论文标题
部分可观测时空混沌系统的无模型预测
Creating an Optimal Portfolio of Crops Using Price Forecasting to Increase ROI for Indian Farmers
论文作者
论文摘要
储层计算是预测湍流的有力工具，其简单的架构具有处理大型系统的计算效率。然而，其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间，然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用，该系统模拟了湍流的若干特征。我们表明，使用径向基函数作为非线性投影器，即使只有部分观测并且不知道控制方程，也能稳健地捕捉复杂的系统非线性。最后，我们表明，当测量稀疏、不完整且带有噪声，甚至控制方程变得不准确时，我们的网络仍然可以产生相当准确的预测，从而为实际湍流系统的无模型预测铺平了道路。
The Indian agricultural sector being in a constant phase of upgradation, has been on the road to modernization for the last couple of years. The fundamental source of livelihood for over 70 percent of the population living in rural parts of the country is still agriculture. The average Indian farmer, although has access to raw and trend data pertaining to crop prices, yield and demand from Indian government and private websites, still struggles to make the right choices. They are constantly faced with the dilemma of choosing the right crop to address market demand and fetch them a decent profit. Since the process of shortlisting crops amongst the many suitable ones isn't completely scientific and usually dictated by area tradition, this project has aimed to address that issue by forecasting the price of those crops and uses that to create an optimal portfolio that the farmers can obtain to arrive at a data-driven decision for crop selection with optimal estimated ROI.
