Knowledge Extracted from Recurrent Deep Belief Network for Real Time Deterministic Control

TL;DR

This paper presents a method to extract IF-THEN rules from recurrent deep belief networks, enabling faster inference for real-time deterministic control in industrial applications.

Contribution

It introduces a novel knowledge extraction technique from deep belief networks that improves computational speed for real-time control tasks.

Findings

Effective knowledge extraction as IF-THEN rules

Improved inference speed demonstrated on benchmark datasets

Potential for real-time industrial control applications

Abstract

Recently, the market on deep learning including not only software but also hardware is developing rapidly. Big data is collected through IoT devices and the industry world will analyze them to improve their manufacturing process. Deep Learning has the hierarchical network architecture to represent the complicated features of input patterns. Although deep learning can show the high capability of classification, prediction, and so on, the implementation on GPU devices are required. We may meet the trade-off between the higher precision by deep learning and the higher cost with GPU devices. We can success the knowledge extraction from the trained deep learning with high classification capability. The knowledge that can realize faster inference of pre-trained deep network is extracted as IF-THEN rules from the network signal flow given input data. Some experiment results with benchmark tests for time series data sets showed the effectiveness of our proposed method related to the computational speed.