Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Constructive higher-order network that is polynomial time
Neural Networks
A feedforward neural network with function shape autotuning
Neural Networks
Data mining
A unifying framework for invariant pattern recognition
Pattern Recognition Letters
Data mining: concepts and techniques
Data mining: concepts and techniques
Data Mining: A Heuristic Approach
Data Mining: A Heuristic Approach
High-order and multilayer perceptron initialization
IEEE Transactions on Neural Networks
Neuron-adaptive higher order neural-network models for automated financial data modeling
IEEE Transactions on Neural Networks
High-order neural network structures for identification of dynamical systems
IEEE Transactions on Neural Networks
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Data Mining is an analytic process designed to explore data (usually large amounts of data - typically business or market related) in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the detected patterns to new subsets of data. One of the most commonly used techniques in data mining, Artificial Neural Networks provide non-linear predictive models that learn through training and resemble biological neural networks in structure. This paper deals with a new adaptive neural network model: a feed-forward higher order neural network with a new activation function called neuron-adaptive activation function. Experiments with function approximation and stock market movement analysis have been conducted to justify the new adaptive neural network model. Experimental results have revealed that the new adaptive neural network model presents several advantages over traditional neuron-fixed feed-forward networks such as much reduced network size, faster learning, and more promising financial analysis.