Nonlinear signal processing

Reinforcement Learning (RL) agents can learn to control a nonlinear system without using a model of the system. However, having a model brings benefits, mainly in terms of a reduced number of unsuccessful trials before achieving acceptable control performance. Several modelling approaches have been used in the RL domain, such as neural networks, local linear regression, or Gaussian processes. In this article, we focus on a technique that has not been used much so far:\ symbolic regression, based on genetic programming.

The proposed hardware architecture is modelled by Verilog HDL and synthesized by a Synopsys Design compiler with Semiconductor Manufacturing International Corporation (SMIC) 65-nm CMOS technology. The upload files are the systhesis reports.

When heterogeneous honeycomb materials are cut using ultrasonic techniques, the ultrasonic frequency tends to vary over time, thereby degrading the quality of the machining. This variation can be addressed with a proportional–integral–derivative (PID) controller; however, these controllers perform relatively poorly in fixed parameter tracking scenarios. In contrast, this paper proposes a tracking model that combines fuzzy and PID control.