在分析指挥控制系统通信网络特点的基础上，采用面向对象的方法和Visual C++ 6.0程序设计语言开发了一个智能故障诊断专家系统，并对该系统的结构和各模块功能进行了阐述。该系统采用神经网络技术对网络告警数据进行处理，并利用专家系...在分析指挥控制系统通信网络特点的基础上，采用面向对象的方法和Visual C++ 6.0程序设计语言开发了一个智能故障诊断专家系统，并对该系统的结构和各模块功能进行了阐述。该系统采用神经网络技术对网络告警数据进行处理，并利用专家系统实现诊断，提高了系统抑制噪声的能力，同时使系统具备了自学习功能。展开更多
By combining fractal theory with D-S evidence theory, an algorithm based on the fusion of multi-fractal features is presented. Fractal features are extracted, and basic probability assignment function is designed. Com...By combining fractal theory with D-S evidence theory, an algorithm based on the fusion of multi-fractal features is presented. Fractal features are extracted, and basic probability assignment function is designed. Comparison and simulation are performed on the new algorithm, the old algorithm based on single feature and the algorithm based on neural network. Results of the comparison and simulation illustrate that the new algorithm is feasible and valid.展开更多
Knowledge acquisition is the “bottleneck” of building an expert system. Based on the optimization model, an improved genetic algorithm applied to knowledge acquisition of a network fault diagnostic expert system is ...Knowledge acquisition is the “bottleneck” of building an expert system. Based on the optimization model, an improved genetic algorithm applied to knowledge acquisition of a network fault diagnostic expert system is proposed. The algorithm applies operators such as selection, crossover and mutation to evolve an initial population of diagnostic rules. Especially, a self adaptive method is put forward to regulate the crossover rate and mutation rate. In the end, a knowledge acquisition problem of a simple network fault diagnostic system is simulated, the results of simulation show that the improved approach can solve the problem of convergence better.展开更多
文摘By combining fractal theory with D-S evidence theory, an algorithm based on the fusion of multi-fractal features is presented. Fractal features are extracted, and basic probability assignment function is designed. Comparison and simulation are performed on the new algorithm, the old algorithm based on single feature and the algorithm based on neural network. Results of the comparison and simulation illustrate that the new algorithm is feasible and valid.
文摘Knowledge acquisition is the “bottleneck” of building an expert system. Based on the optimization model, an improved genetic algorithm applied to knowledge acquisition of a network fault diagnostic expert system is proposed. The algorithm applies operators such as selection, crossover and mutation to evolve an initial population of diagnostic rules. Especially, a self adaptive method is put forward to regulate the crossover rate and mutation rate. In the end, a knowledge acquisition problem of a simple network fault diagnostic system is simulated, the results of simulation show that the improved approach can solve the problem of convergence better.
