6 research outputs found
Structure Selection of Polynomial NARX Models using Two Dimensional (2D) Particle Swarms
The present study applies a novel two-dimensional learning framework
(2D-UPSO) based on particle swarms for structure selection of polynomial
nonlinear auto-regressive with exogenous inputs (NARX) models. This learning
approach explicitly incorporates the information about the cardinality (i.e.,
the number of terms) into the structure selection process. Initially, the
effectiveness of the proposed approach was compared against the classical
genetic algorithm (GA) based approach and it was demonstrated that the 2D-UPSO
is superior. Further, since the performance of any meta-heuristic search
algorithm is critically dependent on the choice of the fitness function, the
efficacy of the proposed approach was investigated using two distinct
information theoretic criteria such as Akaike and Bayesian information
criterion. The robustness of this approach against various levels of
measurement noise is also studied. Simulation results on various nonlinear
systems demonstrate that the proposed algorithm could accurately determine the
structure of the polynomial NARX model even under the influence of measurement
noise
Binary Competitive Swarm Optimizer Approaches For Feature Selection
Feature selection is known as an NP-hard combinatorial problem in which the possible feature subsets increase exponentially with the number of features. Due to the increment of the feature size, the exhaustive search has become impractical. In addition, a feature set normally includes irrelevant, redundant, and relevant information. Therefore, in this paper, binary variants of a competitive swarm optimizer are proposed for wrapper feature selection. The proposed approaches are used to select a subset of significant features for classification purposes. The binary version introduced here is performed by employing the S-shaped and V-shaped transfer functions, which allows the search agents to move on the binary search space. The proposed approaches are tested by using 15 benchmark datasets collected from the UCI machine learning repository, and the results are compared with other conventional feature selection methods. Our results prove the capability of the proposed binary version of the competitive swarm optimizer not only in terms of high classification performance, but also low computational cost