By Gianni A. Di Caro, Guy Theraulaz (eds.)
This booklet constitutes the completely refereed post-conference court cases of the seventh overseas convention on Bio-Inspired versions of community, details and Computing platforms (Bionetics 2012), held in Lugano, Switzerland, in December 2012. The 23 revised complete papers provided have been conscientiously reviewed and chosen from forty submissions. They hide subject matters comparable to networking, robotics and neural networks, molecular scale and bioinformatics, optimization and bio-inspired modeling in numerous fields.
Read Online or Download Bio-Inspired Models of Network, Information, and Computing Systems: 7th International ICST Conference, BIONETICS 2012, Lugano, Switzerland, December 10--11, 2012, Revised Selected Papers PDF
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Additional resources for Bio-Inspired Models of Network, Information, and Computing Systems: 7th International ICST Conference, BIONETICS 2012, Lugano, Switzerland, December 10--11, 2012, Revised Selected Papers
However, regardless of the approach (common or separate values of Ψ and ψ), the proposed modiﬁcations in almost all cases decrease the learning time of the ASR-FA algorithm. Best results can be obtained when using the modiﬁcation of both Ψ and ψ (Action 3). 46 M. Gadomska-Kudelska et al. 5 Mean Packet Interval Time Decrease [s] Fig. 6. Performance comparison of diﬀerent versions of the reactive ASR-FA algorithm in terms of the learning time (left) and the mean data packet delay (right). The value of βm set to 1, β modiﬁed independently for each destination.
Network topology used in simulation experiments We observe the average ∈w∀a and variance σa2 of one-way end-to-end delay of session 1 at the sending rate a Mbps. We prepared 10 traﬃc patterns of session 2 whose sending rate is 9 Mbps. e. 1. 0 . e. 0 If the attractor perturbation principle holds, there exists the linear relationship between Δa · σa2 and ∈w∀a+Δa − ∈w∀a as Eq. (1) indicates. 1 −∈w∀a are plotted on Fig. 3 as crosses. 1 − ∈w∀a and we can conﬁrm the attractor perturbation principle in a packet-based network.
Consequently, the EM algorithm is more robust to the noise and small insigniﬁcant peaks that we do not want to model, as they may turn out to bias parameter estimates of nearby peaks (details in ). For each possible path i = 1, 2, . . , M , the conditional probability densities for the gamma components estimated in E-step are calculated as pΓ,i (xt , θk ) = k k fΓ (xt − si |θΓ,i ) πΓ,i M j=1 k k k k πΓ,j fΓ (xt − si |θΓ,j ) + πEx,j fEx (xt − si |θEx,j ) (6) and for the exponential components as pEx,i (xt , θk ) = k k fEx (xt − si |θEx,i ) πEx,i M j=1 k k k k πΓ,j fΓ (xt − si |θΓ,j ) + πEx,j fEx (xt − si |θEx,j ) (7) The robust parameter estimates are calculated in the M-step as follows: k+1 πΓ,i = k+1 πEx,i = k+1 λ = Γ,i k+1 λ = Ex,i 1 N 1 N N pΓ,i (xt , θk ) (8) pEx,i (xt , θk ) (9) t=1 N t=1 N k k νΓ,i t=1 wΓ,it pΓ,i (xt , θ ) N k k t=1 wΓ,it (xt − si )pΓ,i (xt , θ ) N k t=1 wEx,it pEx,i (xt , θ ) N k k t=1 wEx,it (xt − si )pEx,i (xt , θ ) k+1 k νΓ,i = νΓ,i + ak GaνΓ,i (X, θk ) (10) (11) (12) The parameters wit weigh the data points using the Mahalanobis distance dit = |xt − μi |/ηi , for i = 1, .