Spiking neurons model a type of biological neural system where information is encoded with spike times. In this paper, a new method for decoding input spikes according to their absolute arrival times is proposed. The output times, which are responses to different input patterns, can differentiate these input patterns uniquely. Features of Spiking Neural Networks (SNN) such as actual spike input...

in this study, optimized adaptive neuro-fuzzy inference system (oanfis) was employed on a set of daily, weekly, 10-days and monthly data of inflow water into the alavian dam to predict the real-time inflow of the reservoir. sequential and exhaustive search algorithms were used to determine the numbers and time steps of the model inputs and also reducing the prediction’s errors. in sequential se...

In a smooth input/output process y = f(x), if the input data x ∈ R is noise free and only the output data y is corrupted by noise, then a near optimal smooth model ĝ will be a close approximation to f . However, as previously observed, for example in [Kantz and Schreiber, 2004], if the input data is also corrupted by noise then this is no longer the case. With noise on the inputs, the best pred...

In this paper, we provide optimal solutions to two different (but related) input/output design problems involving large-scale linear dynamical systems, where the cost associated to each directly actuated/measured state variable can take different values, but is independent of the labeled input/output variable. Under these conditions, we first aim to determine and characterize the input/output p...

The Task System and Item Architecture (TSIA) is a model for transparent application execution. In many real-world projects, a TSIA provides a simple application with a transparent reliable, distributed, heterogeneous, adaptive, dynamic, real-time, parallel, secure or other execution. TSIA is suitable for many applications, not just for the simple applications served to date. This presentation s...