【学位論文】
総合社会システムに関する研究, 東京大学, 1974.3.
【論文】
1. Yoichi Kaya, Masumi Ishikawa, Test of goodness of fit of impulse responsemodel, IEEE Trans. AC, Vol.16, No.3, pp.247-254, 1971.6.
2. 松田郁夫, 石川眞澄, 田村佳彦, 多部門・多地域の配分モデル, シミュレーション技術研究会論文集, Vol.3, No.2, pp.45-48, 1975.3.
3. 茅陽一, 石川眞澄, 松田郁夫, 田村佳彦, 多目的形産業配置総合モデル, 電気学会論文誌, vol.97-C, No.6, pp.101-108, 1977.6.
4. 松田郁夫, 石川眞澄, 大規模系の感度分析について, シミュレーション技術研究会
論文集, Vol.6, pp.19-24, 1978.9.
5. 石川眞澄, 茅陽一, 森俊介, 改良形クロスインパクト法, シミュレーション技術研究会論文集, Vol.6, No.4, pp.37-42, 1978.12.
6. 石川眞澄, 松田郁夫, 茅陽一, 多目的計画における選好解探索, 電気学会論文誌,
Vol.98-C, No.12, pp.375-382, 1978.12.
7. Y. Kaya, M. Ishikawa, S. Mori, A revised cross impact method and its application
to the forecast of urban transportation technology, Technological Forecasting
and Social Change, Vol.14, No.3, pp.243-257, 1979.8.
8.

石川眞澄, システム環境の予測, 電気学会雑誌，Vol.99, No.11, pp.1016-1020,
1979.11.

9.

石川眞澄, 松田郁夫, 茅陽一, 一対比較データに基づくランキングの決定, 電気学会論文誌, Vol.99-C, No.12, pp.307-314, 1979.12.

10. 松田郁夫，石川眞澄，茅陽一, 巨視的大気汚染モデル, 計測と制御，Vol.19, No.7,
pp.713-716, 1980.7.
11. Masumi Ishikawa, Ikuo Matsuda, Yoichi Kaya, Revised multiobjective
optimization in industry allocation problems, IEEE Trans. on SMC, Vol.10,
No.11, pp.696-704, 1980.11.
12. M. Ishikawa, M Toda, S. Mori, Y. Kaya, An application of the extended crossimpact method to generating scenarios of social change in Japan, Technological
Forecasting and Social Change, Vol.18, pp.217-233, 1980.11.
13. 松田郁夫, 石川眞澄, 茅陽一, 多変量解析による大規模系の感度分析とその産業配置モデルへの応用, 電気学会論文誌, Vol.101-C, No.1, pp.17-23, 1981.1.
14. Ikuo Matsuda, Masumi Ishikawa, Yoichi Kaya, Macroscopic approach to theenvironment in an industry allocation model, Ecological Modelling, Vol.12,
pp.69-82, 1981.3.
1

15. 石川眞澄, 茅陽一, 文章による判断確率の表現, 計測自動制御学会論文集, Vol.19,
No.2, pp.42-47, 1983.2.
16. 石川眞澄, 茅陽一, 頻度分布を用いたアンケート調査の属性分析, 電気学会論文誌,
Vol.104-C, No.4, pp.25-32, 1984.4.
17. 石川真澄, 仁木和久, 中島秀之, 諏訪基, 三段論法における人間の思考過程のモデ
ル化, 人工知能学会誌, Vol.3, No.4, pp.486-493, 1988.7.
18. 石川真澄, ニューラルネットワークによる動的システムの学習と制御, 電気学会論
文誌産業応用部門誌, vol.109-D, no. 4, pp.225-229, 1989.4.
19. 石川真澄, コネクショニストアプローチによるインタフェースのユーザモデル, 人
工知能学会誌, Vol.4, No.4, pp.398-410, 1989.7.
20. 石川眞澄, 動的コネクショニストモデルの学習性能, 電気学会論文誌電子・情報・システム部門誌, Vol.110-C, No.3, pp.133-140, 1990.3.
21. 石川真澄, 忘却を用いたコネクショニストモデルの構造学習アルゴリズム, 人工知能学会誌，Vol.5, No.5, pp.595-603, 1990.
22. 石川眞澄, コネクショニズムと学習, 認知科学の発展, Vol.4, pp.51-77, 1991.11.
23. 石川眞澄, モジュール構造ニューラルネットワークの学習, 人工知能学会誌, Vol.7,
No.2, pp.329-335, 1992.
24. 石川眞澄, コネクショニストモデルの忘却を用いた一般構造学習, 電気学会論文
誌, Vol.112-C, No.6, pp.364-369, 1992.
25. 石川眞澄, 山本洋嗣, ニューラルネットワークの構造学習による規則性の発見と汎化, 日本神経回路学会誌, Vol.1, No.2, pp.57-63, 1994.
26. Masumi Ishikawa, Learning of modular structured networks, Artificial
Intelligence, Vol.75, pp.51-62, 1995.
27. Chen Ke and Masumi Ishikawa, A parallel voting scheme for aspect recovery,
Journal of Computer Science & Technology, Vol.10, No.5, pp.385-402, 1995.9.
28. Masumi Ishikawa, Structural learning with forgetting, Neural Networks,
Vol.9, No.3, pp.509-521, 1996.
29. 新藤浩之, 野城真理, 福岡豊, 石川眞澄, 南谷晴之, ニューラルネットワークを用いた NARMAX モデルのパラメータ推定法, システム制御情報学会論文誌, Vol.9,
No.8, pp.366-374, 1996.
30. Masumi Ishikawa and Teppei Moriyama, Prediction of time series by astructural learning of neural networks, Fuzzy Sets and Systems, Vol.82,
pp.167- 176, 1996.
31. 石川眞澄, ニューラルネットの忘却付き構造学習, 日本ファジィ学会誌, Vol.9,
No.1, pp.3-10, 1997.
32. Y. Fukuoka, M. Noshiro, H. Shindo , H. Minamitani, M. Ishikawa, Nonlinearity2

