Inverted Pendulum Control Using Negative Data

In the training phase of learning algorithms, it is always important to have a suitable training data set. The presence of outliers, noise data, and inappropriate data always affects the performance of existing algorithms. The active learning method (ALM) is one of the powerful tools in soft computing inspired by the computation of the human brain. The operation of this algorithm is completely based on simple calculations and tries to model a Multi-Input Single-Output (MISO) system as a set of Single-Input Single-Output (SISO) subsystems and breaks a complex problem into several simpler problems. Each of the subsystems is then modeled by an Ink Drop Spread (IDS) plane. In this paper, to improve the performance of ALM, with changes in how it works, data called negative data has been used. In the ALM, it is possible to use negative data in the training phase by using the IDS operator. By doing so, a policy similar to the reward and punishment policy is in reinforcement learning methods is used. To investigate the accuracy of the proposed algorithm, some simulations in control have been done on an inverted pendulum system and the FVU value is 0.0143. The simulations results confirm the proper performance and increase the computational power of the proposed method compared to the existing method.