The support vector machine is a new type of machine learning methods based on statistical learning theory. Because of good promotion and a higher accuracy, support vector machine has become the research focus of the machine learning community. This paper introduces the basic theory of support vector machine, the basic idea of the classification and currently used support vector machine classification algorithm. Practical problems with which an algorithm, and proves the effectiveness of the algorithm, the final outlook of the prospects of support vector machines in classification applications. Finally the prospect of the prospect of support vector machines in classification applications.

Support Vector Machine Classification Algorithm and Its Application

Hybrid wavelet-support vector machine approach for modelling rainfall-runoff process.

Least squares support vector machine (LS-SVM) has been widely used in engineering practice, using for reference algorithm of combinatorial optimization, this paper puts forward the combinatorial optimization least squares support vector machine algorithm (COLS-SVM). Based on algorithmic analysis of COLS-SVM, it can be used on individual credit evaluation and compared with Lagrange support vector machine (LSVM) and K-nearest neighbor (KNN), the numerical experiment results show that the proposed COLS-SVM algorithm has good classified forecast ability. Individual credit evaluation is a general reflect of individual capital and credit situation, it has great significance and application value.

The Individual Credit Evaluation Based on COLS-SVM

with the rapid development of AI, its application in various fields is more and more extensive This paper expounds the definitions and principles of artificial neural network, expert system and case-based reasoning, as well as its application in the field of engineering evaluation, analyses and summarizes the advantages and disadvantages, and points out possible future directions of the development in the field of the three artificial intelligences

https://iopscience.iop.org/article/10.1088/1742-6596/1069/1/012010/pdf

A Brief Discussion on the Applications of Artificial Intelligence in the Field of Valuation

123456A novel data driven approach is developed for fault diagnosis and remaining useful life (RUL) prognostics for lithium-ion batteries using Least Square Support Vector Machine (LS-SVM) and Memory-Particle Filter (M-PF). Unlike traditional data-driven models for capacity fault diagnosis and failure prognosis, which require multidimensional physical characteristics, the proposed algorithm uses only two variables: Energy Efficiency (EE), and Work Temperature. The aim of this novel framework is to improve the accuracy of incipient and abrupt faults diagnosis and failure prognosis. First, the LSSVM is used to generate residual signal based on capacity fade trends of the Li-ion batteries. Second, adaptive threshold model is developed based on several factors including input, output model error, disturbance, and drift parameter. The adaptive threshold is used to tackle the shortcoming of a fixed threshold. Third, the M-PF is proposed as the new method for failure prognostic to determine Remaining Useful Life (RUL). The M-PF is based on the assumption of the availability of real-time observation and historical data, where the historical failure data can be used instead of the physical failure model within the particle filter. The feasibility of the framework is validated using Li-ion battery prognostic data obtained from the National Aeronautic and Space Administration (NASA) Ames Prognostic Center of Excellence (PCoE). The experimental results show the following: (1) fewer data dimensions for the input data are required compared to traditional empirical models; (2) the proposed diagnostic approach provides an effective way of diagnosing Li-ion battery fault; (3) the proposed prognostic approach can predict the RUL of Li-ion batteries with small error, and has high prediction accuracy; and, (4) the proposed prognostic approach shows that historical failure data can be used instead of a physical failure model in the particle filter.

/pdf/fault-diagnosis-and-failure-prognostics-of-lithium-ion-z0m40jgrng.pdf

Fault Diagnosis and Failure Prognostics of Lithium-ion Battery based on Least Squares Support Vector Machine and Memory Particle Filter Framework

In daily life fault diagnosis is widely used production. With the rapid development of science and technology, the new high-tech products emerged. It is not enough data of samples. Conventional approach is ineffective. It is need to find a good method. The least squares support vector machine algorithm and the proximal of support vector machine applied to fault diagnosis. Through experiments when learning samples is not enough, equipment failure does not reduce and the classification accuracy has increased even. On fault diagnosis the training speed has been to improve and the cost of building has been reduced. Improve overall system performance of fault diagnosis.

Application of Least Squares Support Vector Machine in Fault Diagnosis

In this paper, a model combining the wavelet transform and support vector machine to predict the time series is set up. First, wavelet transform is applied to decompose the series into sub series with different time scales. Then, the SVM is applied to the sub series to simulate and predict future behavior. And then by the inverse wavelet transform, the series are reconstructed, which is the prediction for the time series. The prediction precision of the new model is higher than that of the SVM model and the artificial neural network model for many processes, such as runoff, precipitation, temperature. The universal applicability of the new Wavelet-SVM model and the improvement direction are discussed in this paper.

Prediction Based on Wavelet Transform and Support Vector Machine

Similarity degree and the principle of selection are analyzed in this paper. Fuzzy mathematics is used to Market comparison approach. By applying fuzzy theory into real estate valuation, calculation example shows that the application of the method of real estate assessment can overcome the shortcomings of traditional methods, the valuation results are more scientific and rational and fair.

Application of Fuzzy Mathematics in Real Estate Valuation

After support vector machine multi-class classification algorithms was studied, a kind of improved method of support vector machine for multi-class problems was given in this paper. After experimenting, it is better than one-against-one Method and one-against-the rest Method. This support vector machine for multi-class method saves time and enhances precision of forecast. This improved multi-class method is used to class seismic facies. The precision of forecast is very high.

Improved Support Vector Machine Multi-classification Algorithm

Linear algebra is one of the important basic courses for high school. Its abstract course content caused the students have difficulty to accept, and they are lack of motivation to learn. We analyzed the status and inadequate of the course, followed by the necessity and content analysis of the idea, which put geometric thinking into linear algebra teaching practice. It is expected to provide a theoretical reference for linear algebra teaching practice.

Xiaohong Liu

Papers

Application of Least Squares Support Vector Machine in Fault Diagnosis

Prediction Based on Wavelet Transform and Support Vector Machine

Application of Fuzzy Mathematics in Real Estate Valuation

Improved Support Vector Machine Multi-classification Algorithm

Exploration the Teaching Method of Linear Algebra Based on Geometric Thought