Development of a Global and Integral Model of Business Management Using an Unsupervised Model

Abstract

AbstractIn 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.KeywordsEnergy FunctionBusiness ManagementMultivariate Normal DistributionLateral ConnectionUnsupervised ModelThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.