Abstract
In this paper, we use a recent artificial neural architecture called Cooperative Maximum Likelihood Hebbian Learning (CMLHL) in order to categorize the necessities for the Acquisition, Transfer and Updating of Knowledge of the different departments of a firm. We apply Maximum Likelihood Hebbian learning to an extension of a negative feedback network characterised by the use of lateral connections on the output layer. These lateral connections have been derived from the Rectified Gaussian distribution. This technique is used as a tool to develop a part of a Global and Integral Model of business Management, which brings about a global improvement in the firm, adding value, flexibility and competitiveness. From this perspective, the model tries to generalise the hypothesis of organizational survival and competitiveness, so that the organisation that is able to identify, strengthen, and use key knowledge will reach a pole position.
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© 2004 Springer-Verlag Berlin Heidelberg
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Corchado, E., Fyfe, C., Sáiz, L., Lara, A. (2004). Development of a Global and Integral Model of Business Management Using an Unsupervised Model. In: Yang, Z.R., Yin, H., Everson, R.M. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2004. IDEAL 2004. Lecture Notes in Computer Science, vol 3177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28651-6_73
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DOI: https://doi.org/10.1007/978-3-540-28651-6_73
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22881-3
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