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NNDemo2.0是对神经网络实验演示系统NNDemo1.0的进一步增强与扩充， 可以形象的演示各种网络模型的一些应用或性质，其中仿真例题是《人工 神经网络原理及仿真实例》教材中所有的例题。该系统使用极其简便，即使 你对各种网络模型不是很深刻的了解，也可以很好的使用该系统。使用时， 你可以自己修改网络的各种参数，交互性较好，而且该系统通过大量的图示 及参数设置，可以让你了解每个应用实例实现的过程及详细步骤。如果你想 学习神经网络的Matlab仿真（编程），进一步提高自己的编程能力，下面的 文件说明可以帮助你很快的找到系统的每个源文件。 (NNDemo2.0 is a neural network system NNDemo1.0 experimental demonstration of the further enhanced and expanded)

梁单元动力反应程序，包括各种动力计算方法和频响分析(Beam element dynamic response procedures, including frequency response analysis and dynamic calculation methods)

d-fnn动态模糊神经网络程序，可用于自学，探讨。(d-fnn program)

最佳升余弦滚降系统的实现及其误码率，比较适合初学者学习，可以更好的理解matlab的编程及其使用(Raised cosine roll-off the best system and the bit error rate, more suitable for beginners to learn, you can better understand the programming and the use of matlab)

说明：  用积分方程法求P波波场，适合初学积分方程研究散射问题(Integral Equation Method P wave field)

线性感知机算法，并已给出数据和结果，代码有注释哈(Linear perceptron algorithm, and has presented data and results, code annotated Kazakhstan)

说明：  用MATLAB仿真KL变换，并与DCT变换进行了比较(KL transform using MATLAB simulation and a comparison with the DCT transform)

matlab gui的基本应用代码，可以让你对GUI的功能有基本的了解(matlab gui application code that allows you to have a basic understanding of the functionality of the GUI)

In order to minimize power losses caused by high current and improve the voltage profile in the network distribution, the introduction of dispersed generations also called productions decentralized in distribution network (DG) plays an important role. The installation of DGs directly affects the power requested or purchased. The sizing and placement of DGs in the system must be optimal because a wrong choice has a negative impact on the system behavior. To solve this combinatorial problem, an algorithm known as Firefly algorithm is proposed in this paper. This is a meta-heuristic algorithm inspired by the behavior of fireflies flashing. The main objective of firefly flash is to act as a signaling system to attract other fireflies. Networks tested IEEE69-bus and IEEE33-bus are used to evaluate the effectiveness of this method. The results are compared with those obtained by genetic algorithm (GA) to IEEE69-bus, and Shuffled frog leaping algorithm (SFLA) for IEEE 33-bus.

这一段代码是基于matlab的仿真编程，主要是针对16QAM的调制，代码及其仿真结果无不从各个方面详细分析了技术原理。(This piece of code is based on the Matlab simulation programming for 16QAM modulation, code and its simulation results are all from the various aspects of a detailed analysis of the technical principles.)