Introduction to the theory of neural computation
Introduction to the theory of neural computation
1994 Special Issue: Design and evolution of modular neural network architectures
Neural Networks - Special issue: models of neurodynamics and behavior
Neural networks in applied statistics
Technometrics
Neural Processing Letters
A Minimax Method for Learning Functional Networks
Neural Processing Letters
Functional Networks with Applications: A Neural-Based Paradigm
Functional Networks with Applications: A Neural-Based Paradigm
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Functional networks combine both domain and data knowledge to develop optimal network architectures for some types of interesting problems. The topology of the network is obtained from qualitative domain knowledge, and data is used to fit the processing functions appearing in the network; these functions are supposed to be linear combinations of known functions from appropriate families. In this paper we showthat these functions can also be estimated using feedforward neural nets, making no assumption about the families of functions involved in the problem. The resulting models are optimal modular network architectures for the corresponding problems. Several examples from nonlinear time series prediction are used to illustrate the performance of these models when compared with standard functional and neural networks.