identified by neural network models in Pco2 control system in humans,
Medical & Biological Engineering & Computing, Vol.35, pp.33-39, 1997.1.
33. 石川眞澄, 吉岡康裕, 本間直人, ニューラルネットワークによる概念の双方向的空間配置, 電気学会論文誌電子・情報・システム部門誌, Vol.117-C, No.6, pp.814820, 1997.
34. 章宏, 石川眞澄, 逆最適化問題のニューラルネットワーク学習による解法, 電気学会論文誌電子・情報・システム部門誌, Vol.117-C, No.7, pp.985-991, 1997.
35. 本間直人, 石川眞澄, 数量化 III 類の逆問題を用いたキーワードと文献の双方向的空間配置, 電子情報通信学会論文誌, Vol.J81-D-II, No.3, pp.564-573, 1998.
36. 藤村喜久郎, 徳高平蔵, 石川眞澄, 多都市巡回セールスマン問題での改良 SOMTSP 法の性能評価, 電気学会論文誌, 電子・情報・システム部門誌, Vol.199-C,
No.7, pp.875-882, 1999.7.
37. Masumi Ishikawa, Rule extraction by structural learning with an immediate
critic, Journal of Advanced Computational Intelligence, Vol.3, No.5, pp.341347, 1999.10.
38. 章宏, 石川眞澄, ニューラルネットワーク学習による動的逆最適化問題の解法, 電気学会論文誌, 電子・情報・システム部門誌, Vol.120-C, No.4, pp.481-488,
2000.4.
39. 章宏, 石川眞澄, ニューラルネットワーク学習を用いた静的逆最適化問題の一般解法, 電気学会論文誌, 電子・情報・システム部門誌, Vol.120-C, No.6, pp.857-864,
2000.6.
40. 章宏, 石川眞澄, 動的逆最適化による評価関数構造の決定, 電気学会論文誌, 電子・情報・システム部門誌, Vol.120-C, No.12, pp.2088-2093, 2000.12.
41. Masumi Ishikawa, Rule extraction by successive regularization, Neural
Networks, Vol.13, No.10, pp.1171-1183, 2000.12.
42. Masumi Ishikawa, Recognition of hand-gestures based on self-organization
using a DataGlove, Australian Journal of Intelligent Information Processing
Systems, Vol.6, No.2, pp.65-71, 2000.12.
43. 章宏, 石川眞澄, 静的逆最適化による大規模実データの効率的解釈, 電気学会論文
誌, Vol.123-C, No.6, pp.1173-1181, 2003.6.
44. 章宏, 石川眞澄, ニューラルネットワークによる２次形制約下での静的逆最適化問
題の解法, 電気学会論文誌, Vol.123-C, No.10, pp.1901-1907, 2003.
45. Masumi Ishikawa, Gesture recognition based on SOM using multiple sensors,
Neural Information Processing: Research and Development, Vol.152, pp.387404, Springer-Verlag, 2004.5.
46. Fredrik Linaker and Masumi Ishikawa, Real-time appearance-based Monte
3

