An adaptive fuzzy semantic memory model based on the computational principles of the human hippocampus

Fuzzy systems have been successfully applied to solve many engineering problems. However, traditional fuzzy systems are often manually crafted, and their structures (knowledge rule-bases) are static and cannot be trained or tuned to improve the system performance. This subsequently leads to an intense research on the autonomous construction and tuning of a fuzzy system directly from the observed training data to address the knowledge acquisition bottleneck. However, the complex and dynamic nature of real-world problems demanded that fuzzy systems be able to adapt their structures, parameters and ultimately evolve their intelligence to continuously address the non-stationary characteristics of their operating environments. This paper presents the evolving fuzzy semantic memory (eFSM) model, a neuro-fuzzy architecture with a continuously adaptive structure (rule-base). The computational principles responsible for the online identification of the proposed eFSM model and its evolving capability are based on the functional mechanisms of the human hippocampus, a brain construct that plays a significant role in the acquisition of the long-term human declarative memories.