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New approach to real-time adaptive learning control of neural networks based on an evolutionary algorithm (II)

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2 Author(s)
Sung-Ouk Chang ; Dept. of Intelligence Mech. Eng., Pusan Nat. Univ., South Korea ; Jin-Kul Lee

For pt.I see ibid., p.1871-6 (2001). In this study, in order to confirm the algorithms that are suggested from part I as an experimental result, as the applied results of the hydraulic servo system are very strong, a nonlinearity of the fluid in the computer simulation, the real-time adaptive learning control algorithm is validated. The evolutionary strategy has characteristics that are automatically adjusted in search regions with natural competition among many individuals. The error that is generated from the dynamic system is applied to the mutation equation. Competitive individuals are reduced with automatic adjustments of the search region in accordance with the error. In this paper, the individual parents and offspring can be reduced in order to apply evolutionary algorithms in real-time as in the description of part I. The possibility of a new approaching algorithm that is suggested from the computer simulation of part I would be proved as the verification of a real-time test and the consideration its influence from the actual experiment

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Industrial Electronics, 2001. Proceedings. ISIE 2001. IEEE International Symposium on  (Volume:3 )

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