Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
About the use of fuzzy clustering techniques for fuzzy model identification
Fuzzy Sets and Systems
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Design of structural modular neural networks with genetic algorithm
Advances in Engineering Software
Adaptive Neuro-Fuzzy Networks with the Aid of Fuzzy Granulation
IEICE - Transactions on Information and Systems
Fast learning in networks of locally-tuned processing units
Neural Computation
An stable online clustering fuzzy neural network for nonlinear system identification
Neural Computing and Applications
On the use of the weighted fuzzy c-means in fuzzy modeling
Advances in Engineering Software
A locally recurrent fuzzy neural network with support vector regression for dynamic-system modeling
IEEE Transactions on Fuzzy Systems
Integration of supervised ART-based neural networks with a hybrid genetic algorithm
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Linguistic models as a framework of user-centric system modeling
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A fuzzy clustering-based rapid prototyping for fuzzy rule-based modeling
IEEE Transactions on Fuzzy Systems
A new approach to fuzzy modeling
IEEE Transactions on Fuzzy Systems
Development of a systematic methodology of fuzzy logic modeling
IEEE Transactions on Fuzzy Systems
Generalized weighted conditional fuzzy clustering
IEEE Transactions on Fuzzy Systems
A fuzzy-logic-based approach to qualitative modeling
IEEE Transactions on Fuzzy Systems
Conditional fuzzy clustering in the design of radial basis function neural networks
IEEE Transactions on Neural Networks
A new clustering technique for function approximation
IEEE Transactions on Neural Networks
RBF neural network center selection based on Fisher ratio class separability measure
IEEE Transactions on Neural Networks
On global-local artificial neural networks for function approximation
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Conventional modeling of the multilayer perceptron using polynomial basis functions
IEEE Transactions on Neural Networks
Evolving space-filling curves to distribute radial basis functions over an input space
IEEE Transactions on Neural Networks
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In this paper, we propose an Output-Constricted Clustering (OCC) algorithm for Radial Basis Function Neural Network (RBFNN) initialization. OCC first roughly partitions the output based on the required precision and then refinedly clusters data based on the input complexity within each output partition. The main contribution of the proposed clustering algorithm is that we introduce the concept of separability, which is a criterion to judge the suitability of the number of sub-clusters in each output partition. As a result, OCC is able to determine the proper number of sub-clusters with appropriate locations within each output partition by considering both input and output information. The resulting clusters from OCC are used to initialize RBFNN, with proper number and initial locations of for hidden neurons. As a result, RBFNN starting it's learning from a good point, is able to achieve better approximation performance than existing clustering methods for RBFNN initialization. This better performance is illustrated by a number of examples.