Showing papers on "Complex network published in 1993"

A class of hypercube-like networks

Abstract: We introduce a class of graphs which are variants of the hypercube graph. Many of the properties of this class of graphs are similar to that of the hypercube hence, we refer to them as the class of hypercube-like graphs/networks. We show that the hypercube, the twisted n-cube and the multiply-twisted cube are members of this class of graphs. We also propose simple strategies for distributed routing and broadcast and discuss some issues regarding embedding other graphs and reconfiguration in such networks. >

Combined Neural Networks for Time Series Analysis

Abstract: We propose a method for improving the performance of any network designed to predict the next value of a time series. We advocate analyzing the deviations of the network's predictions from the data in the training set. This can be carried out by a secondary network trained on the time series of these residuals. The combined system of the two networks is viewed as the new predictor. We demonstrate the simplicity and success of this method, by applying it to the sunspots data. The small corrections of the secondary network can be regarded as resulting from a Taylor expansion of a complex network which includes the combined system. We find that the complex network is more difficult to train and performs worse than the two-step procedure of the combined system.

Complex-valued radial basis function networks

Abstract: The complex radial basis function (RBF) network proposed has complex centres and weights but the response of its hidden nodes remains real. Several learning algorithms for the existing real RBF network are extended to this complex network. The proposed network is capable of generating complicated nonlinear decision surface or approximating an arbitrary nonlinear function in multidimensional complex space and it provides a powerful tool for nonlinear signal processing involving complex signals. This is demonstrated using two practical applications to communication systems. The first case considers the equalisation of time-dispersive communication channels, and the authors show that the underlying Bayesian solution has an identical structure to the complex RBF network. In the second case, they use the complex RBF network to model nonlinear channels, and this application is typically found in channel estimation and echo cancellation involving nonlinear distortion. >

Nonblocking multirate distribution networks

Abstract: Results for nonblocking distribution networks are generalized for the multirate environment in which different user connections share a switch's internal data paths for arbitrary fractions of the total capacity. Conditions under which network proposed by Y.P. Ofman (1965), C.D. Thompson (1978), and N. Pippenger (1973) lead to multirate distribution networks are derived. The results include both rearrangement and wide-sense nonblocking networks. The complexity of the rearrangement multirate network exceeds that of the corresponding space-division network by a log log factor, while the complexity of the wide-sense nonblocking network is within a factor of two of the corresponding space-division network. >

A general framework for developing adaptive fault-tolerant routing algorithms

Abstract: It is shown that Cartesian product (CP) graph-based network methods provide a useful framework for the design of reliable parallel computer systems. Given component networks with prespecified connectivity, more complex networks with known connectivity and terminal reliability can be developed. CP networks provide systematic techniques for developing reliable fault-tolerant routing schemes, even for very complex topological structures. The authors establish the theoretical foundations that relate the connectivity of a CP network, the connectivity of the component networks, and the number of faulty components: present an adaptive generic algorithm that can perform successful point-to-point routing in the presence of faults: synthesize, using the theoretical results, this adaptive fault-tolerant algorithm from algorithms written for the component networks: prove the correctness of the algorithm: and show that the algorithm ensures following an optimal path, in the presence of many faults, with high probability. >

A virtual world for network management

Abstract: Existing network management systems typically use a combination of textual displays and 2-D directed graph representations of network topology. A network management system is being designed that uses a virtual world presented through a 3-D stereo display and manipulated with a 3-D mouse. The goal is to allow the user to better understand and control the structure and behavior of a large, complex network. In the current prototype, the user interacts with a 3-D representation of a network whose topology and behavior are specified by a separate network emulator. The user can choose from among a set of different views of the network. For example, one view shows a selected virtual path as a series of logical links contained within a physical path. The system will serve as a testbed for the knowledge-based design of network visualizations. >

IDEAL: A multi-agent architecture for the management of complex networks

Abstract: The role of distribution, cooperation, and coordination issues in the domain of network management is discussed. IDEAL, the framework under development to investigate distributed network management solutions is presented. An example is given of distributed network management which shows how set diagnosis agents cooperate and coordinate themselves to diagnose multiple problems. >

Studies on a network of complex neurons

Abstract: In the last decade, much effort has been directed towards understanding the role of chaos in the brain. Work with rabbits reveals that in the resting state the electrical activity on the surface of the olfactory bulb is chaotic. But, when the animal is involved in a recognition task, the activity shifts to a specific pattern corresponding to the odor that is being recognized. Unstable, quasiperiodic behavior can be found in a class of conservative, deterministic physical systems called the Hamiltonian systems. In this paper, we formulate a complex version of Hopfield's network os real parameters and show that a variation on this model is a conservative system. Conditions under which the complex network can be used as a Content Addressable memory are studied. We also examine the effect of singularities of the complex sigmoid function on the network dynamics. The network exhibits unpredictable behavior at the singularities due to the failure of a uniqueness condition for the solution of the dynamic equations. On incorporating a weight adaptation rule, the structure of the resulting complex network equations is shown to have an interesting similarity with Kosko's Adaptive Bidirectional Associative Memory.

Process models and network complexity

Abstract: A neural network is used to model a plasma etching system. The network is trained from a large database generated from actual CMOS production runs. One aspect of this model is described which predicts the amount of over-etching into the underlying oxide during silicide gate patterning. Comparing networks of varying complexity, it is found that large, complex networks, which perform better on the task of learning the training data, do not necessarily perform as well on verification. The neural network model is used to extract information regarding the relative influence of various process variables and signatures in the etching process. For such procedures, small, simple networks often prove to be better and more accurate. >

Terminal-pair reliability using network decomposition

Abstract: In this paper, the idea of the network decomposition is discussed. The decomposition technique to evaluate the reliability of complex networks is introduced. The decomposition is used to tear the network into two subnetworks. Some basic rules for combining probabilities is given and is modified to combine the reliabilities of subnetworks. Two trends are presented. The first is to generate all network states, and the other is to enumerate all the network paths in a general network (both directed and undirected networks). The above methods are illustrated by numerical examples.

New degree four networks: properties and performance

Abstract: Two-dimensional tori, or its variants such as the midimew networks, are the most popular degree-four interconnection networks. However, the number of nodes interconnected by two-dimensional tori or the midimew networks grows as a square of their diameters. The authors discuss two different types of degree-four interconnection networks, the starcake networks and the k-ary 2-cliques. These graphs are regular, vertex-symmetric, maximally fault-tolerant and have a better diameter than the popular degree-four networks. They discuss the construction and routing of these networks and compare them with other interconnection networks. A preliminary performance comparison indicates that the proposed networks offer better throughput-delay characteristics than tori and midimew networks. >

Complexity bounds of emerging structures in self-organizing networks

Abstract: The idea of self-organizing systems is to acquire a meaningful internal structure just by being exposed to some 'natural' environment. Due to the complex network dynamics it appears very hard to analyze the structures that may emerge in such a system. Results on the complexity of classification functions and the preconditions necessary in order to allow the computation of such functions are presented.