Description: 落煤残存瓦斯量的确定是采掘工作面瓦斯涌出量预测的重要环节,它直接影响着采掘工作面瓦斯涌出量预测的精度,并与煤的变质程度、落煤粒度、原始瓦斯含量、暴露时间等影响因素呈非线性关系。人工神经网络具有表示任意非线性关系和学习的能力,是解决复杂非线性、不确定性和时变性问题的新思想和新方法。基于此,作者提出自适应神经网络的落煤残存瓦斯量预测模型,并结合不同矿井落煤残存瓦斯量的实际测定结果进行验证研究。结果表明,自适应调整权值的变步长BP神经网络模型预测精度高,收敛速度快 该预测模型的应用可为采掘工作面瓦斯涌出量的动态预测提供可靠的基础数据,为采掘工作面落煤残存瓦斯量的确定提出了一种全新的方法和思路。-charged residual coal gas is to determine the volume of mining gas emission rate forecast an important link, which directly affect mining gas emission rate forecast accuracy, and with coal metamorphism, loading coal particle size, the original gas content, exposure time and other factors nonlinear relationship. Artificial neural networks have expressed arbitrary nonlinear relationships and the ability to solve complex nonlinear, time-varying uncertainty and the new ideas and new approaches. Based on this, the author of adaptive neural network loading coal residual gas production forecast model, and a combination of different loading coal mine gas remnants of the actual test results of research. Results show that the adaptive value of the right to change step BP neural network model predict Platform: |
Size: 60416 |
Author:王静 |
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Description: 一个很好用的自适应人工神经网络模型库，包括ADALINE/RBF/多层前向等，另外内含Demo，更易上手-A good use of adaptive artificial neural network model library, including the ADALINE/RBF/Multi-layer and so on, the other containing the Demo, even易上手 Platform: |
Size: 147456 |
Author:风影 |
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Description: 气象保障常用软件包:本书分为10章，包括客观分析、诊断分析、多元统计分析、主分量分析、自适应统计天气预报方法、时间序列分析、谱分析、人工神经网络天气预报方法、灰色系统及气象绘图软件等内容。 Fortran代码写在word文档中。-Commonly used meteorological package: This book is divided into 10 chapters, including an objective analysis, diagnostic analysis, multivariate statistical analysis, principal component analysis, adaptive statistical weather forecasting methods, time series analysis, spectral analysis, artificial neural network forecast method, the gray systems and meteorological elements, such as mapping software. Fortran code written on the word document. Platform: |
Size: 212992 |
Author:王哲 |
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Description: 由于人工神经网络具有大规模并行信息处理，良好的自适应与自学习
等许多特点，因此利用神经网络解决复杂非线性动态系统的预测问题就有了一条新的可能途径
-As massively parallel artificial neural network information processing, a good adaptive and self-learning and many other features, the use of neural networks to solve complex nonlinear dynamic system of prediction may have a new way Platform: |
Size: 3972096 |
Author:杨利明 |
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Description: 析了变速恒频双馈风力发电系统的工作原理。结合风力机特性和双馈发电机特性，说明了变速恒频运行方式下的最优风能捕获策略，并介绍了风力发电系统的滑模变结构控制、自适应控制、鲁棒控制和人工神经网络控制。总结了变速恒频风力发电系统的发展趋势。-Analysis of a Variable Speed Constant Frequency Wind Power Generation System works. Combination of wind turbine characteristics and the characteristics of doubly-fed generator, variable speed constant frequency shows the optimal operating mode to capture wind energy strategy, and introduced wind power generation system with variable structure control, adaptive control, robust control and artificial neural networks control. Summarizes the VSCF wind power system trends. Platform: |
Size: 429056 |
Author:wuxing |
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Description: 人工神经网络(Artificial Neural Network)是从生理角度对智能的模拟，具有极
高的学习能力和自适应能力，能够以任意精度逼近任意函数，完成对系统的仿真；
而遗传算法是对自然界生物进化过程的模拟，具有极强的全局寻优能力，这两种
算法都是当下研究较多的智能方法。将这两种方法与常规的 PID 控制相结合，
构成智能 PID 控制器，使其具有参数自整定、自适应的能力，以适应复杂环境
下的控制要求，这一思路对提高控制效果具有很好的现实意义。
-Artificial Neural Network (ANN) is an imitation of the intelligence by the point of physiological. It has a high capacity of learning and adaptive, can approximate any function to arbitrary accuracy, and complete the simulation of the system. The Genetic
algorithm is a simulation of natural biological evolution, which has a strong ability of
global optimization. These two algorithms are more intelligent method of current
research. The idea of combining these two methods with the conventional PID
controller to be a intelligent controller with the abilities of parameter auto-tuning and
adaptive for the requirements of the complex environment, has a high practical
significance of improving the control effect.
