研究者詳細 - 武藤　佳恭

論文 - 武藤　佳恭

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ハイパースペクトラム画像解析による人物の肌の特徴抽出

武藤佳恭他

日本人間工学会誌　34（特別）（日本人間工学会第39回大会講演集） 1998年05月

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Grouping Complex Face Parts by Nonlinear Oscillations

Takefuji, Y. et al

European Symposium on ANN 1998年04月

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Adaptation of Neural Agent in Dynamic Environment: Hybrid System of Genetic Algorithm and Neural Network

Takefuji, Y.et al

Second International Conference on Knowledge-based Intelligent Electronic Systems (KES '98) 1998年04月

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Agent-based Artificial Market Model

Takefuji, Y. et al

International Conference of Multi-Agent System '98 (ICMAS '98) 1998年04月

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Open Architecture of Infrastracture for Socialized Mobile Agents

Takeda, K., Takefuji, Y.

International conference ICCIMA1998 1998年02月

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ニューラルコンピューティングを用いた現代いろは歌の作成

武藤佳恭他

第57回情報処理学会全国大会 1998年

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A new self-organization classification algorithm for remote-sensing images

Oka Souichi, Ogawa Tomoaki, Oda Takayoshi, Takefuji Yoshiyasu

IEICE Transactions on Information and Systems E81-D （ 1 ） 132 - 136 1998年

ISSN 0916-8532

This paper presents a new self-organization classification algorithm for remote-sensing images. Kohonen and other scholars have proposed self-organization algorithms. Kohonen's model easily converges to the local minimum by tuning the elaborate parameters. In addition to others, S.C. Amatur and Y. Takefuji have also proposed self-organization algorithm model. In their algorithm, the maximum neuron model (winner-take-all neuron model) is used where the parameter-tuning is not needed. The algorithm is able to shorten the computation time without a burden on the parameter-tuning. However, their model has a tendency to converge to the local minimum easily. To remove these obstacles produced by the two algorithms, we have proposed a new self-organization algorithm where these two algorithms are fused such that the advantages of the two algorithms are combined. The number of required neurons is the number of pixels multiplied by the number of clusters. The algorithm is composed of two stages: in the first stage we use the maximum self-organization algorithm until the state of the system converges to the local-minimum, then, the Kohonen self-organization algorithm is used in the last stage in order to improve the solution quality by escaping from the local minimum of the first stage. We have simulated a LANDSAT-TM image data with 500 pixel × 100 pixel image and 8-bit gray scaled. The results justifies all our claims to the proposed algorithm.

Adaptation of neural agent in dynamic environment

Iba Takashi, Takefuji Yoshiyasu

575 - 584 1998年

The purpose of this study is to propose the adaptive agent as hybrid of Genetic Algorithm and Neural Network, and to clarify the effectiveness of the combination of two mechanisms in the dynamic environment. Evolution and learning can be explained as the mechanism of searching a solution in the enormous possibilities at the population level and individual level respectively. Genetic algorithm and neural network are computational models. Genetic algorithm is suitable for global search, and neural network at the local search. Combination of genetic algorithm and neural network seems natural from the biological viewpoint. There are two ways of combination of genetic algorithm and neural network, that is Darwinian and Lamarckian framework. In Lamarckian framework the acquired traits during the lifetime can be passed on to the offspring directly, and in Darwinian framework, these cannot be passed on. We propose `Neural Agent' whose initial weights of their neural networks are determined by their genome data, as a simple model of hybrid system of genetic algorithm and neural network. We examine which framework is better in the dynamic system. The result of our simulation shows Darwinian framework is better than Lamarckian.

