Research On Intellegent Learning Of Small Sample Data Of Project Cost And Its Application For Power Project

With the development of market economy, especially implementation of bidding system, traditional budget making system mainly based on quotas can not meet the need of project construction, in order to solve such question, it is necessary to introduce estimation method of project cost based on history project datas which is widely used in foreign contries. Because history datas are difficult to be collected, in most case, they are small samples, so cost estimation based on history project datas is essentially the question of small samples learning which is more difficult than massive datas learning, classical statistical methods based on hyposesis of infinite samples can not be used to solve such question. In the recent twenty years, theories such as fuzzy mathematics, grey correlation and neural network were widely used for research on cost estimation, but algorithms and models according to fuzzy mathematics and grey correlation theory were too simple, meawhile convergence,robust and generalization ability of neural network theory was poor, they can not meet the need of practical application. Support vector machine (SVM) technique is the best choice for research on small samples learning. In this paper, other artificial intelligence theories such as particle swarm optimization algorithm and clustering technique are used to improve regression SVM , a kind of intellegent learning modified algorithms of small sample based on parameter optimization regression SVM is proposed, meanwhile such modified learning algorithms is used for rapid estimation of project cost.Firstly, per-processing technique of small sample data is studied, combined with specific characteristics of history project data, a kind of specific small sample data per-processing method which include data cleaning, data conversion and data reduction is proposed, moreover, an illustrative example of transimission line projects proving the efficiency of the method is given. Secondly, new concepts named repulsive velocity and auto-tuning territory are introduced to improve particle swarm optimization algorithm, Self-adaptive multi-grouped PSO (abbr. SAMPSO) is proposed, its mathematical model and work flow are given, which successfully solve optimization problem of multi-modal function, optimization efficiency of SAMPSO is higher than standard PSO for multi-modal function, specially all local optimal points of multi-modal functions can be founded out by SAMPSO. Thirdly, SAMPSO is used to improve the performance of clustering of small sample data, two stages clustering modified algorithm is proposed, its mathematical model , work flow and an illustrative example of transimission line projects proving the efficiency of modified algorithm are given, Simulation results show that efficiency of such modified algorithm is better than conventional clustering algorithm such as FCM. New modified algorithm powerfully improve the performance of SVM clustering and regression on convergence , robust and exactness. Fourthly, SAMPSO and GA algorithms are used for parameter optimization of SVM at the same time, simulation results show that SAMPSO has better performance than GA, meanwhile nonlinear kernel principal component analysis (abbr. KPCA) is used to process small sample data, based on SVM and KPCA, a regression SVM modified algorithm(Prameters Optimization Support Vector Machine Algorithm, abbr. POSVMA) is proposed, its mathematical model and work flow are given, simulation results of transimission line projects by POSVM and SVM are compared to verify the efficiency of modified algorithm . At last, using above research achievements, based on POSVMA, a rapid estimation method of project cost is presented, its work flow is given. Simulation results of transimission line projects and power transformation projects show that such method can basically meet the practical need of management and control of project cost.