Research on Improved IPSO-LSSVM Method and Its Application

It adopts support vector machine which is applicable for small sample prediction and constructs the prediction model. It is based on analyzing characteristics of particle swarm optimization and support vector machine. The improved IPSO-LSSVM prediction model shall be used to predict the development cost of military excavator. The prediction result indicates that compared with traditional SVM algorithm and BP algorithm, the prediction model has a better small sample adaptability, a faster training velocity and a higher prediction accuracy and it is more applicable to predict the development cost of engineering equipment. Introduction The engineering equipment has a long preparation time, a difficult technology, and many uncertain factors. Many costs in the developing process are hard to get and related costs and data can be obtained after costs take place. A few effective training samples become a key problem in affecting the cost prediction. To solve above problems effectively, systematic and scientific analytical method needs to be adopted and the development cost of engineering equipment shall be predicted in accordance with historical data which is of typical significance. Because it is hard to collect abundant effective sample data and factors which affect the development cost of equipment are nonlinear, so it has limitations to predict the development cost of equipment using traditional parametric method [1]. Support vector machine has a strong ability to deal with nonlinear problems and it is applicable to small sample data. Therefore, the development cost of engineering equipment shall be predicted by using improved support vector machine. Prediction Model Based on Improved IPSO-LSSVM Adaptive Inertia Factor Strategy It can be seen that the initial stage of iteration determines global searching ability of algorithm, the final stage determines local searching ability [2], if search time of both ends can be lengthened, it is of great benefit to the improvement of algorithm performance, and the weight of the following can be adopted to self-adopt update method: ) ) / ( 20 exp( ) ( 6 max min max min t t w w w w       (1) max w , min w takes 0.9 and 0.1 separately. This method makes inertia weight keep a larger value for a long time at the beginning of iteration, it keeps a smaller value for a long time at the end of iteration, thus the global search time in early 644 Advances in Computer Science Research (ACRS), volume 54 International Conference on Computer Networks and Communication Technology (CNCT2016) iterations and the local search time in late iterations will be added, the intensity of global search in early iterations and the intensity of local search in late iterations will be strengthened, keeping global search ability and local search ability in good balance. Mutation Strategy With No Evolution Generation (1)Calculate fitness rate: fitness rate refers to fitness rate of historically optimal location of particle g p on this time iteration and M times iterations before this time (M is 1): )) ( ( )) ( ( )) ( ( t p f M t p f t p f FSlope g g g    (2) (2)Statistics of no evolutionary times: When the evolution begins, no evolutionary times stop time shall be 0, fitness change threshold Slope value (take 10e-10), no evolutionary threshold value of particles Max step and mutation probability m p (take 0.4). In the process of iteration, no evolutionary times of particles shall be decided by fitness rate, as shown below: 1 0 FSlope Slopevalue Stoptime stoptime FSlope Slopevalue Stoptime         (3) If no evolutionary times have exceeded set no evolutionary threshold value, the algorithm may be in a stage of stagnation, the population shall implement mutation operation according to mutation probability: ) ( () 5 . 0 ) ( ) ( t p rand t p t p gi gi gi     (4) The improvement make IPSO avoid phenomenon that it falls into local minimum and the training stops when training, when the training stagnates, algorithm makes the population particle jump out of local minimum point by mutation operation and keeps drawing close to global optimum point. LSSVM Deformation Strategy According to the method of transforming standard SVM into duality problem [3], LSSVM primal problem can be transformed into duality problem by derivation: