The Study Of New Model Approach And Its Application To Quantitative Structure-activity Relationships

Modeling is to abstract qualitative relationships of variables involved in objects, and is an important means of recognizing and describing the behavior of objects under studying. Many techniques are involved in modeling methods such as optimization, statistics, artificial intelligence, pattern recognition, machine learning, neural network, and so on.Building a proper model is important to Quantitative structure-activity relationships(QSAR) research. In this paper, two new approach were proposed and used to QSAR modeling. The two mothod are EGA-RBF-CSR and EB-LSSVMPLS.Compared to the other models, This two models not only holds on fine learning ability but also gives better prediction performance and steady capability.This article includes the following parts mainly:(1) By changing the dimension of solution space, cyclic subspace regression(CSR)gets a serial of regression models which contains least square regression,principal component regression,partial least squares regression and other medial regression outcomes.Through the cyclic subspace regression(CSR) being applied to determine the weight of Radial Basis Functions(RBF) networks,the RBF-CSR model is produced. The approach has the merit of RBF-PLS,and it can select the optimal model in wider range.(2)A eugenic evolution strategy was proposed to improve the efficiency of the conventional simple genetic algorithm (SGA) searching. The Eugenic evolution genetic algorithm (EGA) collects the population information along the evolution of children generations and constructs a deterministic optimization algorithm, which will be embedded in the evolution process at appropriate stage to speed up the local searching. The EGA was successfully applied to optimize the RBF-CSR parameter and EGA-RBF-CSR model was proposed. Finally, EGA-RBF-CSR was applied successfully to modeling quantitative structure-activity relationships.(3)Research contributions and major problems in statistical learning theory study(SLT) and support vector machine are reviewed.Then least squares support vector machine(LSSVM) was introduced. LSSVM is a modified version of SVM.The quadratic programming in original support vector machines is replaced by the linear set of equations.The approach has the merit of SVM, and it can compute fastly and stably.(4)A new nonlinear partial least squares algorithm embedded least squares support vector machine (LSSVM) into the regression framework of partial least squares (PLS) method was proposed in this paper. In this approach, LSSVM is used to fit the nonlinear inner relations between PLS components, thus a multi-input multi-output nonlinear modeling task was decomposed into linear outer relations and simple nonlinear inner relations that were performed by a number of single-input single-output LSSVM models. By using the universal approximation property of LSSVM, the PLS modeling method is generalized to a non-linear framework. Subsequently, to increase PLS components interpretative capability, the error-based weights updating procedure in the PLS input outer model was deduced and implemented in the LSSVMPLS regression framework. Finally, the EB-LSSVMPLS is applied to modeling QSAR.Finally,a brief review of this thesis is given.Some future research directions are highlighted.