Exploiting a Modified Gray Model in Back Propagation Neural Networks for Enhanced Forecasting

Gao, Xuejun; Huang, Tingwen; Wang, Zhenyou; Xiao, Mingqing

doi:10.1007/s12559-014-9247-2

Cited by 11 publications

(3 citation statements)

References 18 publications

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“…It is able to analyse the system that includes insufficient information and unapparent relationship [26,27]. Hence, to predict some unknown information based on limited information, GPT is often used.…”

Section: Imponderable Parameter Forecastingmentioning

confidence: 99%

A Semi-blind Model with Parameter Identification for Building Temperature Estimation

et al. 2017

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An accurate thermal model for building enables the heating system (HS) to work efficiently as well as save energy. Thermal modelling often requires physical parameters of the building, which are difficult to be accurately determined. The aim of this work is to develop an optimal thermal model for better understanding of thermal dynamics with the goal of using this to estimate temperature variation in a few hours ahead within building. Based on the characteristics of thermal motion, a conventional physics-based (PB) model for building temperature estimation is introduced first. Afterwards, in order to refine the model and improve the actual performance, we propose an innovative semi-blind (SB) model based on data-driven approaches. Additionally, the methodologies including self-adaptive algorithms (SAAs) and grey prediction technique (GPT) have been applied in dealing with the integrated parameters estimation (IPE) process to ensure the practicability of the implemented model. The proposed model schema is validated by testing in a laboratory. The results indicate that the proposed approach achieves much higher accuracy in estimating temperature variation than the conventional PB model, with only limited knowledge of the building characteristics. The root mean square deviation (RMSD) of SB model and PB model are 0.18 and 0.43, respectively. According to the results, it can be conclu-

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Section: Imponderable Parameter Forecastingmentioning

confidence: 99%

A Semi-blind Model with Parameter Identification for Building Temperature Estimation

et al. 2017

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“…The input of the hidden layer and output layer nodes are the weighted value of the output of the previous layer nodes. The incentive degree of each node is decided by its excitation function [7][8][9].…”

Section: Back Propagation Neural Network Modeling Processmentioning

confidence: 99%

Control and Anticontrol of Chaos for Arrhythmia Based on Impulsive Differential Theory

Miao¹

2015

IJUNESST

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“…Especially in recent years, IT2 FLSs have been widely used on forecasting activities. Most recent studies on load forecasting show that IT2 FLSs (Khosravi et al, 2012; Khosravi and Nahavandi, 2014) have superiority approximation capability even better than nonparametric neural networks (Barbounis and Theocharis, 2007; Gao et al, 2014; Gusev and Burkovskii, 2013; Mehdi et al, 2016; Pany and Ghoshal, 2015). Furthermore, IT2 FLSs based on optimization algorithms outperform their T1 counterparts on forecasting.…”

Section: Introductionmentioning

confidence: 99%

Design of Begian–Melek–Mendel structure-based interval type-2 fuzzy logic systems optimized with backpropagation algorithms

Chen

2022

Transactions of the Institute of Measurement and Control

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As an emerging technology, the efficient and energy conserving process of permanent magnetic drive (PMD) presents high uncertainties. This paper designs a type of Begian–Melek–Mendel (BMM) structure interval type-2 fuzzy logic systems (IT2 FLSs) for PMD process uncertain parameters forecasting. The antecedent, consequent, and input measurements of systems are all selected as the Gaussian type-2 primary membership functions with uncertain standard deviations. Then the backpropagation algorithms are used to tune the parameters of IT2 FLSs. According to the Monte Carlo simulation studies and convergence analysis, the proposed IT2 FLSs are proved to be superior to two corresponding T1 FLSs in generalization ability.

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Exploiting a Modified Gray Model in Back Propagation Neural Networks for Enhanced Forecasting

Cited by 11 publications

References 18 publications

A Semi-blind Model with Parameter Identification for Building Temperature Estimation

A Semi-blind Model with Parameter Identification for Building Temperature Estimation

Control and Anticontrol of Chaos for Arrhythmia Based on Impulsive Differential Theory

Design of Begian–Melek–Mendel structure-based interval type-2 fuzzy logic systems optimized with backpropagation algorithms

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