To find a neural network model suitable to identify the concentration of mixed pernicious gases in pig house, the quantitative detection model of pernicious gases in pig house was set up based on BP （ Back propagation） neural network. The BP neural network was trained separately by the three functions, trainbr, traingdm and trainlm, in order to identify the concentration of mixed pernicious gases composed of ammonia gas and hepatic gas. The neural network toolbox in MATLAB software was used to simulate the detection. The results showed that the neural network trained by trainbr function has high average identification accuracy and faster detection speed, and it is also insensitive to noise; therefore, it is suitable to identify the concentration of pemidous gases in pig house. These data provide a reference for intelligent monitoring of pemicious gases in pigsty.
The sustainable development of grassland resources objectively requires that the social, economic and ecological costs of grassland resources should be brought into economic activities in order to internalize the external costs of grassland resources. Eco-tax is a very effective means to internalize the externality of grassland resources. Its main advantage is to bdng the cost of ecological environment into economic life, which can amend market pdce, improve the effectiveness of government policies, perform the eco-saboteurs-pays principle, and thus perfect the ecological compensation system.
