Application of an artificial immune system-based fuzzy neural network to a RFID-based positioning system

Authors:
R. J. Kuo;W. L. Tseng;F. C. Tien;T. Warren Liao
Affiliations:
Department of Industrial Management, National Taiwan University of Science and Technology, No. 43, Section 4, Kee-Lung Road, Taipei, Taiwan, ROC;Ting Hsin International Group, No.425, Wei-Ken Street, HEDA, Hang-Zhou, China;Department of Industrial Engineering and Management, National Taipei University of Technology, No. 1, Section 3, Chung-Hsiao East Road, Taipei, Taiwan, ROC;Department of Construction Management and Industrial Engineering, Louisiana State University, 3128 CEBA, Baton Rouge, LA 70803, USA
Venue:
Computers and Industrial Engineering
Year:
2012

Citing 21
Cited 2

The immune system, adaptation, and machine learning

Physica D
Learning internal representations by error propagation

Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Neural-Network-Based Fuzzy Logic Control and Decision System

IEEE Transactions on Computers - Special issue on artificial neural networks
Fuzzy logic, neural networks, and soft computing

Communications of the ACM
Structure optimization of fuzzy neural network by genetic algorithm

Fuzzy Sets and Systems - Special issue on fuzzy neural control
A learning algorithm of fuzzy neural networks with triangular fuzzy weights

Fuzzy Sets and Systems - Special issue on fuzzy neural control
Self-tuning fuzzy modeling with adaptive membership function, rules, and hierarchical structure based on genetic algorithm

Fuzzy Sets and Systems - Special issue on fuzzy neural control
Manufacturing process control through integration of neural networks and fuzzy model

Fuzzy Sets and Systems
A hybrid genetic fuzzy neural network algorithm designed for classification problems involving several groups

Fuzzy Sets and Systems
An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and artificial neural network

Fuzzy Sets and Systems
LANDMARC: Indoor Location Sensing Using Active RFID

PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
Architecture for an Artificial Immune System

Evolutionary Computation
Application areas of AIS: The past, the present and the future

Applied Soft Computing
A novel approach for ANFIS modelling based on full factorial design

Applied Soft Computing
Identification using ANFIS with intelligent hybrid stable learning algorithm approaches

Neural Computing and Applications
2009 Special Issue: A novel multi-epitopic immune network model hybridized with neural theory and fuzzy concept

Neural Networks
Advances in artificial immune systems

IEEE Computational Intelligence Magazine
Learning and optimization using the clonal selection principle

IEEE Transactions on Evolutionary Computation
Neural networks that learn from fuzzy if-then rules

IEEE Transactions on Fuzzy Systems
A neural fuzzy system with linguistic teaching signals

IEEE Transactions on Fuzzy Systems
Neuro-fuzzy rule generation: survey in soft computing framework

IEEE Transactions on Neural Networks

Intelligent forecasting system based on integration of electromagnetism-like mechanism and fuzzy neural network

Expert Systems with Applications: An International Journal
Application of an optimization artificial immune network and particle swarm optimization-based fuzzy neural network to an RFID-based positioning system

Information Sciences: an International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

Due to the rapid development of globalization, which makes supply chain management more complicated, more companies are applying radio frequency identification (RFID), in warehouse management. The obvious advantages of RFID are its ability to scan at high-speed, its penetration and memory. In addition to recycling, use of a RFID system can also reduce business costs, by indentifying the position of goods and picking carts. This study proposes an artificial immune system (AIS)-based fuzzy neural network (FNN), to learn the relationship between the RFID signals and the picking cart's position. Since the proposed network has the merits of both AIS and FNN, it is able to avoid falling into the local optimum and possesses a learning capability. The results of the evaluation of the model show that the proposed AIS-based FNN really can predict the picking cart position more precisely than conventional FNN and, unlike an artificial neural network, it is much easier to interpret the training results, since they are in the form of fuzzy IF-THEN rules.