As an important artificial neural network, associative memory model can be employed to mimic human thinking and machine intelligence. In this paper, first, a multi-valued many-to-many Gaussian associative memory model (M3GAM) is proposed by introducing the Gaussian unidirectional associative memory model (GUAM) and Gaussian bidirectional associative memory model (GBAM) into Hattori {et al}'s multi-module associative memory model ((MMA)2). Second, the M3GAM's asymptotical stability is proved theoretically in both synchronous and asynchronous update modes, which ensures that the stored patterns become the M3GAM's stable points. Third, by substituting the general similarity metric for the negative squared Euclidean distance in M3GAM, the generalized multi-valued many-to-many Gaussian associative memory model (GM3GAM) is presented, which makes the M3GAM become its special case. Finally, we investigate the M3GAM's application in association-based image retrieval, and the computer simulation results verify the M3GAM's robust performance.
Linear relations, containing measurement errors in input and output data, are taken into account in this paper. Parameters of these so-called \emph{errors-in-variables} (EIV) models can be estimated by minimizing the \emph{total least squares} (TLS) of the input-output disturbances. Such an estimate is highly non-linear. Moreover in some realistic situations, the errors cannot be considered as independent by nature. \emph{Weakly dependent} (α- and φ-mixing) disturbances, which are not necessarily stationary nor identically distributed, are considered in the EIV model. Asymptotic normality of the TLS estimate is proved under some reasonable stochastic assumptions on the errors. Derived asymptotic properties provide necessary basis for the validity of block-bootstrap procedures.
The variance of the number of lattice points inside the dilated bounded set $rD$ with random position in $\Bbb R^d$ has asymptotics $\sim r^{d-1}$ if the rotational average of the squared modulus of the Fourier transform of the set is $O(\rho ^{-d-1})$. The asymptotics follow from Wiener's Tauberian theorem.
This paper studies the leader-following consensus problem of second-order multi-agent systems with directed topologies. By employing the asynchronous sampled-data protocols, sufficient conditions for leader-following consensus with both constant velocity leader and variable velocity leader are derived. {Leader-following quasi-consensus can be achieved in multi-agent systems when all the agents sample the information asynchronously.} Numerical simulations are provided to verify the theoretical results.
The corpus contains pronunciation lexicon and n-gram counts (unigrams, bigrams and trigrams) that can be used for constructing the language model for air traffic control communication domain. It could be used together with the Air Traffic Control Communication corpus (http://hdl.handle.net/11858/00-097C-0000-0001-CCA1-0). and Technology Agency of the Czech Republic, project No. TA01030476