scholarly journals FUZZY NEURAL NETWORKS IN INTELLIGENT MANUFACTURING SYSTEMS

2014 ◽  
pp. 96-100
Author(s):  
Galina Setlak
Keyword(s):  
Neural Networks ◽  
Development Strategy ◽  
Manufacturing Systems ◽  
Quantitative Information ◽  
Intelligent Manufacturing ◽  
Fuzzy Neural Networks ◽  
Practical Implementation ◽  
Fuzzy Neural ◽  
Decision Making Processes ◽  
Product Development Strategy

This paper presents fuzzy neural networks, which are an expansion of classical neural networks. These networks can formally represent and process both the qualitative (linguistic) and quantitative information, which usually describe a complex, multidimensional systems or decision making processes. The second part presents the results of tests and a practical implementation of applications for decision support systems based on fuzzy neural networks used for strategic management and determination of product development strategy.

scholarly journals Predicting Subcontractor Performance Using Web-Based Evolutionary Fuzzy Neural Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Chien-Ho Ko
Keyword(s):  
Neural Networks ◽  
Fuzzy Neural Networks ◽  
Remote Access ◽  
Cost Overruns ◽  
Web Based ◽  
Fuzzy Neural ◽  
Decision Making Processes ◽  
High Level ◽  
Prediction Mechanism ◽  
Future Status

Subcontractor performance directly affects project success. The use of inappropriate subcontractors may result in individual work delays, cost overruns, and quality defects throughout the project. This study develops web-based Evolutionary Fuzzy Neural Networks (EFNNs) to predict subcontractor performance. EFNNs are a fusion of Genetic Algorithms (GAs), Fuzzy Logic (FL), and Neural Networks (NNs). FL is primarily used to mimic high level of decision-making processes and deal with uncertainty in the construction industry. NNs are used to identify the association between previous performance and future status when predicting subcontractor performance. GAs are optimizing parameters required in FL and NNs. EFNNs encode FL and NNs using floating numbers to shorten the length of a string. A multi-cut-point crossover operator is used to explore the parameter and retain solution legality. Finally, the applicability of the proposed EFNNs is validated using real subcontractors. The EFNNs are evolved using 22 historical patterns and tested using 12 unseen cases. Application results show that the proposed EFNNs surpass FL and NNs in predicting subcontractor performance. The proposed approach improves prediction accuracy and reduces the effort required to predict subcontractor performance, providing field operators with web-based remote access to a reliable, scientific prediction mechanism.

scholarly journals INTELLIGENT VEHICLE PARKING USING FUZZY-NEURAL NETWORKS

2013 ◽  
pp. 14-18
Author(s):  
Subhrajit Pradhan ◽  
Srikant Patnaik ◽  
Sukanta Kumar Tulo
Keyword(s):  
Neural Networks ◽  
Fuzzy Controller ◽  
Fuzzy Neural Networks ◽  
Intelligent Vehicle ◽  
Practical Implementation ◽  
Positioning Error ◽  
Neural Controller ◽  
Different Types ◽  
Fuzzy Neural ◽  
Refined Version

This paper analyzes the performance and practical implementation of fuzzy-neural networks for the autonomous motion of mobile robots. The designed fuzzy-neural controller is a refined version of a conventional fuzzy controller, and was trained to optimize a given cost function minimizing positioning error. It was found that the mobile robot with fuzzyneural controller presents good positioning and tracking performance for different types of desired trajectories. It was verified by computer simulation as well as experimentally using a laboratory-scale car-like robot model.

Modeling of Self-Induced Vibrations that Occur During the Machining Process of Casting Patterns with the Use of The Fuzzy-Neural Networks Method

2013 ◽  
Vol 58 (3) ◽  
pp. 871-875
Author(s):  
A. Herberg
Keyword(s):  
Neural Networks ◽  
Control System ◽  
Network Structure ◽  
Fuzzy Systems ◽  
Fuzzy Neural Networks ◽  
Cnc Machining ◽  
Machining Process ◽  
Modeling Process ◽  
Fuzzy Neural ◽  
Takagi Sugeno

Abstract This article outlines a methodology of modeling self-induced vibrations that occur in the course of machining of metal objects, i.e. when shaping casting patterns on CNC machining centers. The modeling process presented here is based on an algorithm that makes use of local model fuzzy-neural networks. The algorithm falls back on the advantages of fuzzy systems with Takagi-Sugeno-Kanga (TSK) consequences and neural networks with auxiliary modules that help optimize and shorten the time needed to identify the best possible network structure. The modeling of self-induced vibrations allows analyzing how the vibrations come into being. This in turn makes it possible to develop effective ways of eliminating these vibrations and, ultimately, designing a practical control system that would dispose of the vibrations altogether.

Hotbox level detection of railway vehicle using fuzzy neural networks

2013 ◽  
Vol 33 (9) ◽  
pp. 2566-2569 ◽  
Author(s):  
Zhuanling CUI ◽  
Guoning LI ◽  
Sen LIN
Keyword(s):  
Neural Networks ◽  
Fuzzy Neural Networks ◽  
Railway Vehicle ◽  
Fuzzy Neural

scholarly journals Affine Memory Control for Synchronization of Delayed Fuzzy Neural Networks

IEEE Access ◽  
2020 ◽  
pp. 1-1
Author(s):  
Wookyong Kwon ◽  
Yongsik Jin ◽  
Dongyeop Kang ◽  
Sangmoon Lee
Keyword(s):  
Neural Networks ◽  
Fuzzy Neural Networks ◽  
Fuzzy Neural ◽  
Memory Control
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