DSP: A Differential Spatial Prediction Scheme for Comprehensive real industrial datasets

08/23/2020
by   Junjie Zhang, et al.
0

Inverse Distance Weighted models (IDW) have been widely used for predicting and modeling multidimensional space in multimodal industrial processes. However, the more complex the structure of multidimensional space, the lower the performance of IDW models, and real industrial datasets tend to have more complex spatial structure. To solve this problem, a new framework for spatial prediction and modeling based on deep reinforcement learning network is proposed. In the proposed framework, the internal relationship between state and action is enhanced by reusing the state values in the Q network, and the convergence rate and stability of the deep reinforcement learning network are improved. The improved deep reinforcement learning network is then used to search for and learn the hyperparameters of each sample point in the inverse distance weighted model. These hyperparameters can reflect the spatial structure of the current industrial dataset to some extent. Then a spatial distribution of hyperparameters is constructed based on the learned hyperparameters. Each interpolation point obtains corresponding hyperparameters from the hyperparametric spatial distribution and brings them into the classical IDW models for prediction, thus achieving differential spatial prediction and modeling. The simulation results show that the proposed framework is suitable for real industrial datasets with complex spatial structure characteristics and is more accurate than current IDW models in spatial prediction.

READ FULL TEXT

Authors

page 7

page 25

02/07/2019

Metaoptimization on a Distributed System for Deep Reinforcement Learning

Training intelligent agents through reinforcement learning is a notoriou...
02/22/2021

Deep Reinforcement Learning for Dynamic Spectrum Sharing of LTE and NR

In this paper, a proactive dynamic spectrum sharing scheme between 4G an...
09/05/2021

Temporal Aware Deep Reinforcement Learning

The function approximators employed by traditional image based Deep Rein...
11/03/2021

Autonomous Attack Mitigation for Industrial Control Systems

Defending computer networks from cyber attack requires timely responses ...
05/12/2020

Unbiased Deep Reinforcement Learning: A General Training Framework for Existing and Future Algorithms

In recent years deep neural networks have been successfully applied to t...
06/06/2022

Deep Reinforcement Learning for Cybersecurity Threat Detection and Protection: A Review

The cybersecurity threat landscape has lately become overly complex. Thr...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.