Automatic extraction and identification of chart patterns towards financial forecast
Applied Soft Computing
Online Dynamic Value System for Machine Learning
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Regression application based on fuzzy ν-support vector machine in symmetric triangular fuzzy space
Expert Systems with Applications: An International Journal
Intelligent fabric hand prediction system with fuzzy neural network
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Improving the intelligent prediction model for macro-economy
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
An Evolutionary Functional Link Neural Fuzzy Model for Financial Time Series Forecasting
International Journal of Applied Evolutionary Computation
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In this paper, an evolutionary fuzzy neural network using fuzzy logic, neural networks (NNs), and genetic algorithms (GAs) is proposed for financial prediction with hybrid input data sets from different financial domains. A new hybrid iterative evolutionary learning algorithm initializes all parameters and weights in the five-layer fuzzy NN, then uses GA to optimize these parameters, and finally applies the gradient descent learning algorithm to continue the optimization of the parameters. Importantly, GA and the gradient descent learning algorithm are used alternatively in an iterative manner to adjust the parameters until the error is less than the required value. Unlike traditional methods, we not only consider the data of the prediction factor, but also consider the hybrid factors related to the prediction factor. Bank prime loan rate, federal funds rate and discount rate are used as hybrid factors to predict future financial values. The simulation results indicate that hybrid iterative evolutionary learning combining both GA and the gradient descent learning algorithm is more powerful than the previous separate sequential training algorithm described in.