aoc_song

无线通信网络的发展趋势之一是将各种不同无线接入技术进行融合。多接入选择作为无线资源管理中的关键技术是目前异构网融合的研究热点之一。从用户的利益出发，充分考虑预接入网络的费用、QoS 和稳定性；假定不同的网络运营商之间是彼此竞争的，即没有达成统一的协议，提出了一种结合目标代价函数与层次分析法的动态多接入选择算法。通过对影响因素进行分析和二次判断，建立了网络选择决策模型，仿真结果验证了该算法的正确性与有效性。(Trends in wireless communication network is one of a variety of different wireless access technologies will be integrated. Multi-access radio resource management in selection as the key technology is the integration of heterogeneous networks of research focus. From the user' s interests fully into account the cost of pre-access networks, QoS and stability assumed between different network operators are competing with each other, that did not reach uniform agreement, proposed a combination of objective cost function and level analysis of dynamic multi-access selection algorithm. Through analysis of the factors affecting the judge and the second, the establishment of a network selection decision-making model, simulation results verify the correctness and validity of the algorithm.)

zigbee的一些基础知识，引脚及其相关协议等(basic zigbee )

关好电力规约IEC103的编程流程图.()

说明：  变频器使用手册 MM440使用手册 西门子通用变频器应用实例手册(Inverter Manual Manual Siemens MM440 Inverter Application Manual Universal)

AF，DF，CC,三种基本协作方式仿真程序(Simulation of three basic collaborative approach)

说明：  DL/T860系列文档完整版，IEC61850通讯模型(DL / T860 series document complete version, IEC61850 communication model)

A good book for C++ for beginners

混沌控制经典书籍--混沌控制及其优化应用-作者邹恩(Chaos control of classic books- Chaos control and optimization applications- Author Zou En)

仿真实现了关于认知无线电中合作频谱检测的性能分析(Simulation to achieve the performance of cooperative spectrum sensing in cognitive radio)

说明：  求解双层规划问题常用的算法有极点算法、直接搜索法、下降法和非数值优化方法(如模拟退火算法、遗传算法等)，遗传算法的求解思路是：首先对上层的决策变量编码，代人下层规划模型，通过求解下层模型的决策变量值，代入上层模型计算适应度值，然后进行交叉、变异、选择操作，最后求出最优解。(Solving Bilevel Programming Problems with pole algorithm commonly used algorithms, direct search method, descent and non-numerical optimization methods (such as simulated annealing, genetic algorithms, etc.), genetic algorithm for solving approaches: the first on the top of the decision variable coding, on behalf of the were lower programming model, by solving the lower model of decision variables values into the upper model on behalf of the fitness value, and then crossover and mutation, select Options, and finally get the best solution.)