Dairy cow feed intake evaluation method based on genetic algorithm optimized BP neural network

Abstract

The invention discloses a dairy cow feed intake evaluation method for optimizing a BP neural network based on a genetic algorithm. The method comprises the following steps: 1, determining a topological structure of the BP neural network; 2, preprocessing sample data; 3, initializing a population; 4, calculating a population fitness value; 5, selecting, crossing and mutating; 6, judging whether evolution is completed or not; 7, constructing a model; 8, verifying the model. According to the method, a feed intake evaluation model is established by adopting a method of optimizing a BP neural network weight threshold by adopting a genetic algorithm, and scientific basis and theoretical guidance are provided for accurately evaluating the change of the feed intake of dairy cows and reasonably controlling the change rule of the feed intake of the dairy cows. According to the method, the defect that the BP neural network may fall into local optimum is overcome, the convergence rate of the modelis improved, and a relatively high evaluation effect is achieved.

Description

technical field [0001] The invention relates to a method for evaluating the feed intake of dairy cows, in particular to a method for evaluating the feed intake of dairy cows based on genetic algorithm optimization of BP neural network. Background technique [0002] Dairy farming is a traditional animal husbandry industry in my country, and it accounts for a relatively large proportion of the animal husbandry industry. Therefore, how to improve the economic benefits of my country's dairy farming industry and obtain a better input-output ratio is an urgent problem to be solved at present. Among them, the dry matter intake in the dairy farming industry is the quantitative basis for the nutrients required for the healthy production of dairy cows, an important indicator to measure the production performance and potential of dairy cows, and an important indicator to determine the level of dairy cow production, obtain higher economic benefits, and promote dairy farming. However, it...

AI Technical Summary

Problems solved by technology

The evaluation of feed intake of dairy cows in this study belongs to the process description of multi-input and single output, and there is a relatively complex internal relationship between various influencing factors and feed intake. Therefore, the BP neural network model can be used to evaluate the feed intake of dairy cows. However, in practical applications, a single BP neural network model is prone to fall into local optimum, which makes the prediction accuracy relatively low. Performance, such as: Song Zhiqiang et al. used the firefly algorithm to optimize the BP neural network modeling in the prediction of plant vibration induced by unit vibration and obtained satisfactory prediction results

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