Expert System （ES） is considered effective and efficient in agricultural production, as agricultural informationization becomes a main trend in agricultural development. ES, however, is applied unsatisfactorily in m...Expert System （ES） is considered effective and efficient in agricultural production, as agricultural informationization becomes a main trend in agricultural development. ES, however, is applied unsatisfactorily in most rural areas of China and it has considerably affected and restricted the development of the agricultural informationization. This paper proposed a solution to voice service system of ES, which was suitable for the information transmission, and it especially could help the peasants in remote regions obtain knowledge from ES through the voice service system. As for the disadvantages of massive knowledge data and slow deduction, in this system the classification method could be adopted based on the decision tree. Designing pruning algorithm to ＂trim off＂ the unrelated knowledge to the users in query course would simplify the structure of the decision tree and accelerate the speed of deduction before the inference engine deduced the knowledge required by users.展开更多
基金Supported by Northeast Agricultural University Doctoral Development FoundationChina Postdoctoral Science Foundation
文摘Expert System （ES） is considered effective and efficient in agricultural production, as agricultural informationization becomes a main trend in agricultural development. ES, however, is applied unsatisfactorily in most rural areas of China and it has considerably affected and restricted the development of the agricultural informationization. This paper proposed a solution to voice service system of ES, which was suitable for the information transmission, and it especially could help the peasants in remote regions obtain knowledge from ES through the voice service system. As for the disadvantages of massive knowledge data and slow deduction, in this system the classification method could be adopted based on the decision tree. Designing pruning algorithm to ＂trim off＂ the unrelated knowledge to the users in query course would simplify the structure of the decision tree and accelerate the speed of deduction before the inference engine deduced the knowledge required by users.
