With the development of the science and technology, the more requirements such as cost effective, high specific impulse in wide operation rang, becomes stricter and multiplicity. However, the existing supersonic inlet can no longer adjust to all the new projectiles. In this paper, based on the basic characteristic of inlet and considering the design requirements, the two-dimensional supersonic projectile inlet was designed and verified by numerical simulation under different operating conditions such as attack angle, altitude, and so on. The results are shown that: 1) The design process is successful, but the working conditions should be limited to the small angle of attack; 2) The total pressure recovery coefficient is increasing as the Ma number increases, and then is gradually decreased after the point of Mach number is equal to 0.5; 3) The existence of attack angle reduces values of total pressure recovery. And moreover, the shock wave which occurs at the anterior point is gradually deviating from projectile body direction with the increase of attack angle; 4). The variance ratio in the outlet has the acute changed with increasing of altitudes clearly, but its corresponding values degrade sharply in the entrance.

The finite element analysis in theory of elasticity is corresponded to the quadratic programming with equality constraint, which can be further transformed into the unconstrained optimization. In the paper, the neural network of finite element solving was obtained on the basis of Hopfield neural network that was reformed. And the no error solving of finite element neural net computation was realized in theory. And a design method to construct an artificial neuron by using electronic devices such as operational amplifier, digital controlled potentiometer and so on was presented. A programmable hardware neural network of finite element can be build up by using analog switches to interconnect inputs/outputs of hardware neurons. The weights, biases and connection in the hardware neural network of finite element can be adjusted automatically by microprocessor according to the results of train to controlling system, This programmable hardware neural network of finite element has some more adaptability for different systems. In addition, the authors present the computer simulation and analogue circuit experiment to verify this method. The results are revealed that: 1) The results of improved Hopfield neural network are reliable and accuracy; 2) The improved Hopfield neural network model has an advantage on circuit realization and the computing time, which is unrelated with complexity of the structure, is constant. It is practical significance for the research and calculation.