1) Factor Neural Networks
因素神经元网络
2) multiple-factor neural network fracture
多因素神经网络
3) factor neural network
因素神经网络
1.
On the basis of the merits of fuzzy theory and factor neural network,a new model for software engineering quality assessment is proposed.
基于模糊理论及因素神经网络的优点,提出了一种软件工程质量评价模型。
4) neural networks
神经元网络
1.
Application of neural networks to heat transfer calculation in a round billet continuous casting mould;
神经元网络应用于圆坯连铸结晶器传热计算
2.
Stock Price Forecasting Based on Neural Networks;
基于神经元网络的股票市场预测
3.
Aimed at a typical object in petrochemical industry, an intrinsically non-linearthird order lag with dead-time and non-minimum phase, this paper reports that an identifier and a controller have been constructed based upon neural networks, of which the fundamentals, architecture, simulation results and the author's analysis and proposals are alsopresented.
针对石油化学工业中的某一典型对象即具有严重非线性和纯滞后特性的三阶非最小相位系统,介绍利用神经元网络(ANN)构成的辨识器和控制器的原理、构成以及仿真结果,并给出了作者对神经元网络型辨识器与控制器的分析和建议。
5) neural network
神经元网络
1.
Application of neural network in calculation of steel quenching degree;
神经元网络在钢淬透性计算中的应用
2.
Multi-variable PID neural network control systems and their application to coordination control;
多变量PID型神经元网络控制系统及其在协调控制中的仿真研究
3.
Optimization system of utility boiler combustion based on neural network;
基于神经元网络的电厂锅炉燃烧优化系统
6) neuron network
神经元网络
1.
Application of PSO-based multivariable-PID neuron network in ball mill control;
基于PSO算法的多变量PID型神经元网络在球磨机控制上应用
2.
The Comprehensive Test of Some Type Missile and Neuron Network Based Simulation Diagnosing;
某型号导弹的综合测试和基于神经元网络的模拟诊断
3.
As to its multi-variable, strong coupling, nonlinear, time-varying characteristics, the control method of multi-variable PID neuron network for ball mill was introduced, its time-changing characteristic at different working conditions was described by multi-models, the PID neuron network was used as decoupling controller for each model.
针对球磨机制粉系统的多变量、强耦合、非线性、时变性等特点,提出了采用基于PID型神经元网络的多模型控制方法,在不同工况下系统的时变特性采用多个模型进行描述,而每个模型的控制器则采用PID型神经元网络进行解耦控制。
补充资料:神经元形态中枢神经系统内轴突髓鞘的形成示意图
李瑞端绘
[图]
说明:补充资料仅用于学习参考,请勿用于其它任何用途。
参考词条