Hierarchical Rule-Base Reduction Based ANFIS With Online Optimization
Through DDPG
Abstract
This paper presents a comprehensive approach to designing and optimizing
a Hierarchical Rule-Base Reduction (HRBR) based Adaptive-Network-Based
Fuzzy Inference System (ANFIS) for symmetric linguistic variables.
Specifically, the linguistic joint membership functions that underlie
the ANFIS are defined, focusing on symmetrical inputs/outputs and
jointly optimized trapezoid membership functions to reduce the number of
training parameters. Further optimizations for the ANFIS were derived
based on design assumptions, including training the membership functions
on closed or single-sided domains. The optimal output membership weights
based on mean square error optimization were also symbolically obtained.
The online training of the ANFIS’s input/output membership functions was
performed using the DDPG (Deep Deterministic Policy Gradient) algorithm.
A simulated skid-steered vehicle was used to validate the approach and
performed waypoint-to-waypoint path following. Experimental results
using the Clearpath Jackal demonstrated that the ANFIS model converged
quickly, typically within 6 to 10 episodes of training, from an initial
MAE and RMSE of 0.88 and 1.02 meters, respectively, to a final MAE and
RMSE of 0.087 and 0.10 meters. The results highlight the effectiveness
of the ANFIS approach for vehicular robotics applications and suggest
promising avenues for future research and development.