Separate initialization of dynamics and nonlinearities in nonlinear state-space models

This work focuses on the identification of nonlinear dynamic systems. In particular the problem of obtaining good starting values for the identification of nonlinear state-space models is addressed. A fast and efficient initialization algorithm is proposed, combining the use of methods from the statistical learning community to model the nonlinearities and classic system identification tools to capture system dynamics. The performance of the method is evaluated on simulation examples

Promoting adaptive flood risk management: the role and potential of flood recovery mechanisms

There is a high potential for recovery mechanisms to be used to incentivise the uptake of flood mitigation and loss reduction measures, undertake adaptation and promote community resilience. Indeed, creating a resilient response to flooding requires flood risk management approaches to be aligned and it needs to be ensured that recovery mechanisms to not provide disincentives for individuals and business to take proactive action to reduce risk. However, the degree to which it is desirable and effective for insurers and governments providing compensation to promote resilience and risk reduction depends upon how the cover or compensation is organised and the premiums which are charged. A review of international flood recovery mechanisms has been undertaken to identify firstly the types of schemes that exist and their characteristics. Analysis of existing instruments highlights that there are various potential approaches to encourage or require the uptake of flood mitigation and also discourage the construction of new development in high flood risk. However despite the presence of these instruments, those organising recovery mechanisms could be doing much more to incentivise increased resilience

Neural Adaptive Control of a Robot Joint Using Secondary Encoders

Using industrial robots for machining applications in flexible manufacturing processes lacks a high accuracy. The main reason for the deviation is the flexibility of the gearbox. Secondary Encoders (SE) as an additional, high precision angle sensor offer a huge potential of detecting gearbox deviations. This paper aims to use SE to reduce gearbox compliances with a feed forward, adaptive neural control. The control network is trained with a second network for system identification. The presented algorithm is capable of online application and optimizes the robot accuracy in a nonlinear simulation

Bridges over troubled waters: an interdisciplinary framework for evaluating the interconnectedness within fragmented domestic flood risk management systems

Diversification of strategies in Flood Risk Management (FRM) is widely regarded as a necessary step forward in terms of lessening the likelihood and magnitude of flooding, as well as minimizing the exposure of people and property, and in turn the disruption, economic damage, health impacts and other adverse consequences that ensue when floods occur. Thus, diversification is often heralded as an essential condition for enhancing societal resilience to flooding. However, an inevitable consequence of diversifying strategies and practices in FRM is that it can lead to fragmentation within FRM systems, in terms of the distribution of responsibilities between actors and governing rules enacted within different policy domains. This can prove detrimental to the effectiveness of FRM. Building upon the notion of fragmentation developed in legal and governance literature, this paper introduces the concept of ‘bridging mechanisms’, i.e. instruments that remedy fragmentation by enhancing interconnectedness between relevant actors through information transfer, coordination and cooperation. This paper develops a typology of both fragmentation and bridging mechanisms and analyzes their relations, partly drawing upon empirical research conducted within the EU ‘STAR-FLOOD’ project. In turn, this paper outlines a novel interdisciplinary methodological framework for evaluating the degree and quality of the interconnectedness within fragmented domestic FRM systems. A pragmatic, flexible and broadly applicable tool, this framework is both suited for academic purposes, as well as for practically oriented analysis and (re)development of fragmented FRM systems, and potentially other fragmented systems, within the EU and abroad

Inferring short-term volatility indicators from Bitcoin blockchain

In this paper, we study the possibility of inferring early warning indicators (EWIs) for periods of extreme bitcoin price volatility using features obtained from Bitcoin daily transaction graphs. We infer the low-dimensional representations of transaction graphs in the time period from 2012 to 2017 using Bitcoin blockchain, and demonstrate how these representations can be used to predict extreme price volatility events. Our EWI, which is obtained with a non-negative decomposition, contains more predictive information than those obtained with singular value decomposition or scalar value of the total Bitcoin transaction volume

Local prediction of turning points of oscillating time series

For oscillating time series, the prediction is often focused on the turning points. In order to predict the turning point magnitudes and times it is proposed to form the state space reconstruction only from the turning points and modify the local (nearest neighbor) model accordingly. The model on turning points gives optimal prediction at a lower dimensional state space than the optimal local model applied directly on the oscillating time series and is thus computationally more efficient. Monte Carlo simulations on different oscillating nonlinear systems showed that it gives better predictions of turning points and this is confirmed also for the time series of annual sunspots and total stress in a plastic deformation experiment.Comment: 7 pages, 5 figures, 2 tables, submitted to PR