algorithm level meaning in Chinese
算法级
Examples
- In this paper the reasons for these drawbacks and the methods for overcoming these drawbacks are systemically studied from two levels , algorithm level and computing theory level
本文从算法层和计算理论层两个层次对造成这些缺陷的原因和克服这些缺陷的方法进行了系统的研究。 - The proposed 64 bits high performance alu is optimized at algorithm level , logic level , circuit level and layout level , and is implemented in 0 . 18 m cmos process . furthermore , the testing technique of the alu is discussed . this thesis mainly contributes to the following aspect : 1
文章从部件的算法、逻辑结构、电路参数、物理版图等多个层次进行设计优化,在0 . 18 mcmos工艺下实现了一款64位高性能算术逻辑部件,并对该部件的测试方法进行研究。 - The studies indicate that the algorithm level only deals with getting over the former two drawbacks of neural network learning using advanced optimization algorithms in the intrinsic framework of neural network , and great breakthrough is hard to made because of the limit of current optimization theory
这一层次的研究表明，算法层只是在原有神经网络的框架下利用高性能的优化算法克服网络学习的前两个缺陷，由于受目前优化理论的限制，很难有巨大的突破。 - In the algorithm level , currently various training algorithms of neural networks , including gradient algorithms , intelligent learning algorithms and hybrid algorithms , are comparatively studied ; the optimization principle of bp algorithm for neural networks training is analyzed in detail , and the reasons for serious disadvantages of bp algorithms are found out , moreover , the optimization principle of two kinds of improved bp algorithms is described in a uniform theoretic framework ; and the global optimization algorithms of neural networks , mainly genetic algorithm are expounded in detail , it follows that a improved genetic algorithm is proposed ; finally the training performances of various algorithms are compared based on a simulation experiment on a benchmark problem of neural network learning , furthermore , a viewpoint that genetic algorithm is subject to " curse of dimension " is proposed
在算法层，本文对目前用于神经网络训练的各种算法，包括梯度算法、智能学习算法和混合学习算法进行了比较研究；对用于神经网络训练的bp算法的优化原理进行了详细的理论分析，找到了bp算法存在严重缺陷的原因，并对其两类改进算法-启发式算法和二次梯度算法的优化原理，在统一的框架之下进行了详尽的理论描述；对神经网络全局优化算法主要是遗传算法进行了详细的阐述，并在此基础上，设计了一种性能改进的遗传算法；最后基于神经网络学习的benchmark问题对各种算法在网络训练中的应用性能进行了仿真研究，并提出了遗传算法受困于“维数灾难”的观点。