Carlo localization, Robotics and Autonomous Systems, Vol.54, No.3, pp.205220, 2006.3.31.
47. Keiji Kamei, Masumi Ishikawa, Dependency of values of parameters in
reinforcement learning for navigation of a mobile robot on the environment,
Neural Information Processing --- Letters and Reviews, Vol.10, Nos.7-9,
pp.219-226, 2006.
48. A. Ahrary, A. Nassiraei, M. Ishikawa, A study of an autonomous mobile robot
for sewer inspection system, Journal of Artificial Life and Robotics, Vol.11,
No.1, pp.23-27, 2007.
49. Alireza Ahrary, Yoshinori Kawamura, Masumi Ishikawa, An automatedintelligent fault detection system for inspection of sewer pipes, 電気学会論文誌,
Vol.127-C, No.6, pp.943-950, 2007.6
50. Alireza Ahrary, Li Tian, Sei-Ichiro Kamata, Masumi Ishikawa, Navigation of
an autonomous sewer inspection robots based on stereo camera images andlaser scanner data, International Journal on Artificial Intelligence Tools(IJAIT), Vol.16, Issue 4, pp.611-625, 2007.8.
51. M. Aziz Muslim, Masumi Ishikawa, Tetsuo Furukawa, Task Segmentation in a
Mobile Robot by mnSOM: A new approach to training expert modules, NeuralComputing and Applications, Vol.16, Issue 6, pp.571-580, 2007.9.
52. Alireza Ahrary, Masumi Ishikawa, A Fast Stereo Matching Algorithm for
Sewer Inspection Robots, IEEJ Trans. on Electrical and Electronic
Engineering, Vol.3, Issue 4, pp.441-448, 2008.7.
53. Muhammad Aziz Muslim, Masumi Ishikawa, Tetsuo Furukawa, Task
segmentation in a mobile robot by mnSOM and clustering with spatio-temporalcontiguity, International Journal of Innovative Computing, Information &Control, Vol.5, No.4, pp.865-875, 2009.4.
54. Hong Zhang, Masumi Ishikawa, The performance verification of anevolutionary canonical particle swarm optimizer, Neural Networks, vol.23,
No.4, pp.510-516, 2010.5.
55. Koh Yamamoto, Masumi Ishikawa, A normalized index to quantify publication
productivity across disciplinary boundaries, International Journal of
Institutional Research and Management, Vol.1, No.1, pp.67-81, 2017.3.

4

【国際会議論文】
１． Yoichi Kaya, Masumi Ishikawa, Test of goodness of fit of a dynamic systemmodel, Proceedings of the Third Hawaii International Conference on System
Sciences, pp.95-98, 1970.1.
２． Yoichi Kaya, Masumi Ishikawa, Test of goodness of fit of a state equation
model, Preprints of papers for IFAC Kyoto Symposium on Systems
Engineering Approach to Computer Control, pp.341-346, 1970.8.
３． Norio Shiki, Masumi Ishikawa, Yoichi Kaya, Co-ordination of modeling andoptimal control, Proceedings of the Fourth Hawaii International Conference on
System Sciences, pp.516-518, 1971.1.
４． Yoichi Kaya, Masumi Ishikawa, and others, An approach to general social
system simulation, Proceedings of Tokyo AICA Symposium on Simulation ofComplex Systems, 1971.9.
５． Yoichi Kaya, Masumi Ishikawa, Reallocation of the world industry,
Proceedings of Second IIASA Symposium on Global Modelling, pp.199-248,
1974.10.
６． Ikuo Matsuda, Masumi Ishikawa, Yoichi Kaya, A macroscopic approach toenvironmental modelling, Proceedings of IFAC Symposium on Environmental
Systems Planning, Design and Control, pp.587-591, 1977.8.
７． Masumi Ishikawa, Ikuo Matsuda, Yoichi Kaya, A multi-objective industry
allocation model, Proceedings of IFAC Workshop on Urban, Regional andNational Planning, pp.169-175, 1977.8.5-6.
８． Yoichi Kaya, Hisashi Ishitani, Masumi Ishikawa, Global input-output model----Future of Global Interdependence, Proceedings of Fifth IIASA Symposium on
Glabal Modelling, 1977.9.26-29.
９． Masmi Ishikawa, Ikuo Matsuda, Yoichi Kaya, Revised multi-objective
optimization in industry allocation problem, Proceedings of International
Conference on Cybernetics and Society, pp.505-510, 1978.11.
１０．

Ikuo Matsuda, Masumi Ishikawa, Yoichi Kaya, Sensitivity analysis forlarge scale systems and its application to industry allocation model,
Proceedings of International Conference on Cybernetics and Society, pp.11931198, 1978.11.
１１．

Masumi Ishikawa, An approach to structuring group preference,

Proceedings of IFAC 8th Triennial World Congress, pp.XII38-XII43, 1981.8.
１２．

Ikuo Matsuda, Maxumi Ishikawa, Yoichi Kaya, Min-max determination ofmodel parameters with uncertainty and its application to the industry
5allocation model, Proceedings of IFAC 8th Triennial World Congress, pp.XII3843, 1981.8.
１３．

Yoshihiko Tamura, Masumi Ishikawa, Ikuo Matsuda, Graphic

representation of mass-data and its visual perception, Proceedings of IFAC 8th
Triennial World Congress, pp.68-73, 1981.8.
１４． Masumi Ishikawa, A structural learning algorithm with forgetting of linkweights, International Joint Conference on Neural Networks, 1989.6.
１５．

Masumi Ishikawa, A learning algorithm for discovering a general neuralnetwork structure, Proceedings of International Conference on Fuzzy Logic &Neural Networks (IIZUKA '90), pp.699-702, Iizuka, Japan, 1990.7.22-24.
１６．

