Construction method of fuzzy neural network expert system for water quality assessment in turbot culture

Abstract

The invention relates to a construction method of a fuzzy neural network expert system for the water quality assessment in turbot culture, which comprises neural network modeling and network model testing. The method is characterized in that the neural network modeling comprises the following steps of: setting network training parameters, wherein sensitive indexes of the growth of turbots after three-tiered classification, which include four expert data of temperature, salinity, pH and dissolved oxygen, are used as input parameters; determining the network topology: constructing a three-layer neural network by using the input layers of four input nodes, the hidden layers of two hidden nodes and the output layer of one output node; and determining training samples. The network model testing comprises the steps of: leading in tested samples and assessing the network model. The invention combines the neural network, the fuzzy system and the on-line monitoring system for the water quality assessment in industrial turbot culture for the first time, avoids the operations such as manual setting and the like in the traditional assessment method, and overcomes the influence of human factors and the like on the assessment in the traditional turbot water quality assessment.

Description

technical field [0001] The invention specifically relates to a method for industrially establishing a fuzzy neural network system for water quality evaluation of turbot aquaculture-a method for constructing a fuzzy neural network expert system for water quality evaluation of turbot aquaculture. Background technique [0002] Accurate evaluation of environmental quality is an important task in environmental protection and ecological civilization construction. Water environment quality assessment, referred to as water quality assessment, is a qualitative or quantitative description of the quality of water, so as to accurately reflect the current water quality and pollution status, clarify the law of water quality change and development, and find out the main pollution in the assessed area. It provides a basis for water pollution control, water function zoning, water environment planning and water environment management. The traditional water quality evaluation of turbot factor...

AI Technical Summary

Problems solved by technology

The disadvantages of this method are: the selection of water quality monitoring points is not representative; there are errors in the determination of water quality parameters; the evaluation workload of industry experts is large; the evaluation results are greatly affected by factors such as the subjective knowledge of industry experts, so they are obviously random and consistent poor sex

[0004] In short, the traditional expert system has the disadvantages of difficult knowledge acquisition, weak reasoning ability, low intelligence level, and poor practicability. Combining the expert system with the fuzzy neural network and giving full play to the advantages of the two makes the entire neural network a more intelligent expert. Systematic knowledge base is very necessary

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