• The Transactions of the Korea Information Processing Society
• /
• v.1 no.1
• /
• pp.92-98
• /
• 1994

The hypercube-like interconncetion network,X-hypercubes,has the same number of nodes and edges as conventional hypercubes.By slightly changing the interconneton way between nodes,however,X-hypercubes reduces the diameter by almost half.Thus the communication delay in X-hypercubes can be expected to be much lower than that in hypercubes. This paper gives a new definition of X-hypercubes establishing clear-cut condition of connection between two nodes.As appliction examples of the new definition,this paper presents simple embeddings of hypercubes in X-hypercubes and vice versa.This means that any programs written for hypercubes can be transported onto X-hypercubes and vice versa with minimal overhead.This paper also present bitonic merge sort for X-hypercubes by simulation that for hypercubes.

• Proceedings of the IEEK Conference
• /
• 1998.10a
• /
• pp.475-478
• /
• 1998

The matrix hypercube MH(2,n) is the interconnection network which improves the network cost of the hypercube. In this paper, we propose an algorithm for one-to-all broadcasting in the matrix hypercube MH(2,n). The algorithm can broadcast a message to 22n nodes in O(n) time. The algorithm uses the rich structure of the matrix hypercubes and works by recursively partitioning the original matrix hypercubes into smaller matrix hypercubes.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.2 no.2
• /
• pp.34-41
• /
• 1992

A robust and reliable learning and reasoning mechanism is addressed based upon fuzzy set theory and fuzzy associative memories. The mechanism stores a priori an initial knowledge base via approximate learning and utilizes this information for decision-making systems via fuzzy inferencing. We called this fuzzy computer architecture a 'fuzzy hypercube' processing all the rules in one clock period in parallel. Fuzzy hypercubes can be applied to control of a class of complex and highly nonlinear systems which suffer from vagueness uncertainty. Moreover, evidential aspects of a fuzzy hypercube are treated to assess the degree of certainty or reliability together with parameter sensitivity.

• Honam Mathematical Journal
• /
• v.43 no.4
• /
• pp.679-689
• /
• 2021

The first author suggested an exact volume formula of the hypercubes [0, 1]ⁿ clipped by several hyperplanes expressed directly in terms of linear coefficients of the hyperplanes. However, it requires awkward assumptions to apply the formula to various situations. We suggest a concrete method to overcome those restrictions for two or three hyperplanes using 𝜖-perturbation, which gives an exact value applicable for any kind of arrangement of hyperplanes with no consideration.

• Journal of KIISE:Computer Systems and Theory
• /
• v.29 no.9
• /
• pp.493-502
• /
• 2002

The connectivity and edge-connectivity have been the prime deterministic measure of fault tolerance in multicomputer networks. These parameters have a problem that they do not differentiate the different types of disconnected graphs which result from removing the disconnecting vertices or disconnecting edges. To compensate for this shortcoming, one can utilize generalized measures of connectedness such as superconnectivity, toughness, scattering number, vertex-integrity, binding number, and restricted connectivity. In this paper, we analyze such deterministic measures of fault tolerance in recursive circulants and hypercubes, and compare them in terms of fault tolerance.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.6 no.4
• /
• pp.49-60
• /
• 1996

The objective of this paper is to develop an adaptive learning method for fuzzy hypercubes using a neural network. An intelligent control system is proposed by exploiting only the merits of a fuzzy logic controller and a neural network, assuming that we can modify in real time the consequential parts of the rulebase with adaptive learning, and that initial fuzzy control rules are established in a temporarily stable region. We choose the structure of fuzzy hypercubes for the fuzzy controller, and utilize the Perceptron learning rule in order to upda1.e the fuzzy control ru1c:s on-line with the output errors. As a result, the effectiveness and the robustness of this intelligent controller are shown with application of the proposed adaptive fuzzy-neuro controller to control of the cart-pole system.

• Kyungpook Mathematical Journal
• /
• v.34 no.2
• /
• pp.229-237
• /
• 1994

• 제어로봇시스템학회:학술대회논문집
• /
• 1993.10a
• /
• pp.710-717
• /
• 1993

In this paper, we focus upon the design and applications of adaptive fuzzy-neuro controllers. An intelligent control system is proposed by exploiting the merits of two paradigms, a fuzzy logic controller and a neural network, assuming that we can modify in real time the consequential parts of the rulebase with adaptive learning, and that initial fuzzy control rules are established in a temporarily stable region. We choose the structure of fuzzy hypercubes for the fuzzy controller, and utilize the Perceptron learning rule in order to update the fuzzy control rules on-line with the output error. And, the effectiveness and the robustness of this intelligent controller are shown with application of the proposed adaptive fuzzy-neuro controller to control of the cart-pole system.

• Proceedings of the IEEK Conference
• /
• 2003.11b
• /
• pp.11-14
• /
• 2003

In interconnection network for parallel processing, the cycle partitioning problem for parallel transmission with faulty vertieces or edges is very important. In this paper, we assume that k($\leq$m-1) edges do not share any vertices of m dimension hypercube Q$_{m}$ and show that it is possible to construct a cycle set which consists of k cycles covering all the vertices of the hypercube and one cycle including one of the given edges. This cycle set can be used to parallel transmission between two vertices joined by faulty edges.s.

• Korean Journal of Cognitive Science
• /
• v.4 no.1
• /
• pp.123-152
• /
• 1993

This paper discusses how to automatically extract fuzzy rules from given data.The fuzzy space which contain given data are fitst subdivided into a set of hypercubes.each of which contains the homogeneous data belonging to the same class, and then a fuzzy rule is defined based on the constructed hypercube.In order to dynamically agjust the size of a hypercube. the fuzzy space is to be splitted based on a center vector and then the splitted subspaces are to be merged throungh the adjacency relation.The membership functions.which are to be embedded in a fuzzy rule.are to be formed through analyzing the cummulative histogram of given data along each axis of the constructed hypercube.