Continuous controlling of water vital factor for aquatic farming: Fuzzy Expert System Approach

Expert Systems (ES) are a part of Artificial Intelligence (AI), and were developed by the AI community in ۱۹۶۰. The main idea behind ES is expertise, which is transferred from a human to a computer. First this knowledge is stored in the computer, after that computer make decision based on an especial algorithm, database, and knowledge database. The computer can make inferences and gives advices and explains like a human consultant. ESs provide powerful tools for obtaining solutions to a variety of problems that often cannot be dealt with traditional methods. Therefore, we need it for social life and industry where their application is very vital in the trend of problem solving and decision support. We often encounter uncertain data. Hence, we need a method that can improve our decision. Fuzzy ESs are developed using the method of fuzzy logic. By using of fuzzy logic, process of normal human reasoning is simulated less precisely, logically, and less fault than conventional methods. This method is used because decision-making is not always a zero and one, black and white; it often involves gray areas. In this paper, we use fuzzy expert system for controlling of fish farming. Controlling of vital parameter of water for breeding of fish is important, because aquatics are very sensitive to changes in vital water parameters. In the flowchart below, we introduce a complete cycle of continuous control of fish farming. As it is shown in figure, first, the electronic device measures some water parameters such as PH, temperature, oxygen, transparency, wind speed, etc. using equipped sensors on it. Thereafter, the data are processed by fuzzy expert system. In this point, the data should be stored based on their features. Not only are these data processed and used for making decision, but they also will be utilized for further decision making. The processing section analyses the data based on fuzzy logic after receiving them online. We designed an algorithm which analyses the data, and as a result, we will have an output called an “eligible index.” This output will be sent to the action section.