International Journal of Approximate Reasoning
Generalization by weight-elimination with application to forecasting
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Neural-Network-Based Fuzzy Logic Control and Decision System
IEEE Transactions on Computers - Special issue on artificial neural networks
Fuzzy logic for the management of uncertainty
The Max-Min Delphi method and fuzzy Delphi method via fuzzy integration
Fuzzy Sets and Systems
Fuzzy neural networks: a survey
Fuzzy Sets and Systems
A learning algorithm of fuzzy neural networks with triangular fuzzy weights
Fuzzy Sets and Systems - Special issue on fuzzy neural control
A neural fuzzy control system with structure and parameter learning
Fuzzy Sets and Systems - Special issue on modern fuzzy control
Multi-sensor integration for intelligent control of machining through artificial neural networks and fuzzy modelling
Data mining with neural networks: solving business problems from application development to decision support
Sales forecasting using time series and neural networks
CIE '96 Proceedings of the 19th international conference on Computers and industrial engineering
Manufacturing process control through integration of neural networks and fuzzy model
Fuzzy Sets and Systems
Fuzzy neural networks with application to sales forecasting
Fuzzy Sets and Systems
A neural fuzzy system with linguistic teaching signals
IEEE Transactions on Fuzzy Systems
An extended evaluation framework for neural network publications in sales forecasting
AIA'06 Proceedings of the 24th IASTED international conference on Artificial intelligence and applications
Part family formation through fuzzy ART2 neural network
Decision Support Systems
Hybrid neural systems for large scale credit risk assessment applications
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - VIII Brazilian Symposium on Neural Networks
Expert Systems with Applications: An International Journal
An improvement on genetic-based learning method for fuzzy artificial neural networks
Applied Soft Computing
Evolving fuzzy case-based reasoning in wholesaler's returning book forecasting
Proceedings of the 2009 International Conference on Hybrid Information Technology
Expert Systems with Applications: An International Journal
Review article: A review of soft computing applications in supply chain management
Applied Soft Computing
Computers and Industrial Engineering
Using the Taguchi method for effective market segmentation
Expert Systems with Applications: An International Journal
Evolving case-based reasoning with genetic algorithm in wholesaler's returning book forecasting
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV
Expert Systems with Applications: An International Journal
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Sales forecasting plays a very prominent role in business strategy. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average (ARMA). However, sales forecasting is very complicated owing to influence by internal and external environments. Recently, artificial neural networks (ANNs) have also been applied in sales forecasting since their promising performances in the areas of control and pattern recognition. However, further improvement is still necessary since unique circumstances, e.g. promotion, cause a sudden change in the sales pattern. Thus, this study utilizes a proposed fuzzy neural network (FNN), which is able to eliminate the unimportant weights, for the sake of learning fuzzy IF-THEN rules obtained from the marketing experts with respect to promotion. The result from FNN is further integrated with the time series data through an ANN. Both the simulated and real-world problem results show that FNN with weight elimination can have lower training error compared with the regular FNN. Besides, real-world problem results also indicate that the proposed estimation system outperforms the conventional statistical method and single ANN in accuracy.