The DIVA network model is introduced based on the novel computational principle of divergent autoencoding. DIVA produces excellent fits to classic data sets from Shepard, Hovland & Jenkins (1961) and Medin & Schafffer (1978). DIVA is also resistant to catastrophic interference. Such results have not previously been demonstrated by a model that is not committed to both localist coding of exempla...

In the previous paper (Pupeikis, 1998), the problem of recursive estimation of the state of linear dynamic systems, described by an autoregressive model (AR), in the presence of timevarying outliers in observations to be processed has been considered. An approach to the robust recursive state estimation has been obtained and proved by estimating the real chemical process (Box and Jenkins, 1970)...

For many years, various methods for the identification and estimation of parameters in linear, discrete-time transfer functions have been available and implemented in widely available Toolboxes for Matlab. This paper considers a unified Refined Instrumental Variable (RIV) approach to the estimation of discrete and continuous-time transfer functions characterized by a unified operator that can b...

High performance computing systems are becoming increasingly complex, both in node architecture and in the multiple layers of software stack required to compile and run applications. As a consequence, the likelihood is increasing for application performance regressions to occur as a result of routine upgrades of system software components which interact in complex ways. The purpose of this stud...