J.Y. Uejio, M.M. Blattner, E.E. Schultz, and Masumi Ishikawa, A

structured history for command recall, Proceedings of the 24th HawaiiInternational Conference on System Science, pp.494-507, 1991.1.10.
１７．

Masumi Ishikawa, Interaction between connectionist and symbolic

representations using recurrent networks, International Symposium on Neural
Information Processing, as a part of International Symoposia on Information
Sciences (ISKIT'92) pp.58-61, Iizuka, Japan, 1992.7.12-15.
１８．

Masumi Ishikawa, A new approach to problem solving by modular

structured networks, Proceedings of the 2nd International Conference on
Fuzzy Logic & Neural Networks, pp.855-858, Iizuka Japan, 1992.7.17-22.
１９．

Masumi Ishikawa and Hideki Uchida, A structural learning of neuralnetworks based on an entropy criterion, International Joint Conference on
Neural Networks (IJCNN'92), Beijing, China, II375-380, 1992.
２０．

Masumi Ishikawa, Kengo Matuo and Kenichi Yoshino, Integration ofconnnectionist and symbolic modules in a vision task, Workshop on NeuralArchitectures and Distributed AI: --- From Schema Assemblages to Neural
Networks, pp.1-6, 1993.10.19-20.
２１．

Masumi Ishikawa and Kenichi Yoshino, Automatic task decomposition inmodular networks by structural learning with forgetting, Proceedings of 1993
International Joint Conference on Neural Networks, pp.1345-1348, 1993.10.
２２．

Ke Chen and Masumi Ishikawa, 3-D shape recovery by incorporating

context --- a connectionist approach, Proceedings of 1993 International Joint
Conference on Neural Networks, pp.1177-1180, 1993.10.
２３．

Masumi Ishikawa, Toward guidelines for friendly interfaces, Proceedingsof '94 International Symposium on Next Generation, Human Interface, pp.1-4,
1994.2.
6

２４．

Masumi Ishikawa & Kengo Matsuo, An integrated model of pattern andsymbolic information for the recognition of an object in context, Proceedings ofInternational Conference on Neural Networks (ICNN'94), pp.1810-1814,
Orlando, Florida, 1994.6.28-7.2.
２５．

Teppei Moriyama and Masumi Ishikawa, Prediction of time series based on

the structural learning of neural networks, Proceedings of International
Conference on Neural Information Processing, pp.836-841, Seoul, Korea,
1994.10.17-20.
２６．

Yasuhiro Yoshioka and Masumi Ishikawa, Interactive arrangement ofwords on a plane by inverse optimization using neural networks, Proceedingsof International Conference on Neural Information Processing,pp.1861-1866,
Seoul, Korea, 1994.10.17-20.
２７．

Masumi Ishikawa and Naoto Homma, Modelling of interaction between

pattern information and symbolic information, Proceeding of the Information
Integration Workshop --- Beyond divide and conquer strategy ---, pp.14-23,
Tsukuba, Japan, 1995.4.26-28.
２８．

Masumi Ishikawa, Modeling of visual recognition based on the interaction

between pattern information and symbolic information, Proceedings of the '95
RWC Symposium, pp.77-78, Tsukuba, Japan, 1995.6.12-15.
２９．

Masumi Ishikawa and Kengo Matsuo, An integrated model of visual

recognition with contextual dependency, Proceedings of International
Conference on Neural Networks (ICNN'95), pp.2615-2619, 1995.11.
３０．

Masumi Ishikawa, Structural learning and knowledge acquisition,

International Conference on Neural Networks (ICNN'96), Plenary, Panel andSpecial Sessions, pp.100-105, Washington D.C., 1996.6.2-6.
３１．

Masumi Ishikawa, Rule extraction by successive regularization,

International Conference on Neural Networks (ICNN'96), pp.1139-1143,
Washington D.C., 1996.6.2-6.
３２．

Naoto Homma and Masumi Ishikawa, Spatial representation of keywords

and documents by optimization and inverse optimization, International
Conference on Neural Information Processing (ICONIP'96), Hong Kong,
pp.1158-1163, 1996.9.24-27.
３３．

Masumi Ishikawa, Structural learning and Bayesian regularization inneural networks, International Conference on Neural Information Processing(ICONIP'96), Hong Kong, pp.1377-1380, 1996.9.24-27.
３４．

Kazuhiro Yoshida and Masumi Ishikawa, Bayesian estimation of7parameters of various regularizers in the learning of neural networks,
Proceedings of the 4th International Conference on Soft Computing(IIZUKA'96), pp.680-683, Iizuka, Japan, 1996.9.30-10.5.
３５．

Shinya Kawashima and Masumi Ishikawa, Locally shift invariant andglobally location dependent recognition of complex figures by neural networks,
Proceedings of the 4th International Conference on Soft Computing(IIZUKA'96), pp.478-481, Iizuka, Japan, 1996.9.30-10.5.
３６．

