摘要翻译：
神经网络是静态环境下分类和回归的有力工具。本文描述了一种用于创建动态适应变化条件的神经网络集成的技术。该模型将输入空间划分为四个区域，并根据网络在该区域样本上的性能，在每个区域中赋予网络一个权重。该集成通过根据网络当前的性能不断调整这些权值来动态自适应。所使用的数据集是以预测未来铂金价格为目标的财务指标集合。没有权重的集合不会改善每周没有变化的天真估计；我们的加权算法在20周的预测中给出了63%的平均百分比误差。
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英文标题：
《Prediction of Platinum Prices Using Dynamically Weighted Mixture of
  Experts》
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作者：
Baruch Lubinsky, Bekir Genc and Tshilidzi Marwala
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最新提交年份：
2008
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Neural networks are powerful tools for classification and regression in static environments. This paper describes a technique for creating an ensemble of neural networks that adapts dynamically to changing conditions. The model separates the input space into four regions and each network is given a weight in each region based on its performance on samples from that region. The ensemble adapts dynamically by constantly adjusting these weights based on the current performance of the networks. The data set used is a collection of financial indicators with the goal of predicting the future platinum price. An ensemble with no weightings does not improve on the naive estimate of no weekly change; our weighting algorithm gives an average percentage error of 63% for twenty weeks of prediction.
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PDF链接：
https://arxiv.org/pdf/0812.2785