The nature of statistical learning theory
The nature of statistical learning theory
Note on free lunches and cross-validation
Neural Computation
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
A pruning method for the recursive least squared algorithm
Neural Networks
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
A tutorial on support vector regression
Statistics and Computing
Forecasting stock market movement direction with support vector machine
Computers and Operations Research
Application of SVM and ANN for intrusion detection
Computers and Operations Research
Real-time prediction of order flowtimes using support vector regression
Computers and Operations Research
The effect of new product development acceleration approaches on development speed: A case study
Journal of Engineering and Technology Management
A distributed PSO-SVM hybrid system with feature selection and parameter optimization
Applied Soft Computing
Colonial Competitive Algorithm as a Tool for Nash Equilibrium Point Achievement
ICCSA '08 Proceedings of the international conference on Computational Science and Its Applications, Part II
Expert Systems with Applications: An International Journal
Time Estimation in Injection Molding Production for Automotive Industry Based on SVR and RBF
IWINAC '09 Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part II: Bioinspired Applications in Artificial and Natural Computation
An overlapping process model to assess schedule risk for new product development
Computers and Industrial Engineering
A growing and pruning method for radial basis function networks
IEEE Transactions on Neural Networks
Solving the integrated product mix-outsourcing problem using the Imperialist Competitive Algorithm
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Optimum design of skeletal structures using imperialist competitive algorithm
Computers and Structures
Using SVM based method for equipment fault detection in a thermal power plant
Computers in Industry
Generalized regression neural networks in time-varying environment
IEEE Transactions on Neural Networks
Advances in Engineering Software
| Hi-index | 0.00 |
Time estimation in new product development (NPD) projects is often a complex problem due to its nonlinearity and the small quantity of data patterns. Support vector regression (SVR) based on statistical learning theory is introduced as a new neural network technique with maximum generalization ability. The SVR has been utilized to solve nonlinear regression problems successfully. However, the applicability of the SVR is highly affected due to the difficulty of selecting the SVR parameters appropriately. The imperialist competitive algorithm (ICA) as a socio-politically inspired optimization strategy is employed to solve the real world engineering problems. This optimization algorithm is inspired by competition mechanism among imperialists and colonies, in contrast to evolutionary algorithms. This paper presents a new model integrating the SVR and the ICA for time estimation in NPD projects, in which ICA is used to tune the parameters of the SVR. A real data set from a case study of an NPD project in a manufacturing industry is presented to demonstrate the performance of the proposed model. In addition, the comparison is provided between the proposed model and conventional techniques, namely nonlinear regression, back-propagation neural networks (BPNN), pure SVR and general regression neural networks (GRNN). The experimental results indicate that the presented model achieves high estimation accuracy and leads to effective prediction.