2007年9月15日 · Based on this analysis, we propose a new fuzzy Actor–Critic reinforcement learning network (FACRLN) including a system architecture and a learning algorithm based on a fuzzy RBF (FRBF) network. We use a four-layer FRBF network to approximate both the action function of the Actor and the value function of the Critic simultaneously.

2004年7月31日 · The fuzzy radial basis function (FRBF) network comprises an integration of the principles of a radial basis function (RBF) network and the fuzzy c-means (FCM) algorithm. A programmable parallel architecture design is proposed for the FRBF, both for FCM clustering at the hidden layer and the weight training at the output layer of the network.

2016年5月2日 · In this section, we propose a FRBF Network approach for FR model with fuzzy input and fuzzy output which are symmetric or nonsymmetric TFNs. Our proposed FRBF Network includes fuzzy input (\(X_p )\), fuzzy output (\(Y_p )\), fuzzy weights between input and hidden unit (\(W_{ij} )\) and also fuzzy weights between hidden and output unit (\(V_j )\).

The FRBF network is designed by integrating the principles of a radial basis function network and the fuzzy c-means algorithm. The architecture of the network is suitably modified at the hidden layer to realise a novel neural implementation of the fuzzy clustering algorithm.

2014年2月16日 · Fuzzy Radial Basis Function Neural Networks (FRBFNNs) are designed by integrating the ideas of a Radial Basis Function Neural Network (RBFNN) and the Fuzzy C-Means (FCM) algorithm [22], [44]. The visible advantage of the FRBFNN is that it does not suffer from the curse of dimensionality in comparison with other networks based on grid portioning.

2001年12月1日 · In this paper new methods based on Radial Basis Function (RBF) and Fuzzy Radial Basis Function (FRBF) are used to solve the problem of text classification, where a set of features extracted for...

Abstract: This work attempts to develop an intelligent decision support system for identification of digital signal type of multilevel magnitude based on fuzzy radial basis function (FRBF) neural network.

2016年3月21日 · Abstract: In this paper, we compare 5 different nonlinear kernels: min-max, RBF, fRBF (folded RBF), acos, and acos-$\chi^2$, on a wide range of publicly available datasets. The proposed fRBF kernel performs very similarly to the RBF kernel. Both RBF and fRBF kernels require an important tuning parameter ($\gamma$).

2016年2月19日 · This paper proposes an improved radial basis function neural network based on fuzzy C-means (FCM) algorithm integrated with principal component analysis (PCA) technology (PCA-FRBF). The PCA is used to denoise and reduce dimensions of data to decrease the training time and errors of the modeling process.

The FRBF network is designed by integrating the principles of a radial basis function network and the fuzzy c-means algorithm. The architecture of the network is suitably modified at the hidden layer to realise a novel neural implementation of the fuzzy clustering algorithm.