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.

Laboratory-scale fluidized bed rig for high-temperature tube wastage studies

As part of a study of the wear of candidate heat exchanger tube materials for use in fluidized bed combustors, two similar laboratory-scale rigs have been built and characterized. Specimens of selected alloys are carried on counter-rotating rotors immersed in a fluidized bed, and are exposed to particle impact velocities of up to approximately 3 ms-1 at temperatures up to 1000°C. The performance of this design of apparatus has been investigated in detail. The effects of several experimental variables have been studied, including angle of particle impact, specimen speed, position of the rotor within the fluidized bed, duration of exposure, bed material particle size, degradation of the bed material, degree of fluidization of the bed, and size of specimen. In many cases the results obtained with steel specimens at elevated temperatures are similar to those observed with polymeric specimens at low temperatures.

Free-Space Interconnect for Optical Switch and Analogue Neural Network

In this paper, the architecture of a vector-matrix multiplier (MVM) is simulated. The optical design can be made compact by the use of GRIN lenses for the optical fan-in. The intended application area was in storage area networks (SANs) but the concept can be applied to a neural network. © 2011 Allerton Press, Inc.