discriminant analysis in ar(p) plus different noises processes

Discriminant Analysis of Random Processes

The problem multialternative recognition of nonstationary processes on the basis of dynamic models is investigated in the paper. The algorithms of pointwise and group classifications are compared. Clustering algorithms based on nonlinear mapping of the segments of random processes onto the plain are used to construct the classifiers.

A Wavelet-based Discriminant Procedure for Signal-plus-noise Processes

Face Recognition using Discrete Cosine Transform plus Linear Discriminant Analysis

recognition is a biometric identification method which among the other methods such as, finger print identification, speech recognition, signature and hand written recognition has assigned a special place to itself. In principle, the biometric identification methods include a wide range of sciences such as machine vision, image processing, pattern recognition neural networks and has various app...

Analysis of Kalman Filter with Correlated Noises under Different Dependence ?

In the discrete linear system, Kalman filter will be suboptimal for state estimation when they are correlated between process and measurement noise. According to the occurring time of association, they are divided into two cases: one case is the correlated noise at the same time, and the other case is the correlated noise one time apart. There are two solutions called Method A and Method B to d...

Performance Analysis of Effective Image Restoration Techniques at Different Noises

Image processing is one the hot research topic for the researchers. The image obtained from the different sensors or satellites are degraded and are not suitable for human perception or further process. The images are degraded by many parameters like noises in the environment or blurring of the image during image acquisition or during processing of the image. In order to improve the quality of ...

Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis

Ž . Principal component analysis PCA is the most commonly used dimensionality reduction technique for detecting and diagnosing faults in chemical processes. Although PCA contains certain optimality properties in terms of fault detection, and Ž . has been widely applied for fault diagnosis, it is not best suited for fault diagnosis. Discriminant partial least squares DPLS has been shown to impro...