摘要
随着电子技术的大规模发展,电路可靠性要求逐步提高,电路板测试诊断的重要性日益凸显;如何寻求最佳的测试节点或测试矢量集是电路的故障诊断中的重要问题,提出一种基于遗传粒子群优化算法对测试节点进行优化选择。该方法通过建立电路测试节点的"故障-测试"矩阵,用遗传算法对数学模型的进行优化,并采用粒子群算法搜索实现了快速求解;与传统方法相比较,该方法搜索速度快,优化效果明显,已在工程实践中得到应用。
With the large-scale development of electronic technology, the circuit gradually increase the reliability requirements and circuit board test highlights the growing importance of the diagnosis. How to find the best set of test nodes, or test vectors is a circuit fault diagnosis of important issues, and a kind of method based on genetic particle swarm optimization algorithm is presented to solve the test node optimization problem. It optimizes the test node＇s ＂fault-test＂ matrix by genetic algorithm and searched quickly by particle swarm optimization. Searching speed of the method is faster than traditional methods, and the optimal results are also better. The method has been used in engineering practice.
出处
《计算机测量与控制》
CSCD
北大核心
2010年第5期1036-1038,1045,共4页
Computer Measurement &Control
关键词
测试节点优选
遗传算法
粒子群算法
test nodes optimization
genetic algorithm
particle swarm optimization
