Neuro-Fuzzy Support of Knowledge Management in Social Regulation

The aim of the paper is to demonstrate the neuro-fuzzy support of knowledge management in social regulation. Knowledge, defined as human capability of making data and information useful for decision making processes, could be understood for social regulation purposes as explicit and tacit. Explicit knowledge relates to the community culture indicating how things work in the community based on social policies and procedures. Tacit knowledge is ethics and norms of the community. The former could be codified, stored and transferable in order to support decision making, while the latter being based on personal knowledge, experience and judgments is difficult to codify and store. However, since the tacit knowledge is expressed mainly through linguistic information, it can be stored and, therefore, support the knowledge management in social regulation through the application of fuzzy and neuro-fuzzy logic. Fuzzy logic modeling holds that human values inform the operation of the fuzzy logic by which the outcomes of interactions between interdependent members of any community are determined. With the system simulation where high precision is not required and parameters can be easily estimated for measurement, the fuzzy control model can be applied to estimating the appropriateness of self-organisation of the community. The model incorporates observed behavioral patterns seeking to explain their effects. The neuro-fuzzy approach is based on the integration of artificial neural networks and fuzzy inference systems. Neural network learning algorithms are used to fine tune the parameters of fuzzy inference system. Consequently, the neuro-fuzzy technique provides an ability to handle imprecision and uncertainty from data and to refine them by a learning algorithm. Applied in social regulation the neuro-fuzzy model creates fuzzy rules, which are easy to comprehend because of its linguistic terms and the structure of if-then rules. Neuro-fuzzy models implementing Takagi-Sugeno Kang if-then rules and Mamdani type fuzzy inference systems are tested with tobacco smoking enforcement efforts. The obtained results demonstrate the relevance of each model to knowledge management in social regulation.