Based on expatiated the basic structure model and some general improved algorithms of BP neural network, this paper brings forward a new self-organization learning algorithm.
介绍了BP网络的基本结构模型与常见改进算法，在此基础上提出了一种新型的结构自组织BP网络算法。
The application shows that the algorithms simplify the computing complexity of process neural networks, and raise the efficiency of the network learning and the adaptability to real problem resolving.
应用表明，算法简化了过程神经网络的计算复杂度，提高了网络学习效率和对实际问题求解的适应性。
Mostly used methods are introduced in detail, including fuzzy method, rough sets theory, cloud theory, evidence theory, artificial neural networks, genetic algorithms and induction learning.
详细介绍了数据挖掘技术的常用方法，包括模糊理论、粗糙集理论、云理论、证据理论、人工神经网络、遗传算法以及归纳学习。
Based on the established model, some associative pattern learning algorithms were put forward, which had the physiological features of structure's self-organizing and neural circuits' reverberating.
研究了基于所建模型的联想式模式学习算法。该算法模型体现了结构自组织、神经回路反响谐振等生理学特征。
Now I have done some basic reading on supervised and unsupervised learning algorithms such as decision trees, clustering, neural networks... etc.
现在我已经做了对的监督和无监督学习算法，如决策树，一些基本的阅读聚类，神经网络等。
This paper presents a class adaptive pole assignment control of servo systems based on neural state estimation and develops the system structure and the weight learning algorithms.
提出了一类基于神经元状态估计器的自适应广义极点配置控制，研究了该控制系统的网络结构和权值学习方法。
This algorithms has been widely used in machine learning, artificial intelligence, adaptive control, artificial neural network training, Image processing, among other areas.
目前这类算法已被广泛应用于机器学习，人工智能，自适应控制，人工神经网络训练，图像处理等各个方面。
There are still algorithms that could just as easily fit into multiple categories like Learning Vector Quantization that is both a neural network inspired method and an instance-based method.
仍然有些算法很容易就可以被归入好几个类别，好比学习矢量量化，它既是受启发于神经网络的方法，又是基于实例的方法。
The field goes by many names, such as connectionism, parallel distributed processing, neuro-computing, natural intelligent systems, machine learning algorithms, and artificial neural networks.
这个领域 有很多术语，例如连接机制、并行分布处理、神经计算、自然智能系统、机器学习算法和人工神经网络。
In addition, the paper makes use of Genetic Algorithms to optimize learning rates and inertia coefficients of Fuzzy-neural network, which can ensure that the controller achieves optimization control.
此外，通过遗传算法对模糊神经网络的学习速率和惯性系数等进行了优化，为控制系统实现最优控制提供了有力保证。
Neural network competitive learning algorithms are widely used for vector quantization. In this paper, some typical competitive learning algorithms have been specially investigated and analyzed .
对典型的竞争学习算法进行了研究和分析，提出了一种基于神经元获胜概率的概率敏感竞争学习算法（PSCL）。
The neural networks structure design, learning samples and training algorithms are expounded.
阐明了神经网络状态选择器的结构设计、样本选取及训练方法。
Their success sparked a renewed interest in learning AI methods such as decision trees, neural networks, genetic algorithms, and probabilistic methods.
这些游戏的成功从新燃起了对游戏ai方法的热情，比如：决定树，神经元网络，遗传算法，和盖然论。
Their success sparked a renewed interest in learning AI methods such as decision trees, neural networks, genetic algorithms, and probabilistic methods.
这些游戏的成功从新燃起了对游戏ai方法的热情，比如：决定树，神经元网络，遗传算法，和盖然论。
应用推荐