A novel intelligent fuzzy agent based on input-output association

The inhabited environments are MIMO, uncertainly, and nonlinear complex systems. This paper presents a novel intelligent fuzzy agent(IFA) based on input-output associational algorithm for intelligent inhabited environments. An input-output dynamic associational algorithm based on Hebb learning is proposed, which can divide a complex system into multiple simple systems and eliminate the irrelevant data from the learning data to improve the learning rate. The IFA learns the user´s preferences according to the manually operation of the user and proactively controls the environments. Initially the IFA extracts the membership functions and fuzzy rules from the data captured. After that the IFA learns the membership functions and optimizes the fuzzy rules online when the user´s preferences change. The experience results show that the proposed system is effective.