Robust neurofuzzy rule base knowledge extraction and estimation using subspace decomposition combined with regularization and D-optimality

A new robust neurofuzzy model construction algorithm has been introduced for the modeling of a priori unknown dynamical systems from observed finite data sets in the form of a set of fuzzy rules. Based on a Takagi-Sugeno (T-S) inference mechanism a one to one mapping between a fuzzy rule base and a model matrix feature subspace is established. This link enables rule based knowledge to be extracted from matrix subspace to enhance model transparency. In order to achieve maximized model robustness and sparsity, a new robust extended Gram-Schmidt (G-S) method has been introduced via two effective and complementary approaches of regularization and D-optimality experimental design. Model rule bases are decomposed into orthogonal subspaces, so as to enhance model transparency with the capability of interpreting the derived rule base energy level. A locally regularized orthogonal least squares algorithm, combined with a D-optimality used for subspace based rule selection, has been extended for fuzzy rule regularization and subspace based information extraction. By using a weighting for the D-optimality cost function, the entire model construction procedure becomes automatic. Numerical examples are included to demonstrate the effectiveness of the proposed new algorithm.

“Financial alchemy” or a zero sum game? Real estate finance, securitisation and the UK property market

Following the US model, the UK has seen considerable innovation in the funding, finance and procurement of real estate in the last decade. In the growing CMBS market asset backed securitisations have included $2.25billion secured on the Broadgate office development and issues secured on Canary Wharf and the Trafford Centre regional mall. Major occupiers (retailer Sainsbury’s, retail bank Abbey National) have engaged in innovative sale & leaseback and outsourcing schemes. Strong claims are made concerning the benefits of such schemes – e.g. British Land were reported to have reduced their weighted cost of debt by 150bp as a result of the Broadgate issue. The paper reports preliminary findings from a project funded by the Corporation of London and the RICS Research Foundation examining a number of innovative schemes to identify, within a formal finance framework, sources of added value and hidden costs. The analysis indicates that many of the gains claimed conceal costs – in terms of market value of debt or flexibility of management – while others result from unusual firm or market conditions (for example utilising the UK long lease and the unusual shape of the yield curve). Nonetheless, there are real gains resulting from the innovations, reflecting arbitrage and institutional constraints in the direct (private) real estate market

Symmetric Push-Sum Protocol for decentralised aggregation

Gossip (or Epidemic) protocols have emerged as a communication and computation paradigm for large-scale networked systems. These protocols are based on randomised communication, which provides probabilistic guarantees on convergence speed and accuracy. They also provide robustness, scalability, computational and communication efficiency and high stability under disruption. This work presents a novel Gossip protocol named Symmetric Push-Sum Protocol for the computation of global aggregates (e.g., average) in decentralised and asynchronous systems. The proposed approach combines the simplicity of the push-based approach and the efficiency of the push-pull schemes. The push-pull schemes cannot be directly employed in asynchronous systems as they require synchronous paired communication operations to guarantee their accuracy. Although push schemes guarantee accuracy even with asynchronous communication, they suffer from a slower and unstable convergence. Symmetric Push- Sum Protocol does not require synchronous communication and achieves a convergence speed similar to the push-pull schemes, while keeping the accuracy stability of the push scheme. In the experimental analysis, we focus on computing the global average as an important class of node aggregation problems. The results have confirmed that the proposed method inherits the advantages of both other schemes and outperforms well-known state of the art protocols for decentralized Gossip-based aggregation.