Proceedings IJCIEOM – International Joint Conference on Industrial Engineering and Operations Management


ICIEOM2020_FULL_0007_37400

COMPARATIVE STUDY OF MULTI NEURONS & MULTI LAYERS ARTIFICIAL NEURAL NETWORK IN KNOWLEDGE MANAGEMENT


ÁREA: 07. Knowledge and Information Management

RESUMO:
Knowledge transfer impacts the firm?s performance and its ability to respond to changing landscape. Various knowledge management models capture the process of knowledge transfer. ANN can help simulate and predict the knowledge transfer outcome. However, ANN has various algorithms. This study discusses and develops a model based on the Artificial Neural Network (ANN) framework to transfer knowledge. Virtual reality based simulated animation study is explored as preferred knowledge transfer methodology. ANN offers multiple algorithms to train the model. Levenberg Marquardt (LM) is found to be one of most effective ANN algorithms with accurate and faster output. We further explore LM ANN algorithm and compare the same for efficacy and ability to train the Knowledge model with different combinations of Layers and Neurons. Further the study explores application of LM algorithm with layer-Neurons combinations for most optimum methodology for transfer of knowledge applications. Thus, the most optimum methodology to transfer knowledge is explored using ANN.

PALAVRAS-CHAVE:
knowledge transfer, artificial neural network, neurons, ann layers



DOI: 10.14488/ijcieom2020_full_0007_37400
AUTORES:

SHAILENDRA SINGH
INDIAN INSTITUTE OF MANAGEMENT , LUCKNOW, INDIA

SHANTANU BHATTACHARYA
INDIAN INSTITUTE OF TECHNOLOGY, KANPUR, INDIA

VENKATARAMANAIAH SADDIKUTI
INDIAN INSTITUTE OF MANAGEMENT , LUCKNOW, INDIA






ISSN ENEGEP: 23183349 / ISSN ICIEOM: 23178000


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