A New Adaptive Neural Network Model for Financial Data Mining
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
A new approach for epileptic seizure detection using adaptive neural network
Expert Systems with Applications: An International Journal
ANSER: adaptive neuron artificial neural network system for estimating rainfall
International Journal of Computers and Applications
A new neural network with adaptive activation function for classification of ECG arrhythmias
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
A Hybrid Higher Order Neural Classifier for handling classification problems
Expert Systems with Applications: An International Journal
Data mining using an adaptive HONN model with hyperbolic tangent neurons
PKAW'10 Proceedings of the 11th international conference on Knowledge management and acquisition for smart systems and services
Ultra high frequency sine and sine higher order neural networks
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
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Real-world financial data is often nonlinear, comprises high-frequency multipolynomial components, and is discontinuous (piecewise continuous). Not surprisingly, it is hard to model such data. Classical neural networks are unable to automatically determine the optimum model and appropriate order for financial data approximation. We address this problem by developing neuron-adaptive higher order neural-network (NAHONN) models. After introducing one-dimensional (1-D), two-dimensional (2-D), and n-dimensional NAHONN models, we present an appropriate learning algorithm. Network convergence and the universal approximation capability of NAHONNs are also established. NAHONN Group models (NAHONGs) are also introduced. Both NAHONNs and NAHONGs are shown to be "open box" and as such are more acceptable to financial experts than classical (closed box) neural networks. These models are further shown to be capable of automatically finding not only the optimum model, but also the appropriate order for specific financial data