#Fukuda, T., Y. Komata, and T. Arakawa, "Recurrent Neural Networks with Self-Adaptive GAs for Biped Locomotion Robot", In: Proceedings of the International Conference on Neural Networks ICNN'97, IEEE Press (1997)  Home/Search   Document Not in Database   Summary   Related Articles   Check

....learning. 1. Introduction The complexity and the dynamics of real world problems, such as adaptive speech recognition and language acquisition [21,34,41] adaptive intelligent prediction and control systems [1] intelligent agent based systems and adaptive agents on the Web [81] mobile robots [20], visual monitoring systems and multi modal information processing [37,54] large Bio informatics data processing, and many more [2,4] require sophisticated methods and tools for building on line, adaptive, knowledge based intelligent systems (IS) Such systems should be able to: 1) learn fast ....

....problem space. Another parameter, Nagg, can also be removed as a restriction after certain distribution of the input output space is achieved and aggregation can be applied after every single example is learned. In another scenario, genetic algorithms (GA) and evolutionary programming techniques [23,20,78,79] can be applied to optimise the EFuNNs structural and functional parameters through evolving populations of EFuNNs over generations and evaluating each EFuNN in the population at certain time intervals. After that, only the best EFuNNs are kept and reproduced in another population, which process ....

Fukuda, T., Y. Komata, and T. Arakawa, "Recurrent Neural Networks with SelfAdaptive GAs for Biped Locomotion Robot", In: Proceedings of the International Conference on Neural Networks ICNN'97, IEEE Press (1997)

....learning. 1. Introduction The complexity and the dynamics of real world problems, such as adaptive speech recognition and language acquisition [21,34,41] adaptive intelligent prediction and control systems [1] intelligent agent based systems and adaptive agents on the Web [81] mobile robots [20], visual monitoring systems and multi modal information processing [37,54] large Bio informatics data processing, and many more [2,4] require sophisticated methods and tools for building on line, adaptive, knowledge based intelligent systems (IS) Such systems should be able to: 1) learn fast ....

....problem space. Another parameter, Nagg, can also be removed as a restriction after certain distribution of the input output space is achieved and aggregation can be applied after every single example is learned. In another scenario, genetic algorithms (GA) and evolutionary programming techniques [23,20,78,79] can be applied to optimise the EFuNNs structural and functional parameters through evolving populations of EFuNNs over generations and evaluating each EFuNN in the population at certain time intervals. After that, only the best EFuNNs are kept and reproduced in another population, which process ....

Fukuda, T., Y. Komata, and T. Arakawa, "Recurrent Neural Networks with Self- Adaptive GAs for Biped Locomotion Robot", In: Proceedings of the International Conference on Neural Networks ICNN'97, IEEE Press (1997) 35

....Arai, Fumihito, 16, 44, 81, 323, 1321, 1324, 1325, 1326, 1327, 1331, 1581, 1345] Arai, K. I. 1121] Arai, K. 799] Arai, T. 828] Arakaki, K. 1017] Arakaki, Kouichi, 739, 768, 791, 964, 969] Arakaki, S. 1559] Arakawa, Atsushi, 682] Arakawa, Masao, 1051] Arakawa, Takemasa, [465, 745, 800, 872, 994, 1027, 1033, 1043] Arakawa, T. 899, 935] Araki, D. 841] Araki, K. 476, 795, 992, 1025] Araki, Keijiro, 200, 512, 842, 999] Araki, M. 694, 750, 1080] Araki, Miyuhiko, 1454] Arao, Masaki, 178] Arita, M. 878] Asada, Minoru, 1061] Asahina, T. 1288] Asai, K. 466] Asai, Kiyoshi, 107, ....

....971, 1045, 1318] Fujimura, N. 463] Fujino, A. 186, 947, 1183] Fujino, Atsuya, 644] Fujita, H. 869, 980, 1170, 1196] Fujita, Kikuo, 1115, 1319] Fujita, Satoshi, 723] Fujita, S. 15] Fujiwara, Y. 813, 1253] Fukuda, T. 44, 300, 336, 401, 745, 789, 814, 867] Fukuda, Toshio, [16, 24, 38, 77, 81, 132, 148, 178, 187, 219, 252, 256, 323, 341, 422, 441, 452, 454, 455, 456, 465, 544, 712, 756, 774, 783, 792, 793, 794, 800, 835, 857, 872, 899, 935, 994, 996, 1011, 1027, 1033, 1043, 1139, 1577, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327, 1328, 1329, 1330, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1341, 1342, 1343, 1344, 1581, 1345, 1346, 1347] Fukuda, Toyoo, 1375, 1376] Fukui, T. 951, 1015] Fukumi, M. 480, 686, 747, 1106, 1119, 1222] Fukumi, Minoru, 547, 1348, 1349] Fukunaga, K. 413] Fukushige, T. 711] Fukushima, M. 687, 1067] Fukuyama, Y. 17, 1503, 295, 442] Fukuyama, Yoshikazu, 124, 423, 481, 688, 748] ....

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Toshio Fukuda, Youichirou Komata, and Takemasa Arakawa. Recurrent neural network with self-adaptive GAs for biped locomotion robot. In Proceedings of the 1997 IEEE international Confeence on Neural Networks, volume 3, pages 1710-1715, Houston, TX, 9.-12. June 1997. IEEE, Piscataway, NJ. yEI M178899/97 ga97dToFukuda.

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#Fukuda, T., Y. Komata, and T. Arakawa, "Recurrent Neural Networks with Self-Adaptive GAs for Biped Locomotion Robot", In: Proceedings of the International Conference on Neural Networks ICNN'97, IEEE Press (1997)

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Fukuda, T., Y. Komata, and T. Arakawa, "Recurrent Neural Networks with Self-Adaptive GAs for Biped Locomotion Robot", In: Proceedings of the International Conference on Neural Networks ICNN'97, IEEE Press (1997)

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