A new distributed node localization algorithm named mobile beacons-improved particle filter （MB-IPF） was proposed. In the algorithm, the mobile nodes equipped with globe position system （GPS） move around in the wi...A new distributed node localization algorithm named mobile beacons-improved particle filter （MB-IPF） was proposed. In the algorithm, the mobile nodes equipped with globe position system （GPS） move around in the wireless sensor network （WSN） field based on the Gauss-Markov mobility model, and periodically broadcast the beacon messages Each unknown node estimates its location in a fully distributed mode based on the received mobile beacons. The localization algorithm is based on the IPF and several refinements, including the proposed weighted centroid algorithm, the residual resampling algorithm, and the markov chain monte carlo （MCMC） method etc., which were also introduced for performance improvement. The simulation results show that our proposed algorithm is efficient for most applications.展开更多
Echo state network （ESN） proposed by Jaeger in 2001 has remarkable capabilities of approximating dynamics for complex systems, such as Mackey-Glass problem. Compared to that of ESN, the scale-free highlyclustered ES...Echo state network （ESN） proposed by Jaeger in 2001 has remarkable capabilities of approximating dynamics for complex systems, such as Mackey-Glass problem. Compared to that of ESN, the scale-free highlyclustered ESN, i.e., SHESN, which state reservoir has both small-world phenomenon and scale-free feature, exhibits even stronger approximation capabilities of dynamics and better echo state property. In this paper, we extend the state reservoir of SHESN using leaky integrator neurons and inhibitory connections, inspired from the advances in neurophysiology. We apply the extended SHESN, called eSHESN, to the Mackey-Glass prediction problem. The experimental results show that the e-SHESN considerably outperforms the SHESN in prediction capabilities of the Mackey-Glass chaotic time-series. Meanwhile, the interesting complex network characteristic in the state reservoir, including the small-world property and the scale-free feature, remains unchanged. In addition, we unveil that the original SHESN may be unstable in some cases. However, the proposed e-SHESN model is shown to be able to address the flaw through the enhancement of the network stability. Specifically, by using the ridge regression instead of the linear regression, the stability of e-SHESN could be much more largely improved.展开更多
文摘A new distributed node localization algorithm named mobile beacons-improved particle filter （MB-IPF） was proposed. In the algorithm, the mobile nodes equipped with globe position system （GPS） move around in the wireless sensor network （WSN） field based on the Gauss-Markov mobility model, and periodically broadcast the beacon messages Each unknown node estimates its location in a fully distributed mode based on the received mobile beacons. The localization algorithm is based on the IPF and several refinements, including the proposed weighted centroid algorithm, the residual resampling algorithm, and the markov chain monte carlo （MCMC） method etc., which were also introduced for performance improvement. The simulation results show that our proposed algorithm is efficient for most applications.
文摘Echo state network （ESN） proposed by Jaeger in 2001 has remarkable capabilities of approximating dynamics for complex systems, such as Mackey-Glass problem. Compared to that of ESN, the scale-free highlyclustered ESN, i.e., SHESN, which state reservoir has both small-world phenomenon and scale-free feature, exhibits even stronger approximation capabilities of dynamics and better echo state property. In this paper, we extend the state reservoir of SHESN using leaky integrator neurons and inhibitory connections, inspired from the advances in neurophysiology. We apply the extended SHESN, called eSHESN, to the Mackey-Glass prediction problem. The experimental results show that the e-SHESN considerably outperforms the SHESN in prediction capabilities of the Mackey-Glass chaotic time-series. Meanwhile, the interesting complex network characteristic in the state reservoir, including the small-world property and the scale-free feature, remains unchanged. In addition, we unveil that the original SHESN may be unstable in some cases. However, the proposed e-SHESN model is shown to be able to address the flaw through the enhancement of the network stability. Specifically, by using the ridge regression instead of the linear regression, the stability of e-SHESN could be much more largely improved.
