A Novel Parameter Reduction Method for Simplifying Improved RC-IDeepRT

T Rajalakshmi

doi:10.17485/ijst/v14i1.1311

Abstract

Objectives: To decrease the model parameters in Reinforcement learning with Compressed Improved DeepRT (RC-IDeepRT) for Cyber-Physical System (CPS) data analysis, a method is proposed in this study. Methods: A parameter reduction method is proposed to decrease the model parameter by combining pruning and factorization. The pruning may not save the memory usage in traditional computers unless sparse matrices are supported and explicitly used. However, it directly reduces the usage of synapses in neuromorphic architecture. This method minimizes the total number of required neurons and synapses given a trained model. Findings: The integration of factorization and pruning allows creating sparsely connected reinforcement learning with deep learning network from a given trained network. The dangling connections in the network are determined and the remaining connections of the dangling neurons are further pruned. The pruned network is cost-effective and it classifies the CPS data effectively. Novelty: This proposed method tries to minimize the number of parameters in RC-IDeepRT. The number of parameters becomes a good surrogate metric assuming the deep learning architecture accommodates sparse networks effectively. The reduced model parameters are used for CPS data classification. Keywords: Cyber-physical system; deep learning; DeepRT; reinforcement with compressed improved DeepRT; parameter reduction method; pruning; factorization

Highlights

Cyber-Physical System (CPS) [1,2,3,4,5,6] which are designed to revolutionize society by introducing innovations is rising rapidly in the world
Improved DeepRT (IDeepRT) is the integration of Convolutional Neural Network (CNN) [9] and Recurrent
The efficiency of the Fuzzy Logic Concept [14], Genetic Algorithm-Support Vector Machine (GA-SVM) [15], IFSPT-RC-IDeepRT, and IFSPT-RC-PRIDeepRT based CPS data classification methods are analyzed in terms of Accuracy, Precision, Recall, and F_measure

Summary

Introduction

Cyber-Physical System (CPS) [1,2,3,4,5,6] which are designed to revolutionize society by introducing innovations is rising rapidly in the world. The dynamic nature of sensors in CPS [7] creates a huge volume of data which requires effective techniques for CPS data analysis. Because of gaining significant exposure with topics such as intelligent cities, smart homes with an appliance network, mobility services, and environmental monitoring, smart grids, the classification of CPS data is more required. An Improved DeepRT (IDeepRT) [8] was proposed to enhance the CPS data classification accuracy. IDeepRT is the integration of Convolutional Neural Network (CNN) [9] and Recurrent. Neural Network (RNN) which gets the best feature space as input and classified the CPS data.

Objectives

Methods

Results

Conclusion