Matches in SemOpenAlex for { <https://semopenalex.org/work/W2072647670> ?p ?o ?g. }
Showing items 1 to 83 of
83
with 100 items per page.
- W2072647670 abstract "Many proposals have been presented for the acquisition of inverse models in multilayered neural networks. However, most are concerned with the backpropagation rule or its improvement. In learning in a multilayered neural network based on the backpropagation rule, there must be a supervisor signal for the output layer, and there must be a particular path to propagate the learning signal in the reverse direction. In addition, convergence is slow due to the use of the method of steepest descent in updating the weights. Consequently, this paper proposes a forward-propagation rule in which the neural network model is trained by propagating the motion error exhibited by the control object in the forward direction in the neural network. In the proposed algorithm, the extended Newton's method is used to derive the goal signal (which corresponds to the supervisor signal) in the hidden layer and the output layer. Since linear multiple regression can be used in weight updating for realizing the goal signals, the iteration of weight updating can be reduced compared to the method of steepest descent. A computer simulation was performed for acquisition of a two-link arm model, and the effectiveness of the proposed learning scheme was verified. © 2005 Wiley Periodicals, Inc. Electron Comm Jpn Pt 2, 88(2): 59–68, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjb.20148" @default.
- W2072647670 created "2016-06-24" @default.
- W2072647670 creator A5002661022 @default.
- W2072647670 creator A5018241300 @default.
- W2072647670 creator A5044606811 @default.
- W2072647670 date "2005-01-01" @default.
- W2072647670 modified "2023-09-25" @default.
- W2072647670 title "A forward-propagation learning rule for acquiring inverse models in multilayered neural networks" @default.
- W2072647670 cites W1498436455 @default.
- W2072647670 cites W1501378290 @default.
- W2072647670 cites W1571179961 @default.
- W2072647670 cites W1997508571 @default.
- W2072647670 cites W1998751889 @default.
- W2072647670 cites W2043968544 @default.
- W2072647670 cites W2104893957 @default.
- W2072647670 cites W2127946960 @default.
- W2072647670 cites W4300402905 @default.
- W2072647670 doi "https://doi.org/10.1002/ecjb.20148" @default.
- W2072647670 hasPublicationYear "2005" @default.
- W2072647670 type Work @default.
- W2072647670 sameAs 2072647670 @default.
- W2072647670 citedByCount "3" @default.
- W2072647670 countsByYear W20726476702014 @default.
- W2072647670 countsByYear W20726476702015 @default.
- W2072647670 crossrefType "journal-article" @default.
- W2072647670 hasAuthorship W2072647670A5002661022 @default.
- W2072647670 hasAuthorship W2072647670A5018241300 @default.
- W2072647670 hasAuthorship W2072647670A5044606811 @default.
- W2072647670 hasConcept C11413529 @default.
- W2072647670 hasConcept C123614077 @default.
- W2072647670 hasConcept C153258448 @default.
- W2072647670 hasConcept C154945302 @default.
- W2072647670 hasConcept C155032097 @default.
- W2072647670 hasConcept C162324750 @default.
- W2072647670 hasConcept C17744445 @default.
- W2072647670 hasConcept C199360897 @default.
- W2072647670 hasConcept C199539241 @default.
- W2072647670 hasConcept C207467116 @default.
- W2072647670 hasConcept C2524010 @default.
- W2072647670 hasConcept C2777303404 @default.
- W2072647670 hasConcept C2779110517 @default.
- W2072647670 hasConcept C2779127903 @default.
- W2072647670 hasConcept C2779843651 @default.
- W2072647670 hasConcept C33923547 @default.
- W2072647670 hasConcept C41008148 @default.
- W2072647670 hasConcept C50522688 @default.
- W2072647670 hasConcept C50644808 @default.
- W2072647670 hasConceptScore W2072647670C11413529 @default.
- W2072647670 hasConceptScore W2072647670C123614077 @default.
- W2072647670 hasConceptScore W2072647670C153258448 @default.
- W2072647670 hasConceptScore W2072647670C154945302 @default.
- W2072647670 hasConceptScore W2072647670C155032097 @default.
- W2072647670 hasConceptScore W2072647670C162324750 @default.
- W2072647670 hasConceptScore W2072647670C17744445 @default.
- W2072647670 hasConceptScore W2072647670C199360897 @default.
- W2072647670 hasConceptScore W2072647670C199539241 @default.
- W2072647670 hasConceptScore W2072647670C207467116 @default.
- W2072647670 hasConceptScore W2072647670C2524010 @default.
- W2072647670 hasConceptScore W2072647670C2777303404 @default.
- W2072647670 hasConceptScore W2072647670C2779110517 @default.
- W2072647670 hasConceptScore W2072647670C2779127903 @default.
- W2072647670 hasConceptScore W2072647670C2779843651 @default.
- W2072647670 hasConceptScore W2072647670C33923547 @default.
- W2072647670 hasConceptScore W2072647670C41008148 @default.
- W2072647670 hasConceptScore W2072647670C50522688 @default.
- W2072647670 hasConceptScore W2072647670C50644808 @default.
- W2072647670 hasLocation W20726476701 @default.
- W2072647670 hasOpenAccess W2072647670 @default.
- W2072647670 hasPrimaryLocation W20726476701 @default.
- W2072647670 hasRelatedWork W1605549466 @default.
- W2072647670 hasRelatedWork W1967604662 @default.
- W2072647670 hasRelatedWork W1983738889 @default.
- W2072647670 hasRelatedWork W2034776141 @default.
- W2072647670 hasRelatedWork W2059915984 @default.
- W2072647670 hasRelatedWork W2121423846 @default.
- W2072647670 hasRelatedWork W2136975277 @default.
- W2072647670 hasRelatedWork W2148599005 @default.
- W2072647670 hasRelatedWork W2240896738 @default.
- W2072647670 hasRelatedWork W2609564064 @default.
- W2072647670 isParatext "false" @default.
- W2072647670 isRetracted "false" @default.
- W2072647670 magId "2072647670" @default.
- W2072647670 workType "article" @default.