Incremental personalized web page mining utilizing self-organizing HCMAC neural network
Web Intelligence and Agent Systems
Minimal Structure of Self-Organizing HCMAC Neural Network Classifier
Neural Processing Letters
A nature inspired Ying-Yang approach for intelligent decision support in bank solvency analysis
Expert Systems with Applications: An International Journal
A new pseudo-Gaussian-based recurrent fuzzy CMAC model for dynamic systems processing
International Journal of Systems Science
Two novel feature selection approaches for web page classification
Expert Systems with Applications: An International Journal
Adaptive Growing Quantization for 1D CMAC Network
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Self-organizing CMAC control for a class of MIMO uncertain nonlinear systems
IEEE Transactions on Neural Networks
Kernel CMAC with Reduced Memory Complexity
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
A linguistic CMAC equivalent to a linguistic decision tree for classification
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
CMAC neural networks structures
CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
A Hybrid Higher Order Neural Classifier for handling classification problems
Expert Systems with Applications: An International Journal
System identification using hierarchical fuzzy CMAC neural networks
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
Frontiers of Computer Science in China
ART-type CMAC network classifier
Neurocomputing
A functional neural fuzzy network for classification applications
Expert Systems with Applications: An International Journal
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
Grey adaptive growing CMAC network
Applied Soft Computing
Eigenanalysis of CMAC neural network
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Hybrid neural network model based on multi-layer perceptron and adaptive resonance theory
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Neuron selection for RBF neural network classifier based on multiple granularities immune network
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Two-Dimensional adaptive growing CMAC network
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - 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
KCMAC-BYY: Kernel CMAC using Bayesian Ying-Yang learning
Neurocomputing
Gait Pattern Based on CMAC Neural Network for Robotic Applications
Neural Processing Letters
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This paper presents a self-organizing hierarchical cerebellar model arithmetic computer (HCMAC) neural-network classifier, which contains a self-organizing input space module and an HCMAC neural network. The conventional CMAC can be viewed as a basis function network (BFN) with supervised learning, and performs well in terms of its fast learning speed and local generalization capability for approximating nonlinear functions. However, the conventional CMAC has an enormous memory requirement for resolving high-dimensional classification problems, and its performance heavily depends on the approach of input space quantization. To solve these problems, this paper presents a novel supervised HCMAC neural network capable of resolving high-dimensional classification problems well. Also, in order to reduce what is often trial-and-error parameter searching for constructing memory allocation automatically, proposed herein is a self-organizing input space module that uses Shannon's entropy measure and the golden-section search method to appropriately determine the input space quantization according to the various distributions of training data sets. Experimental results indicate that the self-organizing HCMAC indeed has a fast learning ability and low memory requirement. It is a better performing network than the conventional CMAC for resolving high-dimensional classification problems. Furthermore, the self-organizing HCMAC classifier has a better classification ability than other compared classifiers.