Dynamic learning with the EM algorithm for neural networks

In this paper, we derive an EM algorithm for nonlinear state space models. We use it to estimate jointly the neural network weights, the model uncertainty and the noise in the data. In the E-step we apply a forwardbackward Rauch-Tung-Striebel smoother to compute the network weights. For the M-step, we derive expressions to compute the model uncertainty and the measurement noise. We find that the method is intrinsically very powerful, simple and stable.

Modelling nonlinear vehicle dynamics with neural networks

The nonlinear modelling ability of neural networks has been widely recognised as an effective tool to identify and control dynamic systems, with applications including nonlinear vehicle dynamics which this paper focuses on using multi-layer perceptron networks. Existing neural network literature does not detail some of the factors which effect neural network nonlinear modelling ability. This paper investigates into and concludes on required network size, structure and initial weights, considering results for networks of converged weights. The paper also presents an online training method and an error measure representing the network's parallel modelling ability over a range of operating conditions. Copyright © 2010 Inderscience Enterprises Ltd.

Synchronization in networks of identical linear systems

The paper investigates the synchronization of a network of identical linear state-space models under a possibly time-varying and directed interconnection structure. The main result is the construction of a dynamic output feedback coupling that achieves synchronization if the decoupled systems have no exponentially unstable mode and if the communication graph is uniformly connected. The result can be interpreted as a generalization of classical consensus algorithms. Stronger conditions are shown to be sufficient-but to some extent, also necessary-to ensure synchronization with the diffusive static output coupling often considered in the literature. © 2009 Elsevier Ltd. All rights reserved.

Synchronization in networks of identical linear systems

The paper investigates the synchronization of a network of identical linear time-invariant state-space models under a possibly time-varying and directed interconnection structure. The main result is the construction of a dynamic output feedback coupling that achieves synchronization if the decoupled systems have no exponentially unstable mode and if the communication graph is uniformly connected. Stronger conditions are shown to be sufficient - but to some extent, also necessary - to ensure synchronization with the diffusive static output coupling often considered in the literature. © 2008 IEEE.

Characterization and deformation response of orthotropic fibre networks with auxetic out-of-plane behaviour

In the present paper, highly porous fibre networks made of 316L fibres, with different fibre volume fractions, are characterized in terms of network architecture, elastic constants and fracture energies. Elastic constants are measured using quasi-static mechanical and modal vibration testing, yielding local and globally averaged properties, respectively. Differences between quasi-static and dynamic elastic constants are attributed to through-thickness shear effects. Regardless of the method employed, networks show signs of material inhomogeneity at high fibre densities, in agreement with X-ray nanotomography results. Strong auxetic (or negative Poisson's ratio) behaviour is observed in the through-thickness direction, which is attributed to fibre kinking induced during processing. Measured fracture energies are compared with model predictions incorporating information about in-plane fibre orientation distribution, fibre volume fraction and single fibre work of fracture. Experimental values are broadly consistent with model predictions, based on the assumption that this energy is primarily associated with plastic deformation of individual fibres within a process zone of the same order as the inter-joint spacing. © 2013 Published by Elsevier Ltd. on behalf of Acta Materialia Inc. All rights reserved.