Fuzzy associative memory-driven approach to knowledge integration

We propose a knowledge integration mechanism that yields a cooperated knowledge by integrating user knowledge, expert knowledge and machine knowledge within the fuzzy logic-driven framework, and then refines it with a fuzzy associative memory (FAM) to enhance the reasoning performance. The proposed knowledge integration mechanism is applied for the prediction of Korea stock price index (KOSPI). Experimental results show that the FAM-driven approach can enhance the reasoning performance by refining the cooperated knowledge of fuzzy logic-driven framework. This result means that the FAM-driven approach can be a robust guidance for knowledge integration.