Swarm intelligence is an innovative computational way to solving hard problems.
This discipline is inspired by the behavior of social insects such as fish
schools and bird flocks and colonies of ants, termites, bees and wasps. In general,
this is done by mimicking the behavior of the biological creatures within
their swarms and colonies.
The main features of the considered identification problem are that there is no an a priori separation of the variables into inputs and outputs and the approximation criterion, called misfit, does not depend on the model representation. The misfit is defined as the minimum of the l2-norm between the given time series and a time series that is consistent with the approximate model. The misfit is equal to zero if and only if the model is exact and the smaller the misfit is (by definition) the more accurate the model is. The considered model class consists of all linear time-invariant systems of bounded complexity and the complexity is specified by the number of inputs and the smallest number of lags in a difference equation representation. We present a Matlab function for approximate identification based on misfit minimization. Although the problem formulation is representation independent, we use input/state/output representations of the system in order
A program for solving equations using Newton s Method.Just need to modify the data in the main function. If there is any bugs, please contact chenxiang@cad.zju.edu.cn
CCE is a multi-instance learning method solving multi-instance problems through adapting multi-instance representation to single-instance algorithms, which is quite different from existing multi-instance learning algorithms which attempt to adapt single-instance algorithms to multi-instance representation
These instances, whenmapped to an N-dimensional space, represent a core set that can be
used to construct an approximation to theminimumenclosing ball. Solving the SVMlearning
problem on these core sets can produce a good approximation solution in very fast speed.
For example, the core-vector machine [81] thus produced can learn an SVM for millions of
data in seconds.
