this mapping. Here, we show that structure of the genotype-phenotype map of simple feed-forward circuits is very close to the ones found in RNA; they have a large degree of neutrality, by which a circuit can be completely rewired keeping its input-output function intact, and there is a relatively small neighbourhood of a given circuit containing almost all the phenotypes.     

Passivity Analysis of Dynamic Neural Networks with Different Time-scales

Dynamic neural networks with different time-scales include the aspects of fast and slow phenomenons. Some applications require that the equilibrium points of the designed networks are stable. In this paper, the passivity-based approach is used to derive stability conditions for dynamic neural networks with different time-scales. Several stability properties, such as passivity, asymptotic stability, input-to-state stability and bounded input bounded output stability, are guaranteed in certain senses. A numerical example is also given to demonstrate the effectiveness of the theoretical results.     

On the number of broadcast schemes in networks

Broadcasting is the process of spreading one piece of information among a group of individuals connected by an interconnection network. In this paper we give exact lower and upper bounds for the number of broadcast schemes in arbitrary networks. Also, we give the exact value for complete bipartite graphs and an upper bound for regular networks. Based on the counting method we describe a new random algorithm for broadcasting in networks.     

Convective cooling of supercritical carbon dioxide inside tubes: heat transfer analysis through neural networks

In a previous work, convective heating of carbon dioxide was studied with neural networks (NN), obtaining a totally heuristical heat transfer equation from the direct regression of experimental data. In the present work, the analysis focuses on the cooling process, which has a technical relevance in various applications, as for example in transcritical refrigeration cycles. Heat transfer around the critical zone presents a marked enhancement, that follows the peaks in thermophysical properties like thermal conductivity and heat capacity. Similarly, other properties like density and enthalpy, present a strong variation in narrow temperature intervals around the critical point.This constitutes then a highly non-linear phenomenon, for which it is advisable to use a very flexible function approximator like the NNs. NN models were applied both in terms of dimensionless numbers and of physical quantities, obtaining the two corresponding NN architectures. The choice of the optimal number of neurons in the NN hidden layer is discussed. The NN models are then compared with a recent correlation from literature, for which the validation results present an AAD of 27% and a bias of −26% with an evident prediction shifting. On the other hand the NN models in terms of dimensionless numbers and of physical quantities have AAD and bias of 14% and −4%, and of 7% and −2%, respectively, showing a largely better performance.     

A connectionist implementation of the theory of planned behavior: Association of beliefs with exercise intention.

The theory of planned behavior suggests attitudes are a product of salient beliefs. This study examined whether aggregating salient beliefs was plausible within a more biologically centered information-processing environment. A neural network was used to examine associations among beliefs relating to exercise intention. Data on intentions and behavioral, normative, and control beliefs from 114 respondents were used to train (by error backpropagation) a neural network to associate beliefs with intention. The R2 between the network's estimated and self-reported intention was .66. The network's representation comprised 6 belief profiles associated with high, moderate, or low behavioral intention. The neural network accommodated complex relationships among beliefs and belief-intention associations and indicated how high-level constructs such as attitudes may be viewed as the best fit (compromise state) between aroused beliefs. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

光纤光栅视觉系统

利用光学衍射原理，研制成了光纤光栅衍射屏，并与计算机神经网络相结合，开发了了一种识别立体形状的视觉系统。本文介绍了光纤光栅的设计原理及计算方法，给出了系统的总体结构及工作原理，完成了三棱 柱，三棱锥、四棱柱，四棱锥和球等立体形状学习与识别，并讨论了实验结果。