Neural computing for the m-way graph partitioning problem

Saito Takayuki, Takefuji Yoshiyasu

IEICE Transactions on Information and Systems E80-D （ 9 ） 942 - 947 1997年09月

ISSN 0916-8532

The graph partitioning problem is a famous combinatorial problem and has many applications including VLSI circuit design, task allocation in distributed computer systems and so on. In this paper, a novel neural network for the m-way graph partitioning problem is proposed where the maximum neuron model is used. The unidirected graph with weighted nodes and weighted edges is partitioned into several subsets. The objective of partitioning is to minimize the sum of weights on cut edges with keeping the size of each subset balanced. The proposed algorithm was compared with the genetic algorithm. The experimental result shows that the proposed neural network is better or comparable with the other existing methods for solving the m-way graph partitioning problem in terms of the computation time and the solution quality.

A Neural Network Parallel Algorithm for Meeting Schedule Problems

Tsuchiya Kazuhiro, Takefuji Yoshiyasu

Applied Intelligence 7 （ 3 ） 205 - 213 1997年

ISSN 0924-669X

A parallel algorithm for solving meeting schedule problems is presented in this paper where the problem is NP-complete. The proposed system is composed of two maximum neural networks which interact with each other. One is an M x S neural network to assign meetings to available time slots on a timetable where M and S are the number of meetings and the number of time slots, respectively. The other is an M x P neural network to assign persons to the meetings where P is the number of persons. The simulation results show that the state of the system always converges to one of the solutions. Our empirical study shows that the solution quality of the proposed algorithm does not degrade with the problem size.

A neural network approach to facility layout problems

Tsuchiya Kazuhiro, Bharitkar Sunil, Takefuji Yoshiyasu

European Journal of Operational Research 89 （ 3 ） 556 - 563 1996年03月

ISSN 0377-2217

A near-optimum parallel algorithm for solving facility layout problems is presented in this paper where the problem is NP-complete. The facility layout problem is one of the most fundamental quadratic assignment problems in Operations Research. The goal of the problem is to locate N facilities on an N-square (location) array so as to minimize the total cost. The proposed system is composed of N x N neurons based on an artificial two-dimensional maximum neural network for an N-facility layout problem. Our algorithm has given improved solutions for several benchmark problems over the best existing algorithms.

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A neural network approach to PLA folding problems

Tsuchiya Kazuhiro, Takefuji Yoshiyasu

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 15 （ 10 ） 1299 - 1305 1996年

ISSN 0278-0070

A near-optimum parallel algorithm for solving PLA folding problems is presented in this paper where the problem is NP-complete and one of the most fundamental problems in VLSI design. The proposed system is composed of n X n neurons based on an artificial two-dimensional maximum neural network where n is the number of inputs and outputs or the number of product lines of PLA. The two-dimensional maximum neurons generate the permutation of inputs and outputs or product lines. Our algorithm can solve not only a simple folding problem but also multiple, bipartite, and constrained folding problems. We have discovered improved solutions in four benchmark problems over the best existing algorithms using the proposed algorithm.

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Neural network parallel algorithm for meeting schedule problems

Tsuchiya Kazuhiro, Takefuji Yoshiyasu, Kurotani Ken ichi, Wakahara Kunio

173 - 177 1996年

A parallel algorithm for solving meeting schedule problems is presented in this paper where the problem is NP-complete. The proposed system is composed of two maximum neural networks which interact with each other. One is an M×S neural network to assign meetings to available time slots on a timetable where M and S are the number of meetings and the number of time slots, respectively. The other is an M×P neural network to assign persons to the meetings where P is the number of persons. The simulation results show that the state of the system always converges to one of the solutions and that the solution quality of the proposed algorithm does not degrade with the problem size.

A neural network algorithm for the no-three-in-line problem

Tsuchiya Kazuhiro, Takefuji Yoshiyasu

Neurocomputing 8 （ 1 ） 43 - 49 1995年

ISSN 0925-2312

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Balloon net discovering improved solutions in one of unsolved problems in geometry

Fujisawa Kimiya, Takefuji Yoshiyasu

2208 - 2210 1995年

A balloon net model is introduced and demonstrated for discovering improved solutions in one of unsolved problems in geometry which is referred to as a problem of `Spreading points in a square'. How should n points be arranged in a unit square so that the minimum distance between them is the greatest? Note that d(n) is the greatest possible minimum distance between n points in a unit square. Exact results are known for n≤9 and n = 14, 16, 25, and 36. Many investigators including Schaer, Meir, Kirchner, Wengerodt, Goldberg, Schluter, Valette and others have studied this geometrical problem for many years. The best known result is summarized in the book of `Unsolved Problems in Geometry' (H.T. Croft. K.J. Falconer and R.K. Guy/1991). We have found improved solutions for n = 13 and n = 15 by the proposed algorithm.

