This work addresses the performance comparison between four clustering
techniques with the objective of achieving strong hybrid models in supervised
learning tasks. A real dataset from a bio-climatic house named Sotavento placed
on experimental wind farm and located in Xermade (Lugo) in Galicia (Spain) has
been collected. Authors have chosen the thermal solar generation system in
order to study how works applying several cluster methods followed by a
regression technique to predict the output temperature of the system. With the
objective of defining the quality of each clustering method two possible
solutions have been implemented. The first one is based on three unsupervised
learning metrics (Silhouette, Calinski-Harabasz and Davies-Bouldin) while the
second one, employs the most common error measurements for a regression
algorithm such as Multi Layer Perceptron.

通过比较四种聚类技术的性能，本研究旨在实现在监督学习任务中强大的混合模型。使用来自西班牙加利西亚卢戈 Xermade 的一个实验性风电场上的生态气候房屋 Sotavento 的实际数据集进行研究，选择了热太阳能发电系统，应用多种聚类方法和回归技术来预测系统的输出温度，并采用了三个无监督学习度量标准（Silhouette、Calinski-Harabasz 和 Davies-Bouldin）和多层感知器等回归算法的常见误差度量标准来定义每种聚类方法的质量。

基于太阳热系统的混合回归模型的聚类技术选择研究

Clustering Techniques Selection for a Hybrid Regression Model: A Case  Study Based on a Solar Thermal System

Recurrent Neural Networks (RNNs) have shown great success in modeling
time-dependent patterns, but there is limited research on their learned
representations of latent temporal features and the emergence of these
representations during training. To address this gap, we use timed automata
(TA) to introduce a family of supervised learning tasks modeling behavior
dependent on hidden temporal variables whose complexity is directly
controllable. Building upon past studies from the perspective of dynamical
systems, we train RNNs to emulate temporal flipflops, a new collection of TA
that emphasizes the need for time-awareness over long-term memory. We find that
these RNNs learn in phases: they quickly perfect any time-independent behavior,
but they initially struggle to discover the hidden time-dependent features. In
the case of periodic "time-of-day" aware automata, we show that the RNNs learn
to switch between periodic orbits that encode time modulo the period of the
transition rules. We subsequently apply fixed point stability analysis to
monitor changes in the RNN dynamics during training, and we observe that the
learning phases are separated by a bifurcation from which the periodic behavior
emerges. In this way, we demonstrate how dynamical systems theory can provide
insights into not only the learned representations of these models, but also
the dynamics of the learning process itself. We argue that this style of
analysis may provide insights into the training pathologies of recurrent
architectures in contexts outside of time-awareness.

本文利用定时自动机来介绍一系列监督学习任务，建立在隐藏时间变量和复杂性直接可控的行为模型基础上，通过研究倒置的时间特征，证明基于动态系统理论的工具可以不仅提供模型学习方案的见解而且能够证明训练过程的动力学。