Perception control with improved expectation learning through multilayered neural networks

Sherwin Guirnaldo, Keigo Watanabe, Kiyotaka Izumi, Kazuo Kiguchi

研究成果: Contribution to journalLetter査読

4 被引用数 (Scopus)


In this paper, we investigate the viability of multilayered neural network (NN)-based extension of a conventional "perception" control concept. The perception process selects and completes the information from the system to be controlled before passing it to the controlling agent so that control is not lost when sensory information from the system is incomplete. The perception process produces an expectation of the next set of information to be received from the system. The expectation is used to replace missing parts of the information received and it also influences the next perception. In the existing work, each of the expectation elements is linearly acquired such that the expectation tells only the dominant information in the recent past, i.e., this approach has no capability to sense the trend and the dynamics in the information. This handicap could become a serious problem when the perception process is applied to real physical systems. Here, we introduce an extension of the perception control process by using a radial basis function (RBF) feedforward NN to learn the trend and the dynamics in the information and produce the expectation of the next observation. Through some simulation comparisons, we show that the proposed RBFNN-based method is better than the existing one.

ジャーナルIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
出版ステータス出版済み - 6 2004

All Science Journal Classification (ASJC) codes

  • 制御およびシステム工学
  • ソフトウェア
  • 情報システム
  • 人間とコンピュータの相互作用
  • コンピュータ サイエンスの応用
  • 電子工学および電気工学


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