Masumi Ishikawa and Shinya Kawashima, An integrated neural networkmodel for the recognition of complex figures ---Combination of shift invariance& location dependence ---, 1997 Real World Computing Symposium (RWC'97),
pp.312-317, Tokyo, 19971.29-31.
３７．

Masumi Ishikawa, Towards higher level information processing using

structural learning of neural networks, Proceedings of 1997 China-Japan
Symposium on Advanced Information Technology, Huang Shan, China, pp.276281, 1997.3.31-4.4.
３８．

Wlodzislaw Duch, Rafal Adamczak, K. Grabczewski, Masumi Ishikawa,

Hiroki Ueda, Extraction of crisp logical rules using constrained
backpropagation networks --- comparison of two new approaches, Proc. of theEuropean Symposium on Artificial Neural Networks (ESANN'97), Bruge,
pp.109-114, 1997.4.16-18.
３９．

Masumi Ishikawa, Information integration using neural networks,

Workshop on Thought and Language, pp.9-19, Iizuka, 1997.9.8-9.
４０．

Masumi Ishikawa, Kazuhiko Nishino, Designing neural networks by a

combination of structural learning and genetic algorithms, International
Conference on Artificial Neural Networks ( ICANN'97), Lasanne, Switzerland,
Lecture Notes in Computer Science, 1327, Springer, pp.415-420, 1997.
４１．

Masumi Ishikawa, Structural learning and rule discovery, Proceedings of

the Third Conference Neural Networks and Their Applications, Kule, Poland,
pp.17-29, 1997.10.14-18.
４２．

Masumi Ishikawa and Hiroki Ueda, Structural learning approach to rule

discovery from data with continuous valued inputs, Proceedings ofInternational Conference on Neural Information Processing and Intelligent
Information Systems(ICONIP'97), Dunedin, New Zealand, Vol.2, Springer,
pp.898-901, 1997.
４３．

Hong Zhang and Masumi Ishikawa, A neural networks approach to inverse

optimization, The 2nd R.I.E.C. International Symposium on Design and
8Architecture of Information Processing Systems Based on The Brain
Information Principles (DAIPS), Sendai, pp.197-200, 1998.3.16-18.
４４．

Masumi Ishikawa, Kazuhiro Yoshida, Shun-ichi Amari, Designing

regularizers by minimizing generalization errors, Proceedings of International
Joint Conference on Neural Networks (IJCNN'98), 1998 IEEE World Congress
on Computational Intelligence, pp.2328-2333, 1998.5.4-9.
４５．

Hirohito Shimada and Masumi Ishikawa, Prediction of time series bystructural learning of recurrent neural networks, Proceedings of the 5thInternational Conference on Soft Computing and Information/Intelligent
Systems (IIZUKA'98), Vol.2, pp.610-614, 1998.
４６．

Masumi Ishikawa, Shinya Kawashima and Naoto Homma, Memory-based

location estimation and navigation using Bayesian estimation, International
Conference on Neural Information Processing (ICONIP'98), Vol.1, pp.112-117,
Kitakyushu, 1998.10.21-23.
４７．

Kikuo Fujimura, Heizo Tokutaka, Masumi Ishikawa, Modified counter-

propagation network, International Conference on Neural Information
Processing (ICONIP'98), Vol.1, pp.550-553, Kitakyushu, 1998.10.21-23.
４８．

X.Yu, M. Ishikawa, Huisheng Chi, Rule extraction from data by combining

a structural learning with forgetting and linear discriminant functions,International Conference on Neural Information Processing (ICONIP'98),
Vol.3, pp.1626-1629, Kitakyushu, 1998.10.21-23.
４９．

Masumi Ishikawa, Emergence of symbols and its applications, Proceedingsof 1999 Japan-China Symposium on Advanced Information Technology, Tokyo,
pp.159-166, 1999.10.5-10.7.
５０．

Masumi Ishikawa and Hiroko Matsumura, Recognition of a hand-gesture

based on self-organization using a DataGlove, International Conference on
Neural Information Processing (ICONIP'99), pp.739-745, Perth, Australia,
1999.11.16-20.
５１．

Masumi Ishikawa, Hirohito Shimada and Shun-ichi Amari, Iterative

design of regularizers based on data by minimizing generalization errors,
Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Neural Networks (IJCNN'2000), pp.I3-I8, Como, Italy, 2000.7.24-27.
５２．

Naohiro Sasaki and Masumi Ishikawa, Gesture recognition from dynamic

scene image, Proceedings of 6th International Conference on Soft Computing(IIZUKA'2000), pp.221-226, Iizuka, Japan, 2000.10.1-4.
５３．

Hiroshi Suenaga and Masumi Ishikawa, Self-organizing map with a9variable-size competitive layer, Proceedings of 7th International Conference on
Neural Information Processing (ICONIP-2000), pp.727-731, Taejon, Korea,
2000.11.15-18.
５４．

