在基于BP神经网络生成纹理图象方法^[1]的基础上，提出了一种基于Logistic映射和多层前向神经网络的纹理图象生成方法，该方法使用Logistic映射来调整多层前向神经网络的网络参数，即用Logistic映射产生一组混沌变量，这组混沌变量中的每一个数对应一个需要调整的神经网络参数，由于Logistic映射具有的混沌特性，使多层前向神经网络每次迭代都会产生一组不同的参数，从而克服了使用BP算法调整神经网络参数时容易收敛的缺点，这种基于混沌映射的方法既保留了基于BP神经网络生成纹理图象方法的优点，又对其进行了改进。该方法因不需要计算网络的误差，从而大大简化了计算过程，并且可以产生比使用原有方法更加丰富的纹理图象，仿真结果表明，使用这种改进后的方法比原有的方法更加简单有效。

After sudying the method of generating texture image based on BP neural networks in, an improved method based on Logistic mapping and multi-layer forward neural networks is proposed. With the aid of using Logistic mapping, the parameters of multi-layer forward neural networks are adjusted by the new method. That is, using Logistic mapping generat for generating a set of chaotic variables, and each chaotic variable corresponding to a parameter of neural networks which need to be adjusted. Since Logistic mapping has the characteristic of chaos, which make the multi-layer forward neural networks defrent parameters, the defects of BP algorithm which lead the neural networks to converging is overcome. The method based on chaotic mapping not only ratain the virtue of the method in but also improved the original method. The improved method need not calculate the error of neural networks, so not only the operation proceeding is simple but also more texture images is able to be generated. Simulations show that the improved method is simpler and more efficiency the original method in .