Description: 着重研究了水位、布量、水量和转速脉冲检测原理,给出了详细的水位检测、
重量检测电路。实践表明,系统硬件运行稳定,在进行模糊推理及神经网络学习时具有更快的速度-Focused on water, cloth volume, amount and speed pulse detection principle is given in detail the water level detection, the weight detection circuit. Practice shows that the stable operation of system hardware, in fuzzy inference and neural network learning with a faster rate Platform: |
Size: 260096 |
Author:Vmware007 |
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Description: 设计的bp神经网络，来判断和预测机器的故障级别。-Bp neural network designed to determine and predict the fault-level machines. Platform: |
Size: 1024 |
Author:gezn |
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Description: 仿真曲线可以看出，神经网络PID和模糊PID的响应速度比常规PID控制快，而且系统的调整时间很短，没有超调。神经网络PID与模糊PID的控制效果相同，都能在极短的时间内，消除偏差，快速地克服偏差的影响，改善了系统动态特性，因此说明神经网络PID和模糊PID对锅炉汽包水位的控制效果要优于常规PID控制。-Simulation curve can be seen, PID and fuzzy PID neural network response speed faster than the conventional PID control, and adjust the system time is very short, there is no overshoot. PID and fuzzy neural network PID control has the same effect, can in a very short period of time, to eliminate bias, and quickly overcome the effects of bias and improve the system dynamics, So the neural network PID and fuzzy PID of the boiler drum water level control more effective than the conventional PID control. Platform: |
Size: 2048 |
Author:liuda |
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Description: 采用基于奇异值分解和人工神经网络的多传感器数据融合方法对喷水推进泵的空化状态进行了分类识别研究。首先利用基于奇异值分解的权值估计算法分别对水声信号和振动信号在时间上进行数据级融合，提取出各自的特征，然后将所有特征组合起来作为神经网络的输入，利用ＢＰ网络和ＲＢＦ网络进行特征级融合和分类识别。-The use of water jet propulsion pump cavitation state multi-sensor data fusion method based on singular value decomposition and artificial neural network classification and recognition. First, based on the singular value decomposition weights estimation algorithm level data fusion underwater acoustic signals and vibration signals in time, extract individual characteristics, then combined all features as the input of the neural network, using BP and RBF network feature fusion and classification. Platform: |
Size: 1116160 |
Author:张力 |
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Description: 基于神经网络的双容水箱的液面控制系统，用的是simulink仿真。-Level control system based on neural network-based dual-tank water Platform: |
Size: 157696 |
Author:往阳台 |
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Description: 利用神经网络控制三容水箱水位并在文件中附有模型说明和推导-Neural network control unit- tank with water level and the model described in the document and deduced Platform: |
Size: 136192 |
Author:哈虎 |
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Description: 小波神经网络预测水文数据，径流数据等等，输入层为4天的水位，输出为预测的水位(The wavelet neural network predicts hydrological data, runoff data and so on. The input layer is 4 days water level, and the output is the predicted water level) Platform: |
Size: 1024 |
Author:逍遥胤
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Description: 1.批量最小二乘法算法（也称最小二乘的一次性完成辨识算法）
2.递推最小二乘法算法，应用递推算法对参数估计值进行不断修正,以取得更为准确的参数估计值。
3.粒子群算法(PSO)。粒子群优化算法的基本思想：是通过群体中个体之间的协作和信息共享来寻找最优解．PSO的优点在于简单容易实现并且没有许多参数的调节。
4.BP神经网络，各个神经元仅接收来自前一级的输出，经神经元处理后的信息将输出至下一级，网络中没有反馈，即前一级神经元不会接受后一级神经元的输出。
water tank是原始数据（双容水箱实验）(1. Batch least squares algorithm (also known as least squares one-time completion identification algorithm)
2. Recursive least squares algorithm, applying the recursive algorithm to continuously modify the parameter estimates to obtain more accurate parameter estimates.
3. Particle Swarm Optimization (PSO). The basic idea of particle swarm optimization algorithm is to find the optimal solution through the cooperation and information sharing between individuals in the group. The advantage of PSO is that it is simple and easy to implement and does not have many parameters to adjust.
4.BP neural network, each neuron only receives the output from the previous level, and the information processed by the neuron will be output to the next level. There is no feedback in the network, that is, the neurons in the previous level will not receive the neurons in the next level Meta's output.
water tank is the original data (double capacity water tank experiment)) Platform: |
Size: 6144 |
Author:系基金迪欧 |
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Search - neural network water level