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Learning using distance based training algorithm for pattern recognition

PATTERN RECOGNITION LETTERS 2004年第2期25卷 189-196页

作者： Seow, MJ Asari, VK Old Dominion Univ Dept Elect & Comp Engn VLSI Syst Lab Norfolk VA 23529 USA

A modular approach for improving the performance of 2-D Hopfield neural network is presented in this paper. The approach is inspired by biological visual perception phenomenon. The training method introduces a decaying distance factor into the Hebbian learning rule for image processing applications. The value of the distance factor varies based on the spatial location of the neurons with respect to the neuron under consideration. Experiments performed with character images show that the new approach can learn and recognize patterns very effectively. (C) 2003 Elsevier B.V. All rights reserved.

关键词： learning algorithm pattern recognition hopfield network distance based training algorithm

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distance-based sparse associative memory neural network algorithm for pattern recognition

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NEURAL PROCESSING LETTERS 2006年第1期24卷 67-80页

作者： Chen, Lei Chen, Songcan Nanjing Univ Aeronaut & Astronaut Dept Comp Sci & Engn Nanjing 210016 Peoples R China Nanjing Univ Posts & Telecommun Dept Comp Sci & Technol Nanjing 210003 Peoples R China

A sparse two-Dimension distance weighted approach for improving the performance of exponential correlation associative memory (ECAM) and modified exponential correlation associative memory (MECAM) is presented in this paper. The approach is inspired by biological visual perception mechanism and extensively existing sparse small-world network phenomenon. By means of the approach, the two new associative memory neural networks, i.e., distance-based sparse ECAM (DBS-ECAM) and distance-based sparse MECAM (DBS-MECAM), are induced by introducing both the decaying two-Dimension distance factor and small-world architecture into ECAM and MECAM's evolution rule for image processing application. Such a new configuration can reduce the connection complexity of conventional fully connected associative memories so that makes AM' VLSI implementation easier. More importantly, the experiments performed on the binary visual images show DBS-ECAM and DBS-MECAM can learn and recognize patterns more effectively than ECAM and MECAM, respectively.

关键词： associative memory (AM) neural network sparse connection architecture exponential correlation associative memory (ECAM) distance based training algorithm pattern recognition

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