A study of granular computing in the agenda of growth of artificial neural networks
M Song, Y Wang - Granular Computing, 2016 - Springer
Granular neural networks (GNN) are designed to process complex non-numerical data or
the combination of numerical and non-numerical data. The concept of “granules” here refers …
the combination of numerical and non-numerical data. The concept of “granules” here refers …
A decision-theoretic rough set approach for dynamic data mining
Uncertainty and fuzziness generally exist in real-life data. Approximations are employed to
describe the uncertain information approximately in rough set theory. Certain and uncertain …
describe the uncertain information approximately in rough set theory. Certain and uncertain …
Hybrid fuzzy wavelet neural networks architecture based on polynomial neural networks and fuzzy set/relation inference-based wavelet neurons
This paper presents a hybrid fuzzy wavelet neural network (HFWNN) realized with the aid of
polynomial neural networks (PNNs) and fuzzy inference-based wavelet neurons (FIWNs) …
polynomial neural networks (PNNs) and fuzzy inference-based wavelet neurons (FIWNs) …
On training RBF neural networks using input–output fuzzy clustering and particle swarm optimization
GE Tsekouras, J Tsimikas - Fuzzy Sets and Systems, 2013 - Elsevier
This paper elaborates on the use of particle swarm optimization in training Gaussian type
radial basis function neural networks under the umbrella of input–output fuzzy clustering …
radial basis function neural networks under the umbrella of input–output fuzzy clustering …
Maintenance of approximations in incomplete ordered decision systems while attribute values coarsening or refining
H Chen, T Li, D Ruan - Knowledge-Based Systems, 2012 - Elsevier
Approximations in rough sets theory are important operators to discover interesting patterns
and dependencies in data mining. Both certain and uncertain rules are unraveled from …
and dependencies in data mining. Both certain and uncertain rules are unraveled from …
Design of iterative fuzzy radial basis function neural networks based on iterative weighted fuzzy c-means clustering and weighted LSE estimation
In this article, a reinforced iterative fuzzy radial basis function neural networks (IFRBFNN) is
introduced as an augmented FRBFNN architecture generated through the iterative …
introduced as an augmented FRBFNN architecture generated through the iterative …
Design of K-means clustering-based polynomial radial basis function neural networks (pRBF NNs) realized with the aid of particle swarm optimization and differential …
In this paper, we introduce an advanced architecture of K-means clustering-based
polynomial Radial Basis Function Neural Networks (p-RBF NNs) designed with the aid of …
polynomial Radial Basis Function Neural Networks (p-RBF NNs) designed with the aid of …
A novel training algorithm for RBF neural network using a hybrid fuzzy clustering approach
This paper introduces a novel clustering-based algorithm to train Gaussian type radial basis
function neural networks. In contrast to existing approaches, we develop a specialized …
function neural networks. In contrast to existing approaches, we develop a specialized …
Design of hybrid radial basis function neural networks (HRBFNNs) realized with the aid of hybridization of fuzzy clustering method (FCM) and polynomial neural …
In this study, we propose Hybrid Radial Basis Function Neural Networks (HRBFNNs)
realized with the aid of fuzzy clustering method (Fuzzy C-Means, FCM) and polynomial …
realized with the aid of fuzzy clustering method (Fuzzy C-Means, FCM) and polynomial …
[HTML][HTML] Design methodology for radial basis function neural networks classifier based on locally linear reconstruction and conditional fuzzy C-means clustering
In this study, a new design method for Fuzzy Radial Basis Function Neural Networks
classifier is proposed. The proposed approach is based on conditional Fuzzy C-Means …
classifier is proposed. The proposed approach is based on conditional Fuzzy C-Means …