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The Bayesian Network in the Agricultural Experts Within Application Systems Research

Author: ZuoYongXian
Tutor: LiuWeiYi；YuShanShan
School: Yunnan University
Course: Computer technology
Keywords: Bayesian Network Build Bayesian network Reasoning Agricultural Expert System Shabu hot
CLC: S126
Type: Master's thesis
Year: 2010
Downloads: 87
Quote: 0
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Abstract


Agricultural expert system used in agricultural production, is of great significance to improve the efficiency of agricultural production, the promotion of agricultural production knowledge to achieve agricultural modernization. Knowledge in the field of agriculture in general with uncertainty, in order to establish an efficient agricultural expert system, this paper uses a Bayesian Network to represent and deal with uncertain knowledge. So compared with the early rule-based approach, a clearer semantics can be bi-directional reasoning, to facilitate quick commissioning and Reconstruction. This paper describes and explore the creation of two key issues involved in the agricultural expert system based on Bayesian network: First, the knowledge base of (domain knowledge to establish the appropriate Bayesian network model); inference engine algorithm achieve Bayesian network inference algorithm implementation. In view of the practical application, the paper discusses the agricultural expert systems based on Bayesian network is constructed, how to establish the conditional probability tables for each node in the Bayesian network structure and network. 'Building a network in order to reduce the number of conditional probability tables, separation technology, try to make a simple structure, thereby reducing the complexity of the problem. In the calculation of conditional probability tables, \Inference algorithm, this paper introduces and cutting conditions set algorithm in the Gibbs sampling algorithm in approximate reasoning algorithms and precise reasoning and decision-making network on the basis of the Bayesian network. The application of the decision-making network enhances the function of the system of agricultural experts. Finally, the agricultural expert system based on Bayesian network: rinse spicy plant production forecast, rinse spicy Decision network and rinse spicy Diseases Diagnosis Expert System. Rinse spicy planting can maximize the return on what measures can be taken by the decision-making network. The results obtained by the results and experts in the field of operation of the system is basically the same. This shows that the use of Bayesian Network to represent and deal with uncertain knowledge in the field of agriculture is very effective. The main contribution of this paper is as follows: ● For the agricultural expert system applied research at home and abroad has been made a lot of achievements. But for uncertain knowledge representation and processing is a rule-based approach. If only this single method to deal with the agriculture rich and complex uncertain knowledge, is far short of needs. Based on the lack of processing, we use Bayesian network to dealing with uncertain knowledge, it can improve the accuracy of the reasoning of the expert system and expand the range of uncertainty knowledge representation. ● Bayesian network is an excellent tool for dealing with uncertain knowledge. The article describes the application of Bayesian network and processing uncertain knowledge, and an explanation of the syntax, semantics and properties of Bayesian network. Bayesian network has three major research areas:, reasoning and learning in these areas has made some achievements, but there are still a lot of work needs to be done. The author has done some work in the Bayesian network representation and reasoning algorithms. Involved in the key issues for the establishment of an expert system based on Bayesian network, this paper Bayesian network, separation technology, the local structure of existing mechanisms and conversion uncertainty factor Knowledge Base and other technical simplify Bayesian network, the Bayesian network is applied to the agricultural expert system has received very good results. Introduced in the field of inference algorithm and the the approximate reasoning Gibbs sampling algorithm, the precise reasoning cut set of conditions (in cutset conditioning) algorithm are introduced and applied. And given based the inference algorithm establishment of a network of decision-making methods and procedures. On this basis, developed based on Bayesian network rinse spicy planting production forecasts, rinse spicy decision-making network and rinse spicy pest and disease diagnosis expert system.

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CLC: > Agricultural Sciences > Agriculture as the foundation of science > Agricultural physics > Electronic technology, computer technology in agriculture
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