A self-adaptive communication strategy for flocking in stationary and non-stationary environments

Flocking in Stationary and Non-stationary Environments: A Novel Communication Strategy for Heading Alignment

We propose a novel communication strategy inspired by explicit signaling mechanisms seen in vertebrates, in order to improve performance of self-organized flocking for a swarm of mobile robots. The communication strategy is used to make the robots match each other’s headings. The task of the robots is to coordinately move towards a common goal direction, which might stay fixed or change over ti...

A new adaptive exponential smoothing method for non-stationary time series with level shifts

Simple exponential smoothing (SES) methods are the most commonly used methods in forecasting and time series analysis. However, they are generally insensitive to non-stationary structural events such as level shifts, ramp shifts, and spikes or impulses. Similar to that of outliers in stationary time series, these non-stationary events will lead to increased level of errors in the forecasting pr...

Adaptive and on-line learning in non-stationary environments

Background The computerization of many life activities and the advances in data collection and storage technology lead to obtain mountains of data. They are collected to capture information about a phenomena or a process behaviour. These data are rarely of direct benefit. Thus, a set of techniques and tools are used to extract useful information for decision support, prediction, exploration and...

Structurally Adaptive Localized Mixtures of Experts for Non-Stationary Environments

This paper introduces a neural network capable of dynamically adapting its architecture to realize time variant non-linear input-output maps. This network has its roots in the mixture of experts framework but uses a localized model for the gating network. Modules or experts are grown or pruned depending on the complexity of the modeling problem. The structural adaptation procedure addresses the...

Learning in Non-stationary Environments