脉冲暂态混沌神经网络 (PTCNN)是对暂态混沌神经网络的改进 ,呈现丰富的动力学性质 ,具有很强的跳出局部最小点的功能 ,在解决无约束非线性规划问题时 ,可以找到包括全局和局部最小值的尽量全面的最优解。当遇到带约束条件的非线性规划问题时 ,只有对约束条件进行合理处理 ,才能更有效地解决约束非线性规划问题。文章使用惩罚函数方法对含有约束条件的非线性规划问题进行处理 ,将其变成一个不含约束条件的非线性规划问题 ,进而用PTCNN求解 ,得到了令人满意的结果 Impulsive transient chaotic neural network (PTCNN) is an improvement of transient chaotic neural network, showing a wealth of dynamic properties, has a strong ability to jump out of the local minimum, in solving unconstrained nonlinear programming problems, you can find include Global and local minimum as comprehensive as possible optimal solution. When encountering the nonlinear programming problem with constraints, only constrained conditions can be dealt with more effectively to solve the constrained nonlinear programming problem. In this paper, we use the penalty function method to deal with nonlinear programming problems with constraints and transform it into a non-linear programming problem without constraints, and then use PTCNN to solve the problem and get satisfactory results