Structure optimization of fuzzy neural network by genetic algorithm
Fuzzy Sets and Systems - Special issue on fuzzy neural control
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
The nature of statistical learning theory
The nature of statistical learning theory
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
A Simple Decomposition Method for Support Vector Machines
Machine Learning
RapidAccurate Optimization of Difficult Problems Using Fast Messy Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
A genetic-fuzzy-neuro model encodes FNNs using SWRM and BRM
Engineering Applications of Artificial Intelligence
Fuzzy Logic for Business, Finance, and Management
Fuzzy Logic for Business, Finance, and Management
Experience management: foundations, development methodology, and internet-based applications
Experience management: foundations, development methodology, and internet-based applications
A new fuzzy support vector machine to evaluate credit risk
IEEE Transactions on Fuzzy Systems
Advantages of an alternative form of fuzzy logic
IEEE Transactions on Fuzzy Systems
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Construction projects are, by their very nature, challenging; and project decision makers must work successfully within an environment that is frequently complex and fraught with uncertainty. As many decisions must be made intuitively based on limited information, successful decision making depends heavily on two factors, including the experience of the expert(s) involved and the quality of knowledge accumulated from previous experience. Knowledge, however, is subject to various factors that cause its value and accuracy to deteriorate. Research has demonstrated that artificial intelligence has the potential to overcome these factors. The Evolutionary Fuzzy Support Vector Machine Inference Model (EFSIM), an artificial intelligence hybrid system that fuses together fuzzy logic (FL), a support vector machine (SVM) and fast messy genetic algorithm (fmGA), represents an alternative approach to retaining and utilizing experiential knowledge. A fmGA is used as an optimization tool to search simultaneously for fittest membership functions, defuzzification parameter (dfp) and SVM hyperparameter (herein C and gamma, @c). Two simulations on actual construction management problems demonstrated the EFSIM to be an effective tool for solving various problems in the construction industry.