Scaling properties of neural networks for job-shop scheduling

Foo Simon Y., Takefuji Yoshiyasu, Szu Harold

Neurocomputing 8 （ 1 ） 79 - 91 1995年

ISSN 0925-2312

This paper investigates the scaling properties of neural networks for solving job-shop scheduling problems. Specifically, the Tank-Hopfield linear programming network is modified to solve mixed integer linear programming with the addition of step-function amplifiers. Using a linear energy function, our approach avoids the traditional problems associated with most Hopfield networks using quadratic energy functions. Although our approach requires more hardware (in terms of processing elements and resistive interconnects) than a recent approach by Zhou et al. [2], the neurons in the modified Tank-Hopfield network do not perform extensive calculations unlike those described by Zhou et al.

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Presenting the special issue on optimization and combinatorics

Szu Harold, Takefuji Yoshiyasu, Foo Simon Y.

Neurocomputing 8 （ 1 ） 3 - 4 1995年

ISSN 0925-2312

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Neural computing approach to Japanese electoral system

Saito Takayuki, Takefuji Yoshiyasu

2202 - 2207 1995年

In December 1994, Japanese single-member constituency system for the House of Representatives was established where Japan is divided into three hundred constituencies. A single representative will be elected from each constituency. Zoning three hundred constituencies was accomplished by hand calculators in Japan where zoning the constituencies is a very elaborate task because several constraints must be satisfied. This paper presents a neural computing approach for automatically zoning constituencies. Our method was examined by using 25 Tokyo constituencies. Ideally, the weight of a single vote in a certain population to elect a representative should be equal to that of the other constituencies. Based on the established rule, the ratio of the lightest weight to the heaviest weight must be within two. Our result shows that our ratio is 1.28 while the current (official) ratio is 1.47.

Neural network parallel computing for multi-layer channel routing problems

Suzuki Kyotaro, Amano Hideharu, Takefuji Yoshiyasu

Neurocomputing 8 （ 2 ） 141 - 156 1995年

ISSN 0925-2312

Multi-layer channel routing is one of cumbersome jobs in automatic layout design of VLSI chips and PCBs. As VLSI chips have been used in every field of electrical engineering, it becomes more important to reduce the layout design time. With the advancement of the VLSI technology, four-layer problems can be treated and the algorithms for more than four-layer problems will be demanded in the near future. Proposed algorithm can treat 2 × n-layer problems in parallel. In this paper, the algorithm is introduced and implemented on a multiprocessor system. By minimizing the communication overhead and load unbalance between processors, the performance with 8 processors is improved by between 6 and 6.5 times compared with the sequential version.

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Parallel algorithm for time-slot assignment problems in TDM hierarchical switching systems

Funabiki Nobuo, Takefuji Yoshiyasu

IEEE Transactions on Communications 42 （ 10 ） 2890 - 2898 1994年10月

ISSN 0090-6778

This paper presents a parallel algorithm for time-slot assignment problems in TDM hierarchical switching systems, based on the neural network model. The TDM systems are operated in repetitive frames composed of several time-slots. A time-slot represents a switching configuration where one packet is transmitted through an I/O line. The goal of our algorithm is to find conflict-free time-slot assignments for given switching demands. The algorithm runs on a maximum of n²×m processors for m-time-slot problems in n×n TDM systems. In small problems up to a 24×24 TDM system, the algorithm can find the optimum solution in a nearly constant time, when it is performed on n²×m processors.

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武藤 佳恭

論文 - 武藤 佳恭

武藤　佳恭

論文 - 武藤　佳恭