Evolutionary fuzzy hybrid neural network for project cash flow control

Authors:
Min-Yuan Cheng;Hsing-Chih Tsai;Erick Sudjono
Affiliations:
Department of Construction Engineering, National Taiwan University of Science and Technology, #43, Sec. 4, Keelung Rd., Ecological and Hazard Mitigation Engineering Researching Center, Taipei 106, ...;Department of Construction Engineering, National Taiwan University of Science and Technology, #43, Sec. 4, Keelung Rd., Ecological and Hazard Mitigation Engineering Researching Center, Taipei 106, ...;Department of Construction Engineering, National Taiwan University of Science and Technology, #43, Sec. 4, Keelung Rd., Ecological and Hazard Mitigation Engineering Researching Center, Taipei 106, ...
Venue:
Engineering Applications of Artificial Intelligence
Year:
2010

Citing 2
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Abstract

This paper develops an evolutionary fuzzy hybrid neural network (EFHNN) to enhance project cash flow management. The developed EFHNN combines neural networks (NN) and high order neural networks (HONN) into a hybrid neural network (HNN), which acts as the major inference engine and operates with alternating linear and nonlinear NN layer connections. Fuzzy logic is employed to sandwich the HNN between a fuzzification and defuzzification layer. The authors developed and applied the EFHNN to sequential cash flow trend problems by fusing HNN, FL, and GA. Results show that the proposed EFHNN can be deployed effectively to sequential cash flow estimation. The performance of linear and nonlinear (high order) neuron layer connectors in the EFHNN was significantly better than the performance achieved by previous models that used singular linear NN. Trained results were used for the prediction and strategic management of project cash flow. The proposed strategy can assist project managers to control project cash flows within the banana envelope of the S-curve to enhance project success.