RBF Networks Exploiting Supervised Data in the Adaptation of Hidden Neuron Parameters
AI*IA 01 Proceedings of the 7th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
Mathematics and Computers in Simulation
RBF-based neurodynamic nearest neighbor classification in real pattern space
Pattern Recognition
Output value-based initialization for radial basis function neural networks
Neural Processing Letters
Generalized multiscale radial basis function networks
Neural Networks
Increasing classification efficiency with multiple mirror classifiers
Expert Systems with Applications: An International Journal
Modelling of Dynamic Systems Using Generalized RBF Neural Networks Based on Kalman Filter Mehtod
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Hybrid Wavelet-RBFNN Model for Monthly Anchovy Catches Forecasting
AIMSA '08 Proceedings of the 13th international conference on Artificial Intelligence: Methodology, Systems, and Applications
Solving a system of nonlinear integral equations by an RBF network
Computers & Mathematics with Applications
An Enhanced Swarm Intelligence Clustering-Based RBF Neural Network Web Text Classifier
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
An Empirical Comparison of Training Algorithms for Radial Basis Functions
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
IEEE Transactions on Circuits and Systems Part I: Regular Papers
A novel reformulated radial basis function neural network
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Random Projection RBF Nets for Multidimensional Density Estimation
International Journal of Applied Mathematics and Computer Science - Issues in Fault Diagnosis and Fault Tolerant Control
Functional modelling of large scattered data sets using neural networks
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Stacking MF networks to combine the outputs provided by RBF networks
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
A very fast neural learning for classification using only new incoming datum
IEEE Transactions on Neural Networks
Sparse RBF Networks with Multi-kernels
Neural Processing Letters
Frontiers of Computer Science in China
Comparing linear and non-linear transformation of speech
SSIP '09/MIV'09 Proceedings of the 9th WSEAS international conference on signal, speech and image processing, and 9th WSEAS international conference on Multimedia, internet & video technologies
Construction cosine radial basic function neural networks based on artificial immune networks
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
A complete sequential learning algorithm for RBF neural networks with applications
MMACTEE'06 Proceedings of the 8th WSEAS international conference on Mathematical methods and computational techniques in electrical engineering
Prediction of magnetic field near power lines by normalized radial basis function network
Advances in Engineering Software
A new definition of sensitivity for RBFNN and its applications to feature reduction
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Adaptive point-cloud surface interpretation
CGI'06 Proceedings of the 24th international conference on Advances in Computer Graphics
Gradient descent and radial basis functions
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
A comparison of gaussian based ANNs for the classification of multidimensional hyperspectral signals
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Performance evaluation of recurrent RBF network in nearest neighbor classification
TAINN'05 Proceedings of the 14th Turkish conference on Artificial Intelligence and Neural Networks
An experimental study on training radial basis functions by gradient descent
ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
Training RBFs networks: a comparison among supervised and not supervised algorithms
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
An adaptive classifier based on artificial immune network
LSMS'07 Proceedings of the 2007 international conference on Life System Modeling and Simulation
Design of context-FCM based RBF neural networks with the aid of data information granulation
Proceedings of the 2012 ACM Research in Applied Computation Symposium
Radial basis function neural networks applied to efficient QRST cancellation in atrial fibrillation
Computers in Biology and Medicine
3D face and motion estimation from sparse points using adaptive bracketed minimization
Multimedia Tools and Applications
Hi-index | 0.00 |
This paper presents an axiomatic approach for constructing radial basis function (RBF) neural networks. This approach results in a broad variety of admissible RBF models, including those employing Gaussian RBFs. The form of the RBFs is determined by a generator function. New RBF models can be developed according to the proposed approach by selecting generator functions other than exponential ones, which lead to Gaussian RBFs. This paper also proposes a supervised learning algorithm based on gradient descent for training reformulated RBF neural networks constructed using the proposed approach. A sensitivity analysis of the proposed algorithm relates the properties of RBFs with the convergence of gradient descent learning. Experiments involving a variety of reformulated RBF networks generated by linear and exponential generator functions indicate that gradient descent learning is simple, easily implementable, and produces RBF networks that perform considerably better than conventional RBF models trained by existing algorithms