Advances in neural information processing systems 2
Model selection in neural networks
Neural Networks
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A self-organising network that grows when required
Neural Networks - New developments in self-organizing maps
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Comparing support vector machines with Gaussian kernels to radialbasis function classifiers
IEEE Transactions on Signal Processing
Codevelopmental Learning Between Human and Humanoid Robot Using a Dynamic Neural-Network Model
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Support vector learning mechanism for fuzzy rule-based modeling: a new approach
IEEE Transactions on Fuzzy Systems
Support-vector-based fuzzy neural network for pattern classification
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
A study on reduced support vector machines
IEEE Transactions on Neural Networks
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In this paper, we propose a novel structure automatic change algorithm for neural-network. It can solve the problem that most neural-networks can not change the structure online. This algorithm consists of two main steps: 1) The computation of the neural-network ability to judge whether need to add nodes to the hidden layer or pruning, we use the improved support vector machine (SVM) to decide when and where to change the structure of neural-network hidden layer in this step; 2) Adjusting the parameter of the neural-network, this learning rule for the neural-network is a novel approach based on the modified back-propagation (BP). On the basis of the former methods, we propose a structure automatic changed neural network (SACNN). Finally, the SACNN is applied to track the nonlinear functions, the simulation results show that the results by this neural network perform better than the former growing cell structure (GCS) neural-network.