309 research outputs found
Collaborative Training in Sensor Networks: A graphical model approach
Graphical models have been widely applied in solving distributed inference
problems in sensor networks. In this paper, the problem of coordinating a
network of sensors to train a unique ensemble estimator under communication
constraints is discussed. The information structure of graphical models with
specific potential functions is employed, and this thus converts the
collaborative training task into a problem of local training plus global
inference. Two important classes of algorithms of graphical model inference,
message-passing algorithm and sampling algorithm, are employed to tackle
low-dimensional, parametrized and high-dimensional, non-parametrized problems
respectively. The efficacy of this approach is demonstrated by concrete
examples
An Upper Bound on the Convergence Time for Quantized Consensus of Arbitrary Static Graphs
We analyze a class of distributed quantized consensus algorithms for
arbitrary static networks. In the initial setting, each node in the network has
an integer value. Nodes exchange their current estimate of the mean value in
the network, and then update their estimation by communicating with their
neighbors in a limited capacity channel in an asynchronous clock setting.
Eventually, all nodes reach consensus with quantized precision. We analyze the
expected convergence time for the general quantized consensus algorithm
proposed by Kashyap et al \cite{Kashyap}. We use the theory of electric
networks, random walks, and couplings of Markov chains to derive an upper bound for the expected convergence time on an arbitrary graph of size
, improving on the state of art bound of for quantized consensus
algorithms. Our result is not dependent on graph topology. Example of complete
graphs is given to show how to extend the analysis to graphs of given topology.Comment: to appear in IEEE Trans. on Automatic Control, January, 2015. arXiv
admin note: substantial text overlap with arXiv:1208.078
A Novel Fuzzy Clustering Algorithm for Radial Basis Function Neural Network
A Fuzzy Radial basis function neural network (FRBFNN) classifier is proposed in the framework of Radial basis function neural network (RBFNN). This classifier is constructed using class-specific fuzzy clustering to form the clusters which represent the neurons i.e. fuzzy set hyperspheres (FSHs) in the hidden layer of FRBFNN. The creation of these FSHs is based on the maximum spread from inter-class information and intra-class fuzzy membership mechanism. The proposed approach is fast, independent of parameters, and shows good data visualization. The Least mean square training between the hidden layer to output layer in RBFNN is avoided, thus reduces the time complexity. The FRBFNN is trained quickly due to the fast converge of input data to form the FHSs in the hidden layer. The output is determined by the union operation of the FHSs outputs which are connected to the class nodes in the output layer. The performance of the proposed FRBFNN is compared with the other RBFNNs using ten benchmark datasets. The empirical findings demonstrate that the proposed FRBFNN is highly efficient classifier for pattern recognition
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