摘要
针对前馈神经网络的断路故障 ,将网络容错性的改善转化为一个最小优化问题 ,并通过遗传算法进化求解来获取容错性好、泛化能力强的网络 .该方法不需给网络增加额外冗余 ,也不需修改网络训练算法 ,较好地保持了网络结构、训练算法与容错处理的独立性 .实验表明 。
The problem of improving the fault-tolerance of feedforward neural networks that suffers open fault is transferred to a minimum optimization problem, and a genetic algorithm is used to evolve networks that have good fault-tolerance and strong generalization ability. Since neither extra network redundancy is introduced nor training algorithm is modified, the independence of the network architecture, training algorithm, and fault-tolerant process are well kept. Experiments show that this method achieves good results in two benchmark tests.
出处
《计算机研究与发展》
EI
CSCD
北大核心
2001年第9期1061-1065,共5页
Journal of Computer Research and Development
基金
国家自然科学基金 ( 6 9875 0 0 6 )
江苏省自然科学基金 ( BK990 36 )资助
关键词
遗传算法
进化计算
前馈神经网络
容错性
neural networks, fault tolerance, genetic algorithms, evolutionary computation, optimization