Adaptive modelling with tunable RBF network using multi-innovation RLS algorithm assisted by swarm intelligence

In this paper, we propose a new on-line learning algorithm for the non-linear system identification: the swarm intelligence aided multi-innovation recursive least squares (SI-MRLS) algorithm. The SI-MRLS algorithm applies the particle swarm optimization (PSO) to construct a flexible radial basis function (RBF) model so that both the model structure and output weights can be adapted. By replacing an insignificant RBF node with a new one based on the increment of error variance criterion at every iteration, the model remains at a limited size. The multi-innovation RLS algorithm is used to update the RBF output weights which are known to have better accuracy than the classic RLS. The proposed method can produces a parsimonious model with good performance. Simulation result are also shown to verify the SI-MRLS algorithm.

Is housing good for the economy?

Not surprisingly housing researchers and practitioners frequently call for more resources to be devoted to housing. But governments in recent years have devoted fewer resources to housing rather than more. One of the reasons is that housing expenditures have to be seen in terms of the overall resource constraints on the economy and in many instances this requires a macro‐economic perspective. This paper reviews the macro‐economic arguments for and against housing expenditures, particularly through the use of a quantitative policy simulation model.