Multi-Objective Design of Neuro-Fuzzy Controllers for Robot Behavior Coordination

2006 ◽  
pp. 557-584
Author(s):  
Naoyuki Kubota
Keyword(s):  
Fuzzy Controllers ◽  
Multi Objective ◽  
Neuro Fuzzy ◽  
Robot Behavior

A Neuro-Fuzzy Partner Selection System for Business Social Networks

Data Mining ◽  
2013 ◽  
pp. 231-250
Author(s):  
T. T. Wong ◽  
Loretta K.W. Sze
Keyword(s):  
Social Networks ◽  
Decision Support ◽  
Social Network Sites ◽  
Partner Selection ◽  
Mining System ◽  
Multi Objective ◽  
Business Partners ◽  
Neuro Fuzzy ◽  
Selection Decision ◽  
Data Mining System

Enterprises are now facing growing global competition and the continual success in the marketplace depends very much on how efficient and effective companies are able to respond to customer demands. Business social network sites (BSNS) have provided a powerful tool to link up manufacturers, suppliers, distributors, and customers. Among the emerging business social networks, decision support functionality addressing the issue of selecting business partners is an important domain to be studied, and it is the objective of this chapter to propose a practical partner selection decision support system. Essentially, a neural-network data mining system is used to generate information for subsequent fuzzy multi-objective analysis. It demonstrates the benefits of integrating information technology, artificial intelligence, and multi-objective decision making to form a practical aid that capitalizes on the merits of BSNS. A special feature is that the trust among companies can be incorporated as an evaluation criterion.

A Neuro-Fuzzy Partner Selection System for Business Social Networks

2012 ◽  
pp. 98-118
Author(s):  
T. T. Wong ◽  
Loretta K.W. Sze
Keyword(s):  
Social Networks ◽  
Decision Support ◽  
Social Network Sites ◽  
Partner Selection ◽  
Mining System ◽  
Multi Objective ◽  
Business Partners ◽  
Neuro Fuzzy ◽  
Selection Decision ◽  
Data Mining System

Enterprises are now facing growing global competition and the continual success in the marketplace depends very much on how efficient and effective companies are able to respond to customer demands. Business social network sites (BSNS) have provided a powerful tool to link up manufacturers, suppliers, distributors, and customers. Among the emerging business social networks, decision support functionality addressing the issue of selecting business partners is an important domain to be studied, and it is the objective of this chapter to propose a practical partner selection decision support system. Essentially, a neural-network data mining system is used to generate information for subsequent fuzzy multi-objective analysis. It demonstrates the benefits of integrating information technology, artificial intelligence, and multi-objective decision making to form a practical aid that capitalizes on the merits of BSNS. A special feature is that the trust among companies can be incorporated as an evaluation criterion.

scholarly journals Optimization of EPB Shield Performance with Adaptive Neuro-Fuzzy Inference System and Genetic Algorithm

2019 ◽  
Vol 9 (4) ◽  
pp. 780 ◽  
Author(s):  
Khalid Elbaz ◽  
Shui-Long Shen ◽  
Annan Zhou ◽  
Da-Jun Yuan ◽  
Ye-Shuang Xu
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Pressure Balance ◽  
Anfis Model ◽  
Inference System ◽  
Advance Rate ◽  
Multi Objective ◽  
Neuro Fuzzy ◽  
Epb Shield

The prediction of earth pressure balance (EPB) shield performance is an essential part of project scheduling and cost estimation of tunneling projects. This paper establishes an efficient multi-objective optimization model to predict the shield performance during the tunneling process. This model integrates the adaptive neuro-fuzzy inference system (ANFIS) with the genetic algorithm (GA). The hybrid model uses shield operational parameters as inputs and computes the advance rate as output. GA enhances the accuracy of ANFIS for runtime parameters tuning by multi-objective fitness function. Prior to modeling, datasets were established, and critical operating parameters were identified through principal component analysis. Then, the tunneling case for Guangzhou metro line number 9 was adopted to verify the applicability of the proposed model. Results were then compared with those of the ANFIS model. The comparison showed that the multi-objective ANFIS-GA model is more successful than the ANFIS model in predicting the advance rate with a high accuracy, which can be used to guide the tunnel performance in the field.

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