A novel technique for Rule Advancement in fuzzy Expert System using Einstein Sum

The requirement of user changes according to region wise. But traditional Expert System contains static rules; hence these static rules can´t be updated according to user & region requirement. An enhanced approach namely Rule Advancement Strategy using Einstein Sum is proposed and implemented. This approach enables the Expert System´s inference drawing ability with enhanced intelligence. After using this approach in expert system the priority of rules changes according to region and user requirement. A new algorithm using Rule Advancement Strategy is reported and implemented in Expert System. The Knowledgebase of expert system is rich in knowledge. The Knowledgebase has two types of data. One is static data and second is dynamic data. Static data can´t be changed but dynamic data change according to region and user requirement. Knowledge Base contains the textual as well as pictorial information of Symptoms and Disease which can help to better understanding about the Disease and Symptoms. At last results are compared with existing technique called Rule Promotion Methodology. The Expert System using Rule Advancement Strategy is found more intelligent than the existing technique. The results of this expert system are also verified by the Domain Expert. Using this technique an online fuzzy Expert System is implemented using PHP as front end and MYSQL as back end in horticulture domain.