Robust Performance Using Cascaded Artificial Neural Network Architecture

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概要

• 論文の詳細を見る

• It has been reported that generalization performance of multilayer feedforward networks stongly depends on the attainment of saturated hidden outputs in response to the training set.^<(6)> Usually standard Backpropagation (BP) network mostly uses intermediate values of hidden units as the internal representation of the training patterns. In this letter, we propose construction of a 3-layer cascaded network in which two 2-layer networks are first trained independently by delta rule^<(10)> and then cascaded. After cascading, the intermediate layer can be viewed as hidden layer which is trained to attain preassigned saturated outputs in response to the training set. This network is particularly easier to construct for linearly separable training set, and can also be constructed for nonlinearly separable tasks by using higher order inputs at the input layer or by assigning proper codes at the intermediate layer which can be obtained from a trained Fahlman and Lebiere's network.^<(6)> Simulation results show that, at least, when the training set is linearly separable, use of the proposed cascaded network significantly enhances the generalization performance compared to BP network, and also maintains high generalization ability for nonlinearly separable training set. Performance of cascaded network depending on the preassigned codes at the intermediate layer is discussed and a suggestion about the preassigned coding is presented.

• 社団法人電子情報通信学会の論文
• 1993-06-25

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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