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Invited Survey Paper Fuzzy inductive reasoning: a consolidated approach to data-driven construction of complex dynamical systems

机译:受邀调查论文模糊归纳推理:以数据驱动的复杂动力系统构建方法

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摘要

Fuzzy inductive reasoning (FIR) is a modelling and simulation methodology derived from the General Systems Problem Solver. It compares favourably with other soft computing methodologies, such as neural networks, genetic or neuro-fuzzy systems, and with hard computing methodologies, such as AR, ARIMA, or NARMAX, when it is used to predict future behaviour of different kinds of systems. This paper contains an overview of the FIR methodology, its historical background, and its evolution.
机译:模糊归纳推理(FIR)是从通用系统问题解决程序中获得的一种建模和仿真方法。当用于预测不同类型系统的未来行为时,它与其他软计算方法(例如神经网络,遗传或神经模糊系统)以及硬计算方法(例如AR,ARIMA或NARMAX)相比具有优势。本文概述了FIR方法论,其历史背景及其演变。

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