Hong Zhang and Masumi Ishikawa, Structure determination of a criterion

function by dynamic inverse optimization, Proceedings of 7th International
Conference on Neural Information Processing (ICONIP-2000), pp.662-666,
Taejon, Korea, 2000.11.15-18.
５５．

Masumi Ishikawa and Hiroshi Suenaga, Self-organization for temporal

data of varying length, International Conference on Neural Information
Processing (ICONIP2001), Shanghai, China, pp.247-252, 2001.11.14-18.
５６．

Hong Zhang and Masumi Ishikawa, Efficient interpretation of large-scale

real data by static inverse optimization, International Conference on Inverse
Problems -- Recent Developments in Theories and Numerics, pp.420-429, Hong
Kong, 2002.1.9-12.
５７．

Masumi Ishikawa and Naohiro Sasaki, Gesture recognition based on SOM

using multiple sensors, 9th International Conference on Neural Information
Processing (ICONIP2002), Singapore, pp.1300-1304, 2002.11.18-22.
５８．

Hong Zhang and Masumi Ishikawa, Interpretation of real data by static

inverse optimization with quadratic constraints, 9th International Conference
on Neural Information Processing (ICONIP2002), Singapore, pp.819-823,
2002.11.18-22.
５９．

Masumi Ishikawa and Mizuki Tsutsumi, Integration of self-organizationand supervised learning by attribute weights and dimensional reduction,Workshop on Self-Organizing Maps (WSOM'03), Kitakyushu, Japan, pp.71-76,
2003.9.11-14.
６０．

Hong Zhang, Masumi Ishikawa, A method for interpreting time series databy a chain of static inverse optimization, 33rd International Conference on
Computers and Industrial Engineering, CD-ROM 6 ページ分, Jeju Korea,
2004.3.25-27.
６１．

Keiji Kamei, Masumi Ishikawa, More effective reinforcement learning by

introducing sensory information, International Joint Conference on Neural
Networks (IJCNN04), pp.3185-3188, Budapest, Hungary, 2004.7.25-29.
６２．

M. Ishikawa, F. Linaker, M. Nagamatu, et al., Modeling approaches tobrain-like information technology, Proceedings of Joint 2nd International
Conference on Soft Computing and Intelligent Systems and 5th International
Symposium on Advanced Intelligent Systems, CD-ROM 6 pages, 2004.9.21-24.
10

６３．

Fredrik Linaker and Masumi Ishikawa, Rotation invariant features from

omnidirectional camera images using a polar higher-order local autocorrelation
feature extractor, Proceedings of 2004 IEEE/RSJ International Conference onIntelligent Robots and Systems (IROS2004), pp.4026-4031, Sendai, 2004.9.2810.2.
６４．

Masumi Ishikawa, Fredrik Linaker, Keiji Kamei, A new approach to

localization and navigation of mobile robots ---- Effective Bayesian estimation
and reinforcement learning ----, 6th China-India-Japan-Korea Workshop on
Neuro-biology & Neuroinformatics (NBNI), pp.23-24, 2004.11.18-20.
６５．

Keiji Kamei, Masumi Ishikawa, A genetic approach to optimizing thevalues of parameters in reinforcement learning for navigation of a mobile
robot, International Conference on Neural Information Processing (ICONIP04),
pp.1148-1153, 2004.11.22-25..
６６．

Hong Zhang and Masumi Ishikawa, An extended hybrid genetic algorithm

for exploring a large search space, 2nd International Conference on
Autonomous Robots and Agents, pp.244-248, Palmerston North, New Zealand,
2004.12.13-15.
６７．

Alireza Ahrary, Masumi Ishikawa, Visual fault detection for autonomous

sewer robots, Proceedings of the 20th International Technical Conference on
Circuits/Systems, Computers and Communication (ITC-CSCC'05), vol.1,
pp.201-202, Jeju, Korea, 2005.7.4-7.
６８．

Alireza Ahrary, Masumi Ishikawa, Detecting pipe feature points for sewer

pipe system based on image information, Proceedings of the 10th International
Conference on Mechatronics and Information Technology (ICMIT'05), pp.1-5,
Chongqing, China, 2005.9.21-23.
６９．

Keiji Kamei, Masumi Ishikawa, Improvement of performance ofreinforcement learning by introducing sensory information and a GA with
inheritance, International Conference on Neural Information Processing(ICONIP2005), Taipei, Taiwan, 2005.10.30-11.2.
７０．

Hong Zhang, Masumi Ishikawa, A hybrid real-coded genetic algorithmwith local search, International Conference on Neural Information Processing(ICONIP2005), Taipei, Taiwan, 2005.10.30-11.2.
７１．

Alireza Ahrary, Li Tian, Sei-ichiro Kamata, Masumi Ishikawa, A

cooperative stereo matching algorithm for sewer inspection robots, Proceedingsof the IASTED International Conference on Robotics and Applications (RA'05),
pp.294-299, Cambridge, USA, 2005.10.31-11.2.
11

