Generalization Ability of Extended Cascaded Artificial Neural Network Architecture

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概要

• 論文の詳細を見る

• We present an extension of the previously proposed 3-layer feedforward network called a cascaded network [10]. Cascaded networks are trained to realize category classification employing binary input vectors and locally represented binary target output vectors. To realize a nonlinearly separable task the extended cascaded network presented here is constructed by introducing high order cross producted inputs at the input layer. In the construction of the cascaded network, two 2-layer networks are first trained independently by delta rule [12] and then cascaded. After cascading, the intermediate layer can be understood as a hidden layer which is trained to attain preassigned saturated outputs in response to the training set. In a cascaded network trained to categorize binary image patterns, saturation of hidden outputs reduces the effect of corrupted disturbances presented in the input. We demonstrated that the extended cascaded network was able to realize a nonlinearly separable task and yielded better generalization ability than the Backpropagation network.

• 社団法人電子情報通信学会の論文
• 1993-10-25

著者

• Kamruzzaman Joarder

  the Department of Electrical & Electronic Engineering, Bangladesh University of Engineering and Tech

• Kumagai Yukio

  the Department of Computer Science and Systems Engineering, Muroran Institute of Technology

• Kumagai Yukio

  Department Of Computer Science And Systems Engineering Muroran Institute Of Technology

• Kamruzzaman J

  Monash Univ. Aus

• Hikita Hiromitsu

  Department of Mechanical Systems Engineering, Muroran Institute of Technology

• Hikita Hiromitsu

  the Department of Mechanical Systems Engineering, Muroran Institute of Technology

• Hikita Hiromitsu

  Department Of Mechanical Systems Engineering Muroran Institute Of Technology

関連論文

• Invariant Object Recognition by Artificial Neural Network Using Fahlman and Lebiere's Learning Algorithm
• Performance Formulation and Evaluation of Associative Memory Extended to Higher Order
• Generalization Ability of Extended Cascaded Artificial Neural Network Architecture
• Comparison of Convergence Behavior and Generalization Ability in Backpropagation Learning with Linear and Sigmoid Output Units
• Robust Performance Using Cascaded Artificial Neural Network Architecture
• Incremental Learning and Generalization Ability of Artificial Neural Network Trained by Fahlman and Lebiere's Learning Algorithm

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