Artificial Intelligence in Construction Management : Evolutionary Fuzzy Hybrid Neural Network as decision-making machine

Erick Sudjono

ISBN 10: 3639082478 / ISBN 13: 9783639082470

Published by VDM Verlag Dr. Müller, VDM Verlag Dr. Müller E.K., 2008

About this Item

Neuware - Many studies have found that High-order neuralnetwork (HONN) is enables to boost neural networkperformance. This research utilize a hybrid modelwith HONN and Linear Neural Network (NN) concepts todevelop high-order and linear neural connectors forlayer connections. Consequently, this developed HNNwill involve a linear/nonlinear switch for eachneural layer connection. Furthermore, fuzzy logic(FL) has already been introduced to neural networkand it is also found that the combination of FL andFNN has been proof-reading. Furthermore, fuzzy logic(FL) also has been introduced to neural network andbeen proofed with fuzzy neural network (FNN).Therefore, this research fuses fuzzy logicadditionally to develop a fuzzy hybrid neuralnetwork (FHNN) architecture. Sequentially, geneticalgorithm (GA) is employed to globally optimizemembership function of FL and HNN topology andparameters.

Seller Inventory # 9783639082470

Bibliographic Details

• Title: Artificial Intelligence in Construction Management : Evolutionary Fuzzy Hybrid Neural Network as decision-making machine
• Publisher: VDM Verlag Dr. Müller, VDM Verlag Dr. Müller E.K.
• Publication Date: 2008
• Language: English
• ISBN 10: 3639082478
• ISBN 13: 9783639082470
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 9D millimeters 220 millimeters width by 150 millimeters height by 9 millimeters depth
• Weight: 238 grams

About this title

Synopsis

Many studies have found that High-order neural network (HONN) is enables to boost neural network performance. This research utilize a hybrid model with HONN and Linear Neural Network (NN) concepts to develop high-order and linear neural connectors for layer connections. Consequently, this developed HNN will involve a linear/nonlinear switch for each neural layer connection. Furthermore, fuzzy logic (FL) has already been introduced to neural network and it is also found that the combination of FL and FNN has been proof-reading. Furthermore, fuzzy logic (FL) also has been introduced to neural network and been proofed with fuzzy neural network (FNN). Therefore, this research fuses fuzzy logic additionally to develop a fuzzy hybrid neural network (FHNN) architecture. Sequentially, genetic algorithm (GA) is employed to globally optimize membership function of FL and HNN topology and parameters.

About the Author

MSc.Eng.,Construction Engineering, NTUST, Taiwan, 2006/08S.T.,Civil Engineering, ITS, Indonesia, 2001/05Professional Experience2008, J Steel singapore2006, PT.Acset Indonusa