In this paper, a novel self-organizing fuzzy neural network is proposed that constructed by an input–output mapping and monitored by a hierarchy of validity degrees. We define new operators called validification and devalidification to propagate validity into the six layers of proposed architecture. Self-organizing in structure learning is accomplished through a new measure that depends on error, the number of rules, and validity degrees. Additionally, a parameter learning condition is derived through studying the stability of proposed approach. To evaluate the proposed approach, an impedance controller is designed facing three challenges: different kinds of uncertainty, partial truth, and real-time realization. In the proposed controller, considering the challenge of partial truth, we assume that the manipulator’s inertia is known according to first-principle knowledge while other parts are uncertain. Simulation results show the effectiveness of the proposed approach in the presence of disturbance. The proposed approach emerges as a promising approach by involving self-organizing property and possibility (fuzzy) and validity aspects of information.
Presented on 12.02.2020 by Ayaz Hussain