A locally constrained radial basis function for registration and warping of images
Pattern Recognition Letters
Flood forecasting using radial basis function neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
The Development of Incremental Models
IEEE Transactions on Fuzzy Systems
Least squares quantization in PCM
IEEE Transactions on Information Theory
Conditional fuzzy clustering in the design of radial basis function neural networks
IEEE Transactions on Neural Networks
Reformulated radial basis neural networks trained by gradient descent
IEEE Transactions on Neural Networks
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In this paper, we are concerned with optimization of Context FCM-based RBF neural network realized with the aid of information granulation using clustering techniques that is based on K-Means clustering and Context-based FCM clustering. The objective of this paper is to investigate and compare alternative design models, present an organization of the overall optimization process, and come up with a specification of evaluation mechanisms of the performance of the model. The underlying design tool guiding the development of Context FCM-based RBF neural networks revolves around a certain reconstructability criterion. The design process comprised several main focuses such as: 1) The output space is granulated making use of the K-Means clustering while the input space is clustered with the aid of a context-based fuzzy clustering. 2) The number of information granules produced for each context is adjusted so that we satisfy a certain reconstructability criterion that helps us minimize an error between the original data and the ones resulting from their reconstruction involving prototypes of the clusters and the corresponding membership values. 3) The output neuron of the network exhibits a certain functional nature as its connections are realized as local linear function whose location is determined by the values of the context and the experiments that lead to some design guidelines of the models. Numeric examples involve low dimensional synthetic data and nonlinear process datasets.