Neural Network-Based Fuzzy Control Surface Implementation

Keywords

car parking; fuzzy control surface; Fuzzy Controller; LabVIEW; Neural Network

Abstract

This paper proposes a new design methodology of two-input and one-output fuzzy logic controller by training an Artificial Neural Network (ANN) that approximates a fuzzy control surface resulting from a basic fuzzy controller. The main purpose of this approach is to fully exploit the Artificial Neural Network (ANN) feature by translating the expertise of controlling the plant into two stages. In the first stage, our methodology mathematically presents the fuzzy rules and the procedure of obtaining a fuzzy control surface. In the second stage, we map the resultant fuzzy control surface with an ANN model that can be easily calculated. We have implemented the trained ANN with the LabVIEW2009 program to control the car parking system, whose simulation results established the validity of the proposed controller.

Publication Date

2-23-2016

Publication Title

2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015

Number of Pages

113-117

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/GlobalSIP.2015.7418167

Socpus ID

84964780490 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84964780490

This document is currently not available here.

Share

COinS