基於模組化模糊推理系統處理大數據問題

Processing Big Data Problems using Modular Fuzzy Inference Systems

訊息技術近幾十年來的飛速發展導致每天生成和交換的數字數據量不斷增加，有效地從海量大數據中獲取訊息為公司帶來競爭優勢已經成為一個日益嚴重的問題，本研究通過建立模塊化的模糊推理系統，提出了一種基於大數據的決策支持策略，該系統主要由模糊系統識別，k均值聚類算法和自適應網絡模糊推理系統（ANFIS）組成，基本上，ANFIS被設計為基於模糊If-Then規則具有優化方案的推理系統，特別是在運籌學領域，它的學習能力為決策支持系統帶來了積極的表現且常被用於支持各種決策問題。

Over recent decades, the rapid progress of information technology has led to an ever-increasing amount of digital data being generated and exchanged every day. Therefore, effectively acquiring the information from this vast amount of big data to give a company a competitive advantage has become an increasing issue. This article proposes a decision support strategy based on big data by establishing a modular fuzzy inference system, and the proposed system primarily consists of fuzzy system identification, k-means clustering algorithm, and adaptive network-based fuzzy inference system (ANFIS). ANFIS is designed as a Fuzzy If-Then rules-based inference system with the optimization scheme. In particular, its learning ability brings positive performance to the decision support system, and it is usually applied to support a variety of decision-making issues in operational research.