X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
IEEE Transactions on Intelligent Transportation Systems
A self-organizing fuzzy neural network based on a growing-and-pruning algorithm
IEEE Transactions on Fuzzy Systems
A bayesian network approach to traffic flow forecasting
IEEE Transactions on Intelligent Transportation Systems
Dynamic fuzzy neural networks-a novel approach to functionapproximation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An online self-constructing neural fuzzy inference network and its applications
IEEE Transactions on Fuzzy Systems
A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks
IEEE Transactions on Fuzzy Systems
A Probabilistic Neural-Fuzzy Learning System for Stochastic Modeling
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Neural Networks
Centroid neural network for unsupervised competitive learning
IEEE Transactions on Neural Networks
A new pruning heuristic based on variance analysis of sensitivity information
IEEE Transactions on Neural Networks
A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation
IEEE Transactions on Neural Networks
A node pruning algorithm based on a Fourier amplitude sensitivity test method
IEEE Transactions on Neural Networks
A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks
IEEE Transactions on Neural Networks
Self-association and Hebbian learning in linear neural networks
IEEE Transactions on Neural Networks
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As an effective method that can provide the information about the influence of inputs on the variation of output, variance based sensitivity analysis is widely used to determine the structure of neural networks. In the past, the global sensitivity analysis method for the total effect has been used for the structure learning of neural networks and various growing and pruning algorithms have been developed. In this paper, we find that neuro-fuzzy networks have the characteristics of additive models in which the first order effect index of the influence can provide the same comprehensive information as the total effect index, thus we only need to analyze the first order effects of the inputs to their output layers. Based on this observation, many low-cost effective methods for the first order effect global sensitivity can be used in for developing self-organizing neuro-fuzzy networks. Specifically, Random Balance Designs is employed here for sensitivity analysis. In addition, we also introduce the concept of systemic fluctuation of neuro-fuzzy networks to determine whether adjustment is needed for a network. This concept helps us to build a new procedure about the leaning of self-organizing neuro-fuzzy networks and to accelerate its speed of convergence in learning and organizing. Examples of simulations have demonstrated that our proposed method performs better than other existing procedures for self-organizing neuro-fuzzy networks, especially in learning of the network structure.