７２．

Alireza Ahrary, Li Tian, Sei-ichiro Kamata, Masumi Ishikawa, An

autonomous sewer robots navigation based on stereo camera information,
Proceedings of the 17th IEEE International Conference on Tools with Artificial
Intelligence (ICTAI'05), pp.628-633, Hong Kong, 2005.11.14-16.
７３．

Hong Zhang, Masumi Ishikawa, Performance improvement of hybrid real-

coded genetic algorithm with local search and its applications, International
Conference on Computational Intelligence for Modelling, Control andAutomation (CIMCA2005), Vol.1, pp.1171-1176, Vienna, Austria, 2005.11.28-30.
７４．

Alireza Ahrary, Masumi Ishikawa, An automated fault detection system forsewer inspection robots, Proceedings of the 36th International Symposium on
Robotics (ISR'05), WE3C7, pp.1-5, Tokyo, Japan, 2005.11.29-12.1.
７５．

Alireza Ahrary, Yoshinori Kawamura, Masumi Ishikawa, Fusion of laser

scanner data and image data for landmark detection in sewer pipe systems,
Proceedings of the 9th International Conference on Mechatronics Technology(ICMT'05), ICMT-119, pp.1-5, Kuala Lumpur, Malaysia, 2005.12.5-8.
７６．

Alireza Ahrary, Amir Nassiraei, Masumi Ishikawa, A study of anautonomous mobile robot for sewer inspection system, Proc. of the 11th
International Symposium on Artificial Life and Robotics (AROB’06), pp.205208, Oita, Japan, 2006.1.23-25.
７７．

A. Ahrary, Y. Kawamura, M. Ishikawa, A laser scanner for landmark

detection with the sewer inspection robot KANTARO, Proc. of the IEEE
International Conference on System of Systems Engineering (ICSoSE’06),
pp.310-315, 2006.4.
７８．

A. Ahrary, M. Ishikawa, Self-localization of autonomous sewer robots byusing a stereo camera and a laser scanner, Proc. of the IEEE International
Conference on Networking, Sensing and Control (ICNSC’06), pp.1039-1044,
2006.4.
７９．

A. Ahrary, M. Ishikawa, Sensor fusion for navigation of sewer inspection

robots, Proceedings of the 37th International Symposium on Robotics (ISR’06),
pp.1-7, 2006.5.
８０．

Muhammad Aziz Muslim, Masumi Ishikawa, Tetsuo Furukawa, A new

approach to task segmentation in mobile robots by mnSOM, IEEE World
Congress on Computational Intelligence, pp.6542-6549, Vancouver, Canada,
2006.7.16-21.
８１．

Hong Zhang, Masumi Ishikawa, An evolutionary approach to dataclassification --- Hybrid real-coded genetic algorithm with pruning, IEEE
12

World Congress on Computational Intelligence, pp.5233-5238, Vancouver,
Canada, 2006.7.16-21.
８２．

Muhammad Aziz Muslim, Masumi Ishikawa, Tetsuo Furukawa, Task

segmentation in a mobile robot by mnSOM: A new approach to training expert
modules, International Conference on Neural Information Processing(ICONIP2006), Hong Kong, China, 2006.9.3-6.
８３．

Keiji Kamei, Masumi Ishikawa, Dependency of values of parameters in

reinforcement learning for navigation of a mobile robot on the environment,International Conference on Neural Information Processing (ICONIP2006),
Hong Kong, China, 2006.9.3-6.
８４．

A. Ahrary, M. Ishikawa, A fast stereo matching algorithm using

interpolation for sewer inspection robots, Proc. of the 32nd Annual Conferenceof the IEEE Industrial Electronics Society (IECON’06), pp.4060-4065, 2006.11.
８５．

Zhang Hong, Masumi Ishikawa, An ensemble method in hybrid real-coded

genetic algorithm with pruning for data classification, Proceedings of the 25th
IASTED International Multi-Conference Artificial Intelligence andApplications, pp.446-451, Innsbruck, Austria, 2007.2.12-14.
８６．

Hong Zhang, Masumi Ishikawa, Evolutionary particle swarm optimization(EPSO) ---- Estimation of optimal PSO parameters by GA, International
Association for Engineers (IAENG), International MultiConference ofEngineers and Computer Scientists (IMECS 2007), International Conferenceon Artificial Intelligence and Applications (ICAIA'07), pp.13-18, Hong Kong,
2007.3.21-23.
８７．

M. Aziz Muslim, Masumi Ishikawa, and Tetsuo Furukawa, Task

segmentation in a mobile robot by mnSOM and hierarchical clustering,
Proceedings of the 6th International Workshop on Self-Ogranizing Maps
(WSOM2007), 2007.9.3-6.
８８．

Muhammad Aziz Muslim, Masumi Ishikawa, Tetsuko Furukawa, Task

segmentation in a mobile robot by mnSOM and clustering with spatio-temporalcontiguity, International Conference on Neural Information Processing(ICONIP2007), pp.1075-1084, 2007.11.13-16.
８９．

