主要介绍了python自动生成model文件过程详解,文中通过示例代码介绍的非常详细，对大家的学习或者工作具有一定的参考学习价值

最新试衣间算法论文《"VITON: An Image-based Virtual Try-on Network"》，算法两个shape模型

gsoc17-hhmm：贝叶斯分层隐马尔可夫模型应用于金融时间序列，这是Google Summer of Code 2017的研究复制项目

产生各种调制信号python

data driven model

主要介绍了Keras构建神经网络踩坑(解决model.predict预测值全为0.0的问题)，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧

马尔默 降雨径流模型的模块化评估工具箱-用于46个概念性水文模型的Matlab代码。 MARRMoT是一种新颖的降雨-径流模型比较框架，可以对不同概念性水文模型结构之间进行客观比较。 该框架提供了用于46种独特模型结构的Matlab代码，所有模型结构的标准化参数范围以及每个模型的可靠数值实现。 该框架随附大量文档，用户手册和一些工作流脚本，这些脚本提供了如何使用该框架的示例。 MARRMoT基于单个通量函数和聚合模型函数，可实现多种可能的应用。 如果您对使用或运行代码有任何疑问，或愿意为此做出贡献，请联系wouter.knoben [-at-] usask.ca。 同行评审后的变化 MAR

特斯拉电池技术真相，第二部分The Truth About Tesla Model 3 Batteries - Part 2

In this thesis we consider the problem of designing and implementing Model Predictive Controllers (MPC) for stabilizing the dynamics of an autonomous ground vehicle. For such a class of systems, the non-linear dynamics and the fast sampling time limit the real-time implementation of MPC algorithms to local and linear operating regions. This phenomenon becomes more relevant when using the limited computational resources of a standard rapid prototyping system for automotive applications. In this thesis we first study the design and the implementation of a nonlinear MPC controller for an Active Font Steering (AFS) problem. At each time step a trajectory is assumed to be known over a finite horizon, and the nonlinear MPC controller computes the front steering angle in order to follow the trajectory on slippery roads at the highest possible entry speed. We demonstrate that experimental tests can be performed only at low vehicle speed on a dSPACE rapid prototyping system with a frequency of 20 Hz. Then, we propose a low complexity MPC algorithm which is real-time capable for wider operating range of the state and input space (i.e., high vehicle speed and large slip angles). The MPC control algorithm is based on successive on-line linearizations of the nonlinear vehicle model (LTV MPC). We study performance and stability of the proposed MPC scheme. Performance is improved through an ad hoc stabilizing state and input constraints arising from a careful study of the vehicle nonlinearities. The stability of the LTV MPC is enforced by means of an additional convex constraint to the finite time optimization problem. We used the proposed LTV MPC algorithm in order to design AFS controllers and combined steering and braking controllers. We validated the proposed AFS and combined steering and braking MPC algorithms in real-time, on a passenger vehicle equipped with a dSPACE rapid prototyping system. Experiments have been performed in a testing center equipped with snowy and icy tracks. For both controllers we showed that vehicle stabilization can be achieved at high speed (up to 75 Kph) on icy covered roads. This research activity has been supported by Ford Research Laboratories, in Dearborn, MI, USA.

nonlinear model predictive control （第二版含MATLAB仿真实例）