Document Detail

Learning in Stochastic Bit Stream Neural Networks.
MedLine Citation:
PMID:  12662578     Owner:  NLM     Status:  Publisher    
This paper presents learning techniques for a novel feedforward stochastic neural network. The model uses stochastic weights and the "bit stream" data representation. It has a clean analysable functionality and is very attractive with its great potential to be implemented in hardware using standard digital VLSI technology. The design allows simulation at three different levels and learning techniques are described for each level. The lowest level corresponds to on-chip learning. Simulation results on three benchmark MONK's problems and handwritten digit recognition with a clean set of 500 16 x 16 pixel digits demonstrate that the new model is powerful enough for the real world applications. Copyright 1996 Elsevier Science Ltd
Max van Daalen; John Shawe-Taylor; Jieyu Zhao
Related Documents :
19525198 - Learning robust cell signalling models from high throughput proteomic data.
16433028 - Simulating soft data to make soft data applicable to simulation.
17337268 - An on-line modified least-mean-square algorithm for training neurofuzzy controllers.
15272848 - Data mining and machine learning techniques for the identification of mutagenicity indu...
24737978 - Processing uncertain rfid data in traceability supply chains.
115188 - Non-progressive evolution, the red queen hypothesis, and the balance of nature.
Publication Detail:
Journal Detail:
Title:  Neural networks : the official journal of the International Neural Network Society     Volume:  9     ISSN:  1879-2782     ISO Abbreviation:  Neural Netw     Publication Date:  1996 Aug 
Date Detail:
Created Date:  2003-Mar-28     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8805018     Medline TA:  Neural Netw     Country:  -    
Other Details:
Languages:  ENG     Pagination:  991-998     Citation Subset:  -    
University of London, Egham, Surrey, TW20 0EX, UK
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

Previous Document:  A New Family of Multivalued Networks.
Next Document:  A Decomposition Principle for Complexity Reduction of Artificial Neural Networks.