Abstract
Since the SOM visualizes the similarity of raw information on the competitive layer, it can be utilized in the field of pattern classification, data analysis, and so on. However, it cannot model the input-output characteristics of the system of interest. In order to squeeze out the input-output relationship from the data set with evaluation obtained by trial and error, the novel modeling tool was developed by the author (1999), which is the extension of SOM and in which the input-output relationship of the system is mapped onto the competitive layer. The system is named as self-organizing relationship network (SOR network). A set of units on the competitive layer of the SOR network after learning exhibits a set of typical input-output characteristics of the system of interest and thus the network achieves the knowledge acquisition (IF-THEN rules) from the raw data with evaluation and the effective fuzzy inference with defuzzification. The plenary talk presents the tutorial aspects of the SOR network and an application to an intelligent control.
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References
Penfield, W., Rasmussen, T.: The Cerebral Cortex of Man. Macmillan, New York (1952)
Kohonen, T.: Self-organizing formation of topologically correct feature maps. Biological Cybernetics 43(1), 59–69 (1982)
Kohonen, T.: Analysis of a simple self-organizing process. Biological Cybernetics 44(2), 135–140 (1982)
Yamakwa, T., Horio, K.: Self-organizing relationship (SOR) network. IEICE Transactions on Fundamentals E82-A, 1674–1678 (1999)
Horio, K., Haraguchi, T., Yamakawa, T.: An Intuitive Contrast Enhancement of an Image Data Employing the Self-Organizing Relationship (SOR) Network. In: Proceedings of International Joint Conference on Neural Networks (IJCNN 1999), pp. 10–16 (1999)
Horio, K., Yamakawa, T.: Adaptive Self-Organizing Relationship Network and Its Application to Adaptive Control. In: Proceedings of the 6th International Conference on Softcomputing and Information/Intelligent Systems (IIZUKA 2000), pp. 299–304 (2000)
Laumond, J.P., Sekhavat, S., Lamiraux, F.: Guidelines in Nonholonomic Motion Planning for Mobile Robots. Robot Motion Planning and Control, 229 (1998)
Brockett, R.W.: Asymptotic stability and feedback stabilization. In: Brockket, R., Millman, R., Sussman, H. (eds.) Differential Geometric Control Theory, pp. 181–191. Birkhauser, Boston (1983)
Pomet, J.B.: Explicit design of time-varying stabilization control laws for a class of controllable systems without drift. Systems & Control Letters 18(2), 147–158 (1992)
Tilbury, D., Murray, R.M., Sastry, S.S.: Trajectory Generation for the N-Trailer Problem Using Goursat Normal Form. IEEE Transactions on Automatic Control 40(5), 802–819 (1995)
Sampei, M., Tamura, T., Kobayashi, T., Shibui, N.: Arbitrary path tracking control of articulated vehicles using nonlinear control theory. IEEE Transactions on Control Systems Technology 1(4), 587–592 (1995)
Kolmanovsky, M., Reyhanoglu, M., McClamroch, N.H.: Switched mode feedback control laws for nonholonomic systems in extended power form. Systems & Control Letters 27(1), 29–36 (1996)
Prieur, C., Astolfi, A.: Robust stabilization of chained systems via hybrid control. In: Proceedings of the 41st IEEE Conference on Decision and Control, pp. 522–527 (2002)
Nguyen, D., Widrow, B.: The truck backer-upper: an example of self-learning in neural network. In: Proceedings of International Joint Conference on Neural Networks (IJCNN 1989), pp. 357–363 (1989)
Kong, S.G., Kosko, B.: Adaptive fuzzy-systems for backing-up a truck-and-trailer. IEEE Transactions on Neural Networks 3(2), 211–223 (1992)
Tanaka, K., Sano, M.: A robust stabilization problem of fuzzy control systems and its application to backing up control of a truck-trailer. IEEE Transactions on Fuzzy Systems 2(2), 119–133 (1994)
Ichihashi, H., Miyoshi, T., Nagasaka, K., Tokunaga, M., Wakamatsu, T.: A Neurofuzzy Approach to Variational Problems by Using Gaussian Membership Functions. International Journal of Approximate Reasoning 13(4), 287–302 (1995)
Hong, C.S., Won, J.M., Lee, J.S.: Multi-Thread Evolutionary Programming and Its Application to Truck-and-Trailer Backer-Upper Control. IEICE Transactions on Fundamentals E84-A(2), 597–603 (2001)
Zadeh, L.A.: Fuzzy Sets. Information and Control 8, 338–353 (1965)
Zadeh, L.A.: The concept of a new approach to the analysis of complex systems and decision processes. IEEE Transactions on Systems, Man and Cybernetics 3(1), 28–44 (1973)
Zadeh, L.A.: Knowledge representation in fuzzy logic. IEEE Transactions on Knowledge and Data Engineering 1, 89–100 (1989)
Mamdani, E.H., Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies 7(1), 1–13 (1975)
Mamdani, E.H.: Applications of fuzzy logic to approximate reasoning using linguistic synthesis. IEEE Transactions on Computers 26(12), 1182–1191 (1977)
Takagi, T., Sugeno, M.: Fuzzy Identification of Systems and Its Applications to Modeling and Control. IEEE Transactions on Systems, Man and Cybernetics 15(1), 116–132 (1985)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)
Koga, T., Horio, K., Yamakawa, T.: Applications of brain-inspired SOR network to controller design and knowledge acquisition. In: Proceedings of 5th Workshop on Self-Organizing Maps (WSOM 2005), CD-ROM (2005)
Koga, T., Horio, K., Yamakawa, T.: The Self-Organizing Relationship (SOR) Network Employing Fuzzy Inference Based Heuristic Evaluation. Neural Networks 19, 799–811 (2006)
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Yamakawa, T., Koga, T. (2008). Bio-inspired Self-Organizing Relationship Network as Knowledge Acquisition Tool and Fuzzy Inference Engine. In: Zurada, J.M., Yen, G.G., Wang, J. (eds) Computational Intelligence: Research Frontiers. WCCI 2008. Lecture Notes in Computer Science, vol 5050. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68860-0_8
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