The process of gas concentration control in animal hypoxia experiment is time varying and nonlinear，Combining BP neural network with traditional PID control can achieve better control results, but there are still some problems such as slow convergence speed and poor stability. To solve these problems, a new BP neural network PID controller, optimized by improved genetic algorithm, is proposed. The convergence speed and stability of genetic algorithm are improved in order to optimize the initial weights of BP neural network , then the optimized BP neural network was used to realize on line adjustment of PID parameters in this controller.Finally，the conventional and improved ontrollers are simulated in MATLAB，the results show that the improved BP neural network PID controller has better control performance, compared with the conventional BP neural network PID controller.