Platform: |
Size: 661504 |
Author:baijiaxuan |
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Description: In This paper, the authors propose a Sensorless Direct Torque and Flux Control (DTFC) of
Induction Motor (IM) using two approach intelligent techniques: Mamdani Fuzzy Logic (FL)
controller is used for controlling the rotor speed and Artificial Neural Network (ANN) applied in
switching select stator voltage. We estimated the rotor speed by using the Model Reference
Adaptive Systems (MRAS). The control method proposed in this paper can reduce the torque,
stator flux and current ripples and especially improve system good dynamic performance and
robustness in high and low speeds-In This paper, the authors propose a Sensorless Direct Torque and Flux Control (DTFC) of
Induction Motor (IM) using two approach intelligent techniques: Mamdani Fuzzy Logic (FL)
controller is used for controlling the rotor speed and Artificial Neural Network (ANN) applied in
switching select stator voltage. We estimated the rotor speed by using the Model Reference
Adaptive Systems (MRAS). The control method proposed in this paper can reduce the torque,
stator flux and current ripples and especially improve system good dynamic performance and
robustness in high and low speeds.. Platform: |
Size: 3107840 |
Author:aa |
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Description: 采用bp神经网络对其进行函数拟合。误差反向传播网络（bp网络）是目前人工神经网络模式中最具代表性，应用最广泛的一种模型，具有自学习、自组织、自适应和很强的非线性映射能力，可以以任意精度逼近任意连续函数。-The bp neural network to its function fitting. Error back propagation network (bp network) is the artificial neural network model of the most representative, the most widely used a model, which is of self study, self organization, adaptive and strong nonlinear mapping ability, with arbitrary precision can approximate any continuous function. Platform: |
Size: 67584 |
Author:杨明飞 |
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Description: 掌握用Matlab人工神经网络工具箱设计自适应线性元件的方法，掌握人工神经网络学习和训练过程；理解误差曲面；分析比较自适应线性元件的不足。-Master with Matlab artificial neural network toolbox design adaptive linear element method, to master artificial neural network learning and training process understand the error surface analysis of the lack of more adaptive linear element. Platform: |
Size: 6144 |
Author:楼云 |
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Description: 人工神经网络是由大量的简单基本元件——神经元相互联接而成的自适应非线性动态系统。每个神经元的结构和功能比较简单，但大量神经元组合产生的系统行为却非常复杂。-Artificial neural network is composed of a large number of simple basic components- made of neurons coupled to each other adaptive nonlinear dynamic systems. Structure and function of each neuron is relatively simple, but the combination of system behavior resulting large number of neurons is very complex. Platform: |
Size: 2048 |
Author:utana |
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Description: ADALINE (Adaptive Linear Neuron or later Adaptive Linear Element) is an early single-layer artificial neural network and the name of the physical device that implemented this network Platform: |
Size: 3072 |
Author:gaurav |
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Description: As signatures are widely accepted bio-metric for authentication and identification of a person because every person
has a distinct signature with its specific behavioral property, so it is very much necessary to prove the authenticity of signature
itself. There are various techniques to signature recognition with a lot of scope of research. In this paper off-line signature
recognition and verification system using Artificial Neural Network (ANN) is purposed. The purposed network based upon the
adaption of ANN to recognized signature to connected type pattern. The purpose ANN was trained with back propagation with
momentum and adaptive learning rate. A triple hidden layer ANN with 100 inputs 58.38.20 hidden neurons layers and 5 neurons
in output layers gives best results as compared with other networks. This paper represents a brief review on various approaches
used in signature verification systems. Platform: |
Size: 100352 |
Author:shiwi |
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Description: 利用matlab GUI实现的串口编程例子，基于人工神经网络的常用数字信号调制，车牌识别定位程序的部分功能，均值便宜跟踪的示例，信号处理中的旋转不变子空间法，一些自适应信号处理的算法。-Use serial programming examples matlab GUI implementation, The commonly used digital signal modulation based on artificial neural network, Part of the license plate recognition locator feature, Example tracking mean cheap, Signal Processing ESPRIT method, Some adaptive signal processing algorithms. Platform: |
Size: 6144 |
Author:jeatqm |
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Search - Adaptive Artificial neural network