The kernel-based nonlinear multivariate grey model

TL;DR: The proposed conformable fractional grey model is more efficient in longer term prediction and non-smooth time series forecasting than the existing models and introduces the Brute Force method to optimize its fractional order.

TL;DR: The findings demonstrate that the new MFGM model achieves the best performance, which confirms the effectiveness of background-value optimization in a multi-variable grey forecasting model.

TL;DR: A novel hybrid forecasting model based on an improved grey forecasting mode optimized by multi-objective ant lion optimization algorithm is successfully developed, which can not only be utilized to dynamic choose the best input training sets, but also obtain satisfactory forecasting results with high accuracy and strong ability.

TL;DR: A novel hybrid forecasting system was successfully developed, including four modules: data preprocessing module, optimization module, forecasting module and evaluation module, which showed that the hybrid system not only can be able to satisfactorily approximate the actual value, but also be regarded as an effective and simple tool adopted in smart grids.

TL;DR: This tutorial gives an overview of the basic ideas underlying Support Vector (SV) machines for function estimation, and includes a summary of currently used algorithms for training SV machines, covering both the quadratic programming part and advanced methods for dealing with large datasets.

TL;DR: In this paper, the stability and stabilization of a grey system whose state matrix is triangular is studied and the displacement operator and established transfer developed by the author are the indispensable tool for the grey system.

TL;DR: Support Vector Machines Basic Methods of Least Squares Support Vector Machines Bayesian Inference for LS-SVM Models Robustness Large Scale Problems LS- sVM for Unsupervised Learning LS- SVM for Recurrent Networks and Control.

TL;DR: In this article, a non-linear classification technique based on Fisher's discriminant is proposed and the main ingredient is the kernel trick which allows the efficient computation of Fisher discriminant in feature space.