Semantic Inference based on a Fuzzy Cognitive Map using Self-Organizing Networks

In the management of large organizations and complex systems, presence of a structure in regard with useful pattern recognition can reduce margin of error for decision-making. In some grounds, it provides the context to predict effectively which may lead the efficiency of the organization to increase. Moreover, it offers more ideal application by which identifying bottleneck or loss and damage areas can be provided. By creating a semi-automatic strategy, planning a framework for semantic inference based on fuzzy cognitive map within pattern classification (problems) issues is necessitated according to which behaviors in different parts of organizations could be assessed and supervised by overcoming the complexity of dynamic environments. The obtained pattern must be actual so as the decisions made to prevent or solve problems act with better more practical results. The management can observe the extracted behavioral patterns utilizing the structure via data-by in the sector in organization review as well as strategic changes, based on which an issue may be decided. Comparing the recommended issue with the samples in the obtained patterns can anticipate its profitability and accept or decline the option.

Keywords:

Fuzzy cognitive map , Learning algorithms , Self-organizing networks