This paper proposes a method of feature selection and classifcaition based on ant colony algorithm for hyperspectral remote sensing image. After all features are randomly projected on a plane, each ant stochastically selects a feature on the plane firstly, and then decides which route to be selected in terms of the criterion function among features. Whereafter the feature combination is formed. At last, using combination feature, the classification of AVIRIS image is carried out by maximum likelihood classifier. In order to verify the effectiveness of this algorithm, the approach is compared with the classical suboptimal search technique, using AVIRIS images as a data set. Experimental results prove the processing that based on ant colony algorithm is more effective and is fit for the band selection of hyperspectral image.

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Since digital implementation of repetitive controllers is simpler than analog in many industrial applications, some design criteria for a discrete-time repetitive controller to suppress periodic disturbances within a specified frequency range are presented. The small gain theorem is applied to perform the controller design for system stability. Upper and lower bounds of the discrete-time repetitive controller parameters that ensure the stability of the control system and the tracking performance are derived based on the shaping of the sensitivity functional. Therefore, the repetitive controller is designed by the parameters in between upper and lower bounds. The control performance of the presented method is evaluated in a turntable system which is exposed to periodic disturbances. Computer simulation is presented to illustrate the effectiveness of the proposed repetitive controller design.

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7his paper presentV a modified recursive Bayesian estimation algorithm that combines an importance sampling based measurement update step with a bank of Sigma-Point Kalman Filters for the time-update and proposal distribution generation. The posterior state density is represented by a Gaussian mixture model that is recovered from the weighted particle set of the measurement update step by means of a weighted Expectation-Maximization (EM) algorithm. This step replaces the resampling stage needed by most particle filters and mitigates the sample depletion problem. A tracking example shows that this new approach has a better estimation performance than standard particle filter.