A Modified Support Vector Machine And Its Application In Anti-analysis Of Geoengineering

Support Vector Machine is a kind of new machine studying method, which is based on Statistical Learning Theory. Because it has quite perfect theoretical properties and good learning performance, and can solve some practical problems such as a little sample, non-linear, high-dimension and part minimized value, SVM becomes the new research hotspot after the research of Artificial Nerve Net. However, SVM performance has been validated in many practical applications, there are still some drawbacks. For example: train speed is slow, algorithm is complex, SVM's implementation is efficient and it's adaptability to noises and outliers.Geomechanics is a subject with the deep theory and strong practice. Through a few decades, a lot of methods have been applied to solve the problems on geomechanics and elastic and plastic theory. But owing to rock and soil's features such as the complexity, non-linearity, randomty, uncertainty and obscurity, it is difficult to get satisfactory results by traditional ways. The development of the intelligent rock mechanics offers a new method. SVM is a new method of machine learning, it is fitful for the problem that can't be solved with traditional mathematical model. There is an extensive prospect in geotechnical engineering.In the solution of Geotechnical, it is intractable to ascertain parameter of rock and soil exactly. Back analysis is a good method to get parameter of rock. In this paper, the author applied SVM to geomechanics, and studied the parameter identification of rock and soil. The author's major works are as the following:1 The author briefly summarized some important conclusions of Statistic Learning Theory and discussed in detail the principle of SVM in pattern recognition.2 The author mainly discussed the problems such as the efficiency in SVM's implementation and it's adaptability to noises and outliers. Recent research on these problems and several algorithms are introduced and analyzed in the overview. According to the discussion, we proposed a modified SVM. Some experiments of its performance on artificial data are presented to show its feasibility and effectiveness.3 The author proposed a new method—support vector machine' based on simulated annealing algorithms, which combines the global optimization characteristic of simulated annealing algorithms and the nonlinear mapping characteristic of support vector machine. *4 The author applied the support vector machine to geoengineering, and proposed the simulated annealing—support vector machine method to recognize the parameter of rock and soil, a example prove this method is right.