M. Aziz Muslim, Masumi Ishikawa, Formation of graph-based maps formobile robots using hidden Markov models, 2008 IEEE World Congress on
Computational Intelligence (WCCI2008), pp.3098-3104, Hong Kong, 2008.6.16.6.
９０．

Hong Zhang, Masumi Ishikawa, Evolutionary canonical particle swarm
13

optimizer --- A proposal of meta-optimization in model selection, International
Conference on Artificial Neural Networks (ICANN2008), Lecture Notes in
Computer Science, No.5163, pp.472-481, Prague, Czech, 2008.9.3-6.
９１．

Masumi Ishikawa, Takao Hagiwara, Naoyuki Yamamoto, Fumiko Kiriake,

Brain-inspired emergence of behaviors in mobile robots by reinforcement
learning with internal rewards, 8th International Conference on HybridIntelligent Systems (HIS2008), pp.138-143, Barcelona, Spain, 2008.9.10-12.
９２．

Masumi Ishikawa, Mikio Morita, Brain-inspired emergence of behaviors

based on the desire for existence by reinforcement learning, International
Conference on Neural Information Processing (ICONIP2008), Auckland, New
Zealand, 2008.11.25-28.
９３．

Masumi Ishikawa, Yoshifumi Tanaka, Reinforcement learning withinternal rewards based on error in a grid-based map, International Conferenceon Information and Management Science (IMS2009), pp.316-320, Kunming,
China, 2009.7.20-28.
９４．

Masumi Ishikawa, Kosuke Ueno, Hierarchical architecture with modular

network SOM and modular reinforcement learning, International Conferenceon Artificial Neural Networks (ICANN 2009), Part I, LNCS 5768, pp.546-556,
Limassol, Cyprus, 2009.9.14-17.
９５．

Masumi Ishikawa, Mikio Morita, Brain-inspired emergence of behaviors

based on the desire for existence by reinforcement learning, International
Conference on Neural Information Processing (ICONIP 2008), Part I, LNCS
5506, pp.762-769, Springer-Verlag, Berlin Heidelberg, 2009.
９６．

Youbo Cai, Masumi Ishikawa, Surprise-driven exploration with Rao-Blackwellized particle filters for efficiently constructing occupancy grid maps,
International Conference on Neural Information Processing (ICONIP2009),
Part II, LNCS 5864, pp.420-429, Bangkok, Thailand, 2009.12.1-5.
９７．Mikio Morita, Masumi Ishikawa and Masahiro Nagamatu, Using theInternal Rewards for a Mobile Robot to Survive and Perform a Task Effectively,
Proceedings of the 23th Annual Conference of Biomedical Fuzzy Association(BMFSA 2010), pp.259-262, October 9-12, 2010, Kitakyushu, Fukuoka, Japan.
９８．Keiji Kamei, Masumi Ishikawa, Adaptive Decision Making in Ant Colony
System by Reinforcement Learning, 17th International Conference on Neural
Information Processing (ICONIP2010), LNCS6443, Part I, pp.609-617, Sydney,
Australia, 2010.11.22-25.

14

【解説論文他】
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3. リモートセンシング技術調査研究グループ, リモートセンシング技術に関する調査
報告, 電子技術総合研究所調査報告, pp.1-192, 1977.5.
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pp.73-78, 1978.6.
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1982.5.
8. 遠藤, 石川, 他７名, 省エネルギー課題とエネルギー技術の関連分析, 電子技術総合研究所彙報，Vol.52, No.4, pp.639-659, 1988.4.
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pp.83-90 ,1988.6.
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pp.666-672, 1988.7.
11. 石川眞澄, ネットワーク学習アルゴリズムの最近の話題, 計測と制御，Vol.30,
No.4, pp.285-290, 1991.
12. 石川眞澄, ニューラルネットワークと高次情報処理, シミュレーション, vol.12,
No.3, pp.177-186, 1993.
13. 石川眞澄, ニューラルネットワークによるデータ処理, ぶんせき, No.257, pp.350355, 1996.5.
14. 石川眞澄, 論壇: 脳型コンピュータに何が必要とされるか？, 日本神経回路学会誌,
Vol.5, No.2, p.81, 1998.
15. 石川眞澄, Regularization with a pruning prior, 論文レビュー，日本神経回路学会
誌, Vol.5, No.3, p.116. 1998.
16. 石川眞澄, 特集

脳と情報処理--- 脳はどこまで創れるのか---, ニューラルネッ

トによるデータからの規則の発見, Computer Today, No.90, pp.16-21, 1999.3.
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No.4, pp.174-179, 1999.
18. 石川眞澄, ニューロコンピューティング研究に何が欠けているか？(巻頭言), 日本
神経回路学会誌, Vol.6, No.2, pp.97-98, 1999.
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19. 石川眞澄, ニューラルネットワークの数理と気象への応用（上）, ニューラルネッ
トワークの学習の数理, 気象研究ノート, No.203, pp.29-70, 2002.10.31.
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16