Neural computing: an introduction
Neural computing: an introduction
Forecasting exchange rates using general regression neural networks
Computers and Operations Research - Neural networks in business
An Introduction to Neural Networks
An Introduction to Neural Networks
A comparative study of neural network and Box-Jenkins ARIMA modeling in time series prediction
Computers and Industrial Engineering - 26th International conference on computers and industrial engineering
Modified high-order neural network for invariant pattern recognition
Pattern Recognition Letters
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
A learning algorithm for continually running fully recurrent neural networks
Neural Computation
Time Series Forecasting of Averaged Data With Efficient Use of Information
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Support vector machine with adaptive parameters in financial time series forecasting
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
The wavelet multilayer perceptron for the prediction of earthquake time series data
Proceedings of the 13th International Conference on Information Integration and Web-based Applications and Services
G-HABC Algorithm for Training Artificial Neural Networks
International Journal of Applied Metaheuristic Computing
Global artificial bee colony algorithm for boolean function classification
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
Global Artificial Bee Colony-Levenberq-Marquardt GABC-LM Algorithm for Classification
International Journal of Applied Evolutionary Computation
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This research focuses on using various higher order neural networks (HONNs) to predict the upcoming trends of financial signals. Two HONNs models: the Pi-Sigma neural network and the ridge polynomial neural network were used. Furthermore, a novel HONN architecture which combines the properties of both higher order and recurrent neural network was constructed, and is called dynamic ridge polynomial neural network (DRPNN). Extensive simulations for the prediction of one and five steps ahead of financial signals were performed. Simulation results indicate that DRPNN in most cases demonstrated advantages in capturing chaotic movement in the signals with an improvement in the profit return and rapid convergence over other network models.