This paper presents a neural networks learning algorithm in long term optimization of hydropower station.
提出一种水电站长期优化调度的神经元网络方法。
The LEA decision method has been given so that grads methods and Newton methods can be effectively combined in neural networks learning.
在该系统中采用基于LEA判别法的梯度牛顿有效结合神经网络快速学习方法。
The lea decision method has been given so that grads methods and Newton methods can be effectively combined in neural networks learning. Experimental results are also shown.
介绍一种LEA判别法，实现梯度牛顿有效结合神经网络快速学习方法，并给出了实验结果。
This sort of learning could take place with neural networks or support vector machines, but another approach is to use decision trees.
这种学习可以使用神经网络或者支持向量机，不过用决策树也可以实现类似的功能。
Supervised learning is the most common technique for training neural networks and decision trees.
监督学习是训练神经网络和决策树的最常见技术。
This type of learning could probably be carried out with neural networks, though it is hard to imagine that the problem is simple enough for decision trees.
这种类型的学习通常交给神经网络来完成，虽然很难想象，但用决策树来完成这类问题也很简单。
Learning requires the brain to create new neural networks.
学习需要大脑去创造神经网络。
It is interesting to note that Neural Networks is an evolution of learning-oriented estimation, in which the method algorithm is trained to behave like a human expert.
有意思的是，神经网络是一种对学习型评估的进化，算法经过训练后其行为就像是一个人类专家。
This book will teach you many of the core concepts behind neural networks and deep learning.
这本书将告诉你许多神经网络与深度学习后面的核心概念。
Because that wavelet transform can effectively extract the characters, the Adaptive Resonance Theory (ART) Neural Networks has a good learning ability.
由于小波变换能有效地提取字符的结构特征，自适应共振(art)网络有很好的学习能力。
After working through the book you will have written code that USES neural networks and deep learning to solve complex pattern recognition problems.
在完成本书的学习后，你将可以编写代码来使用神经网络和深度学习来解决复杂的模式识别问题。
Since these variables are characterized as nonlinearities time series data, Artificial Neural networks (ANN) will be employed using back propagation algorithm as learning algorithm.
由于这些变量具有非线性时间序列数据，用人工神经网络(ANN)将使用反向传播算法作为学习算法。
What's more, we'll improve the program through many iterations, gradually incorporating more and more of the core ideas about neural networks and deep learning.
更进一步，我们将通过多次迭代来提升这个程序的效果，逐渐触及越来越多神经网络与深度学习的核心概念。
The purpose of this book is to help you master the core concepts of neural networks, including modern techniques for deep learning.
这本书的目标是帮助你掌握神经网络的核心概念，包括深度学习的前沿技术。
And you will have a foundation to use neural networks and deep learning to attack problems of your own devising.
并且你将拥有使用神经网络和深度学习来解决你自己发现的问题的基础。
It has important theoretical significance and application value how to find an effective learning algorithm of RBF neural networks.
如何找到一种更加行之有效的RBF神经网络学习算法具有重要的理论意义和应用价值。
We'll learn the core principles behind neural networks and deep learning by attacking a concrete problem: the problem of teaching a computer to recognize handwritten digits.
我们通过解决一个具体的问题：交计算机识别手写数字，来学习神经网络与深度学习后面的核心理念。
Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing.
神经网络与深度学习现在为解决许多问题提供了最佳解决方案，例如图像识别、语音识别和自然语言分析。
Deep learning refers to the method of training multi-layer artificial neural networks.
深度学习是指训练多层的人工神经网络的方法。
In essence, learning in artificial neural networks is an optimization process, that is, an artificial network adjusts the weights of the network on its concrete error information.
从本质上讲，人工神经网络的学习过程是一个优化的过程，即根据具体的误差信息来合理地选择网络的权重。
In consideration of the complexity of the aggregation operation of time in process neural networks, a new learning algorithm based on function orthogonal basis expansion is proposed.
该文在考虑过程神经网络对时间聚合运算的复杂性的基础上，提出了一种基于函数正交基展开的学习算法。
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。
In this paper, the dynamic behaviors of continuous neural networks under structural variations in learning process are studied.
本文研究了连续神经网络在学习过程中结构摄动情况下网络的动态特性。
Research on local path planning of mobile robot based on Q reinforcement learning and CMAC neural networks.
基于Q强化学习与CMAC神经网络的移动机器人局部路径规划研究。
A two-stage learning scheme for neural networks is proposed in this paper.
一种两阶段学习方案被提出用于神经网络的训练。
Aiming at dynamic model uncertainties and load disturbances of robot manipulators, an iterative learning control scheme using neural networks is presented.
针对机器人动力学模型的不确定性和负载扰动，提出了一种采用神经网络的机器人迭代学习控制方法。
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。
Using identification of neural networks, a new method of robust iterative learning control algorithm is proposed in the paper.
在神经网络辨识的基础上，提出一种新的鲁棒迭代学习控制方法。
Using identification of neural networks, a new method of robust iterative learning control algorithm is proposed in the paper.
在神经网络辨识的基础上，提出一种新的鲁棒迭代学习控制方法。
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