Brain Control: Human-computer Integration Control Based on Brain-computer Interface Approach

Industrial loop detectors (ILDs) are the most common traffic detectors. In Shanghai, most of the ILDs are installed in a single loop way, which can detect various parameters, such as flow, saturation, and so on. However, they cannot detect the speed directly, which is one of the key inputs of intelligent transportation systems (ITS) for identifying the traffic state. Thus, this paper is dedicated to estimate speed accurately. It proposes a new algorithm that multiple kernel support vector regression (MKL-SVR) to complete this goal, which improves the accuracy and robustness of the speed estimation. Extensive experiments have been performed to evaluate the performances of MKL-SVR, compared with polynomial fitting, BP neural networks and SVR. All results indicate that the performances of MKL-SVR are the best and most robust.

Speed estimation of traffic flow using multiple kernel support vector regression

The optimization method based extreme learning machine (optimization-based ELM) is generalized from single-hidden-layer feed-forward neural networks (SLFNs) by making use of kernels instead of neuron-alike hidden nodes. This approach is known for its high scalability, low computational complexity, and mild optimization constrains. The multi-kernel learning (MKL) framework Simple MKL iteratively determines the combination of kernels by gradient descent wrapping a standard support vector machine (SVM) solver. Simple MKL can be applied to many kinds of supervised learning problems to receive a more stable performance with rapid convergence speed. This paper proposes a new approach: MK-ELM (multi-kernel extreme learning machine) that applies Simple MKL framework to the optimization-based ELM algorithm. The performance analysis on binary classification problems with various scales shows that MK-ELM tends to achieve the best generalization performance as well as being the most insensitive to parameters comparing to optimization-based ELM and Simple MKL. As a result, MK-ELM can be implemented in real applications easily.

Optimization-Based Extreme Learning Machine with Multi-kernel Learning Approach for Classification

Network intrusion data has the characters such as small sample, nonlinear and high dimension, so the detection performance of single kernel support vector machine (SK-SVM) is instability. The choice of kernel function and relative parameters plays an important role in SK-SVM. It greatly influences the generalization performance of SK-SVM. According to the limitation of SK-SVM, in this paper we present an intrusion detection method based on multiple kernel support vector machine (MK-SVM). MK-SVM can calculate the weights of kernel functions and Lagrange multipliers simultaneously through semi-infinite linear programming, and thus achieve the choice of kernel functions and the optimization of classifier. Furthermore, in order to reduce the time and space required of this method, we adopt feature selection and clustering method in the process of input data preprocessing. The experimental results using KDD CUP 1999 show that our method has better adaptability and higher detection accuracy than the method based on SK-SVM.

An Intrusion Detection Method Based on Multiple Kernel Support Vector Machine

In this paper, we propose a multiple kernel relevance vector machine (RVM) method based on the adaptive cloud particle swarm optimization (PSO) algorithm to map landslide susceptibility in the low hill area of Sichuan Province, China. In the multi-kernel structure, the kernel selection problem can be solved by adjusting the kernel weight, which determines the single kernel contribution of the final kernel mapping. The weights and parameters of the multi-kernel function were optimized using the PSO algorithm. In addition, the convergence speed of the PSO algorithm was increased using cloud theory. To ensure the stability of the prediction model, the result of a five-fold cross-validation method was used as the fitness of the PSO algorithm. To verify the results, receiver operating characteristic curves (ROC) and landslide dot density (LDD) were used. The results show that the model that used a heterogeneous kernel (a combination of two different kernel functions) had a larger area under the ROC curve (0.7616) and a lower prediction error ratio (0.28%) than did the other types of kernel models employed in this study. In addition, both the sum of two high susceptibility zone LDDs (6.71/100 km2) and the sum of two low susceptibility zone LDDs (0.82/100 km2) demonstrated that the landslide susceptibility map based on the heterogeneous kernel model was closest to the historical landslide distribution. In conclusion, the results obtained in this study can provide very useful information for disaster prevention and land-use planning in the study area.

https://www.mdpi.com/2220-9964/5/10/191/pdf

Landslide Susceptibility Mapping Based on Particle Swarm Optimization of Multiple Kernel Relevance Vector Machines: Case of a Low Hill Area in Sichuan Province, China

Wang Hong

Papers

On Multiple Kernel Learning Methods