Neural networks in applied statistics
Technometrics
A Minimax Method for Learning Functional Networks
Neural Processing Letters
Extending Neural Networks for B-Spline Surface Reconstruction
ICCS '02 Proceedings of the International Conference on Computational Science-Part II
A Functional-Neural Network for Post-Nonlinear Independent Component Analysis
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Optimal Modular Feedforward Neural Nets Based on Functional Network Architectures
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
A New Artificial Intelligence Paradigm for Computer-Aided Geometric Design
AISC '00 Revised Papers from the International Conference on Artificial Intelligence and Symbolic Computation
A Measure of Noise Immunity for Functional Networks
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Functional networks for B-spline surface reconstruction
Future Generation Computer Systems - Special issue: Computer graphics and geometric modeling
Recovering Missing Data with Functional and Bayesian Networks
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Functional networks for B-spline surface reconstruction
Future Generation Computer Systems
Hybrid computational models for the characterization of oil and gas reservoirs
Expert Systems with Applications: An International Journal
Interpolation mechanism of functional networks
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Modular learning schemes for visual robot control
Biomimetic Neural Learning for Intelligent Robots
Functional networks and the lagrange polynomial interpolation
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Displacement prediction model of landslide based on functional networks
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Navigation Satellite Clock Error Prediction Based on Functional Network
Neural Processing Letters
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In this letter we present functional networks. Unlike neural networks, in these networks there are no weightsassociated with the links connecting neurons, and the internal neuron functions are not fixed but learnable.These functions are not arbitrary, but subject to strong constraints to satisfy the compatibility conditions imposed by the existence of multiplelinks going from the last input layer to the same output units. In fact,writing the values of the output units in different forms, by consideringthese different links, a system of functional equations is obtained. When this system is solved, the numberof degrees of freedom of these initiallymultidimensional functions is considerably reduced. One example illustratesthe process and shows that multidimensional functions can be reduced tofunctions with a single argument. To learn the resulting functions, amethod based on minimizing a least squares error function is used, which,unlike the functions used in neural networks, has a single minimum.