KPCA Based Multi-Spectral Segments Feature Extraction and GA Based Compound Optimization for Frequency Spectrum Data Modeling

نویسندگان

  • Jian Tang
  • Tianyou Chai
  • Wen Yu
  • Lijie Zhao
  • S. Joe Qin
چکیده

Mill load (ML) estimation plays a major role in improving the grinding production rate (GPR) and the product quality of the grinding process. The ML parameters, such as mineral to ball volume ratio (MBVR), pulp density (PD) and charge volume ratio (CVR), reflect the load inside the ball mill accurately. The relative amplitudes of the high-dimensional frequency spectrum of shell vibration signals contain the information about the ML parameters. In this paper, a kernel principal component analysis (KPCA) based multi-spectral segments feature extraction and genetic algorithm (GA) based Combinatorial optimization method is proposed to estimate the ML parameters. Spectral peak clustering algorithm based knowledge is first used to partition the spectrum into several segments with their physical meaning. Then, the spectral principal components (PCs) of different segments are extracted using KPCA. The candidate input features are serial combinated with mill power. At last, GA with Akaike’s information criteria (AIC) is used to select the input features and the parameters for the least square-support vector machine (LS-SVM) simultaneously. Experimental results show that the proposed approach has higher accuracy and better predictive performance than the other normal approaches.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

An Optimal Feature Subset Selection for Leaf Analysis

This paper describes an optimal approach for feature subset selection to classify the leaves based on Genetic Algorithm (GA) and Kernel Based Principle Component Analysis (KPCA). Due to high complexity in the selection of the optimal features, the classification has become a critical task to analyse the leaf image data. Initially the shape, texture and colour features are extracted from the lea...

متن کامل

Overlap-based feature weighting: The feature extraction of Hyperspectral remote sensing imagery

Hyperspectral sensors provide a large number of spectral bands. This massive and complex data structure of hyperspectral images presents a challenge to traditional data processing techniques. Therefore, reducing the dimensionality of hyperspectral images without losing important information is a very important issue for the remote sensing community. We propose to use overlap-based feature weigh...

متن کامل

Identification of Noisy Speech Signals using Bispectrum-based 2D- MFCC and Its Optimization through Genetic Algorithm as a Feature Extraction Subsystem

Power-spectrum-based Mel-Frequency Cepstrum Coefficients (MFCC) is usually used as a feature extractor in a speaker identification system. This one-dimensional feature extraction subsystem, however, shows low recognition rates for identifying utterance speech signals under harsh noise conditions. In this paper, we have developed a speaker identification system based on Bispectrum data that is m...

متن کامل

Comparison Between Different Methods of Feature Extraction in BCI Systems Based on SSVEP

‎There are different feature extraction methods in brain-computer interfaces (BCI) based on Steady-State Visually Evoked Potentials (SSVEP) systems‎. ‎This paper presents a comparison of five methods for stimulation frequency detection in SSVEP-based BCI systems‎. ‎The techniques are based on Power Spectrum Density Analysis (PSDA)‎, ‎Fast Fourier Transform (FFT)‎, ‎Hilbert‎- ‎Huang Transform (H...

متن کامل

Parametric optimization of Nd:YAG laser microgrooving on aluminum oxide using integrated RSM-ANN-GA approach

Nowadays in highly competitive precision industries, the micromachining of advanced engineering materials is extremely demand as it has extensive application in the fields of automobile, electronic, biomedical and aerospace engineering. The present work addresses the modeling and optimization study on dimensional deviations of square-shaped microgroove in laser micromachining of aluminum oxide ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2011