Abstract:

In order to enhance the adaptation effect of ship maintenance cost case, an improved ship maintenance cost case adaptation model is proposed according to the modeling process of producing adaptation knowledge, reducing adaptation knowledge and reasoning adaptation knowledge. In this model, the ship maintenance cost case adaptation knowledge is produced by the direct similarity sets and indirect similarity sets of target cases based on the Policastro’s format; towards the problems that correlation and redundancy exist in the above adaptation knowledge, or the knowledge is too large to deal with, an improved rule reduction method is put forward which can both reduce the above knowledge effectively and measure their importance accurately, in addition, the algorithm for rule reduction is given; in order to improve the performance of knowledge reasoning, least square support vector machine (LSSVM) which has strong generalization power and perfect learning power is used to establish the ship maintenance cost case adaptation knowledge reasoning model, and the parameters of model are optimized according to genetic algorithm (GA) and crossvalidation method. Example analysis reveals that, compared with other case adaptation models, the adaptation accuracy of the proposed model is far better, so the proposed model is scientific and valid.