Novel Strategy for Weight Initialization in Sigmoidal Feed-forward Artificial Neural Networks
Publication details: New Delhi STM Journals 2018Edition: Vol 5 (1), Jan- AprDescription: 62-75pSubject(s): Online resources: In: Journal of artificial intelligence research and advances (JoAIRA)Summary: paper, a novel method of weight initialization is proposed. The proposed method of weight initialization distributes the initial weights and thresholds in such a manner that they lie in different regions of the activation function used at the hidden layer. The proposed method is compared with six other popular weight initialization methods on ten function approximation problems using the RPROP (Resilient Back-propagation) and Levenberg-Marquardt algorithms for training. Two types of activation functions viz. tan hyperbolic and logarithmic sigmoidal functions are used for analysis and comparison.| Item type | Current library | Status | Barcode | |
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School of Engineering & Technology Archieval Section | Not for loan | 2021-2021444 |
paper, a novel method of weight initialization is proposed. The proposed method of weight initialization distributes the initial weights and thresholds in such a manner that they lie in different regions of the activation function used at the hidden layer. The proposed method is compared with six other popular weight initialization methods on ten function approximation problems using the RPROP (Resilient Back-propagation) and Levenberg-Marquardt algorithms for training. Two types of activation functions viz. tan hyperbolic and logarithmic sigmoidal functions are used for analysis and comparison.
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