Abstract: This paper deals with an on-line identification method
of continuous-time Hammerstein systems by using the radial basis
function (RBF) networks and immune algorithm (IA). An unknown
nonlinear static part to be estimated is approximately represented
by the RBF network. The IA is efficiently combined with the
recursive least-squares (RLS) method. The objective function for the
identification is regarded as the antigen. The candidates of the RBF
parameters such as the centers and widths are coded into binary bit
strings as the antibodies and searched by the IA. On the other hand,
the candidates of both the weighting parameters of the RBF network
and the system parameters of the linear dynamic part are updated
by the RLS method. Simulation results are shown to illustrate the
proposed method.
Abstract: Spatial trends are one of the valuable patterns in geo
databases. They play an important role in data analysis and
knowledge discovery from spatial data. A spatial trend is a regular
change of one or more non spatial attributes when spatially moving
away from a start object. Spatial trend detection is a graph search
problem therefore heuristic methods can be good solution. Artificial
immune system (AIS) is a special method for searching and
optimizing. AIS is a novel evolutionary paradigm inspired by the
biological immune system. The models based on immune system
principles, such as the clonal selection theory, the immune network
model or the negative selection algorithm, have been finding
increasing applications in fields of science and engineering.
In this paper, we develop a novel immunological algorithm based
on clonal selection algorithm (CSA) for spatial trend detection. We
are created neighborhood graph and neighborhood path, then select
spatial trends that their affinity is high for antibody. In an
evolutionary process with artificial immune algorithm, affinity of
low trends is increased with mutation until stop condition is satisfied.