Automata theoretic aspects of temporal behaviour and computability in logical neural networks

Imperial Users onl

A Wiener-Laguerre model of VIV forces given recent cylinder velocities

Slender structures immersed in a cross flow can experience vibrations induced by vortex shedding (VIV), which cause fatigue damage and other problems. VIV models in engineering use today tend to operate in the frequency domain. A time domain model would allow to capture the chaotic nature of VIV and to model interactions with other loads and non-linearities. Such a model was developed in the present work: for each cross section, recent velocity history is compressed using Laguerre polynomials. The compressed information is used to enter an interpolation function to predict the instantaneous force, allowing to step the dynamic analysis. An offshore riser was modeled in this way: Some analyses provided an unusually fine level of realism, while in other analyses, the riser fell into an unphysical pattern of vibration. It is concluded that the concept is promissing, yet that more work is needed to understand orbit stability and related issues, in order to further progress towards an engineering tool

Architectural designs of Echo State Network

It investigates systematically the reservoir construction of Echo State Network (ESN). This thesis proposes two very simple deterministic ESN organisation (Simple Cycle reservoir (SCR) and Cycle Reservoir with Jumps (CRJ). Simple Cycle reservoir (SCR) is sufficient to obtain performances comparable to those of the classical ESN. While Cycle Reservoir with Jumps (CRJ) significantly outperform the those of the classical ESN. This thesis also studies and discusses three reservoir characterisations - short-term memory capacity (MC), eigen-spectrum of the reservoir weight matrix and Lyapunov Exponent with their relation to the ESN performance. It also designs and utilises an ensemble of ESNs with diverse reservoirs whose collective readout is obtained through Negative Correlation Learning (NCL) of ensemble of Multi-Layer Perceptrons (MLP), where each individual MPL realises the readout from a single ESN. Finally, this thesis investigates the relation between two quantitative measures characterising short term memory in input driven dynamical systems, namely the short term memory capacity (MC), and the Fisher memory curve (FMC)

Editors' Introduction to [Algorithmic Learning Theory: 21st International Conference, ALT 2010, Canberra, Australia, October 6-8, 2010. Proceedings]

Learning theory is an active research area that incorporates ideas, problems, and techniques from a wide range of disciplines including statistics, artificial intelligence, information theory, pattern recognition, and theoretical computer science. The research reported at the 21st International Conference on Algorithmic Learning Theory (ALT 2010) ranges over areas such as query models, online learning, inductive inference, boosting, kernel methods, complexity and learning, reinforcement learning, unsupervised learning, grammatical inference, and algorithmic forecasting. In this introduction we give an overview of the five invited talks and the regular contributions of ALT 2010