System identification: theory for the user
System identification: theory for the user
Industrial applications of model based predictive control
Automatica (Journal of IFAC) - IFAC-IEEE special issue on meeting the challenge of computer science in the industrial applications of control
Identification of non-linear system structure and parameters using regime decomposition
Automatica (Journal of IFAC)
Regression with Gaussian processes
MANNA '95 Proceedings of the first international conference on Mathematics of neural networks : models, algorithms and applications: models, algorithms and applications
Bayesian Classification With Gaussian Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Monte Carlo methods
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
Comparison of approximate methods for handling hyperparameters
Neural Computation
Discovering hidden features with Gaussian processes regression
Proceedings of the 1998 conference on Advances in neural information processing systems II
Evaluation of gaussian processes and other methods for non-linear regression
Evaluation of gaussian processes and other methods for non-linear regression
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Gaussian process modelling as an indicator of neonatal seizure
SPPRA'06 Proceedings of the 24th IASTED international conference on Signal processing, pattern recognition, and applications
On the interpretation and identification of dynamic Takagi-Sugeno fuzzy models
IEEE Transactions on Fuzzy Systems
Extending the functional equivalence of radial basis function networks and fuzzy inference systems
IEEE Transactions on Neural Networks
Nonlinear control structures based on embedded neural system models
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Variational Gaussian process classifiers
IEEE Transactions on Neural Networks
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
Orthogonal least squares learning algorithm for radial basis function networks
IEEE Transactions on Neural Networks
Local Model Network Identification With Gaussian Processes
IEEE Transactions on Neural Networks
Functional equivalence between radial basis function networks and fuzzy inference systems
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Gaussian process approach for modelling of nonlinear systems
Engineering Applications of Artificial Intelligence
A radial basis function redesigned for predicting a welding process
MICAI'10 Proceedings of the 9th Mexican international conference on Artificial intelligence conference on Advances in soft computing: Part II
Analysis and evaluation in a welding process applying a Redesigned Radial Basis Function
Expert Systems with Applications: An International Journal
Local model network identification for online engine modelling
Information Sciences: an International Journal
Statistical inference in a redesigned Radial Basis Function neural network
Engineering Applications of Artificial Intelligence
| Hi-index | 0.00 |
Neural networks have been widely used to model nonlinear systems for control. The curse of dimensionality and lack of transparency of such neural network models has forced a shift towards local model networks and recently towards the nonparametric Gaussian processes approach. Assuming common validity functions, all of these models have a similar structure. This paper examines the evolution from the radial basis function network to the local model network and finally to the Gaussian process model. A simulated example is used to explain the advantages and disadvantages of each structure.