Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Soft Computing and Fractal Theory for Intelligent Manufacturing
Soft Computing and Fractal Theory for Intelligent Manufacturing
Divide-and-conquer learning and modular perceptron networks
IEEE Transactions on Neural Networks
IFSA '07 Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing
Information Sciences: an International Journal
Interval type-2 fuzzy logic and modular neural networks for face recognition applications
Applied Soft Computing
Using a non-uniform self-selective coder for option pricing
Applied Soft Computing
Level-dependent Sugeno integral
IEEE Transactions on Fuzzy Systems
Accurate segmentation of dermoscopic images by image thresholding based on type-2 fuzzy logic
IEEE Transactions on Fuzzy Systems
An inequality related to Minkowski type for Sugeno integrals
Information Sciences: an International Journal
The modified self-organizing fuzzy neural network model for adaptability evaluation
LSMS'07 Proceedings of the 2007 international conference on Life System Modeling and Simulation
Construction of a new BRB based model for time series forecasting
Applied Soft Computing
Improving project-profit prediction using a two-stage forecasting system
Computers and Industrial Engineering
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We describe in this paper the application of a modular neural network architecture to the problem of simulating and predicting the dynamic behavior of complex economic time series. We use several neural network models and training algorithms to compare the results and decide at the end, which one is best for this application. We also compare the simulation results with the traditional approach of using a statistical model. In this case, we use real time series of prices of consumer goods to test our models. Real prices of tomato in the U.S. show complex fluctuations in time and are very complicated to predict with traditional statistical approaches. For this reason, we have chosen a neural network approach to simulate and predict the evolution of these prices in the U.S. market.