Innovation and export performance:evidence from UK and German manufacturing plants

Using comparable plant-level surveys we demonstrate significant differences between the determinants of export performance among the UK and German manufacturing plants. Product innovation, however measured, has a strong effect on the probability and propensity to export in both countries. Being innovative is positively related to export probability in both countries. In the UK the scale of plants’ innovation activity is also related positively to export propensity. In Germany, however, where levels of innovation intensity are higher but the proportion of sales attributable to new products is lower, there is some evidence of a negative relationship between the scale of innovation activity and export performance. Significant differences are identified between innovative and non-innovative plants, especially in their absorption of spill-over effects. Innovative UK plants are more effective in their ability to exploit spill-overs from the innovation activities of companies in the same sector. In Germany, by contrast, non-innovators are more likely to absorb regional and supply-chain spill-over effects. Co-location to other innovative firms is generally found to discourage exporting.

Neural network enhanced self tuning adaptive control application for non-linear control of dynamic systems

The main theme of research of this project concerns the study of neutral networks to control uncertain and non-linear control systems. This involves the control of continuous time, discrete time, hybrid and stochastic systems with input, state or output constraints by ensuring good performances. A great part of this project is devoted to the opening of frontiers between several mathematical and engineering approaches in order to tackle complex but very common non-linear control problems. The objectives are: 1. Design and develop procedures for neutral network enhanced self-tuning adaptive non-linear control systems; 2. To design, as a general procedure, neural network generalised minimum variance self-tuning controller for non-linear dynamic plants (Integration of neural network mapping with generalised minimum variance self-tuning controller strategies); 3. To develop a software package to evaluate control system performances using Matlab, Simulink and Neural Network toolbox. An adaptive control algorithm utilising a recurrent network as a model of a partial unknown non-linear plant with unmeasurable state is proposed. Appropriately, it appears that structured recurrent neural networks can provide conveniently parameterised dynamic models for many non-linear systems for use in adaptive control. Properties of static neural networks, which enabled successful design of stable adaptive control in the state feedback case, are also identified. A survey of the existing results is presented which puts them in a systematic framework showing their relation to classical self-tuning adaptive control application of neural control to a SISO/MIMO control. Simulation results demonstrate that the self-tuning design methods may be practically applicable to a reasonably large class of unknown linear and non-linear dynamic control systems.