Construction of polynomial matrix using block coefficient matrix representation auto-regressive moving average model for actively controlled structures

2004 ◽  
Vol 20 (6) ◽  
pp. 661-667
Author(s):  
Li Chunxiang ◽  
Zhou Dai
Keyword(s):  
Polynomial Matrix ◽  
Matrix Representation ◽  
Moving Average ◽  
Coefficient Matrix ◽  
Average Model ◽  
Moving Average Model ◽  
Auto Regressive ◽  
Controlled Structures

scholarly journals Forecasting India’s Electricity Demand Using a Range of Probabilistic Methods

Energies ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2574 ◽  
Author(s):  
Yeqi An ◽  
Yulin Zhou ◽  
Rongrong Li
Keyword(s):  
Power Generation ◽  
Electricity Generation ◽  
Moving Average ◽  
Probabilistic Methods ◽  
Grey Model ◽  
Annual Growth Rate ◽  
Average Model ◽  
Non Linear ◽  
Moving Average Model ◽  
Auto Regressive

With serious energy poverty, especially concerning power shortages, the economic development of India has been severely restricted. To some extent, power exploitation can effectively alleviate the shortage of energy in India. Thus, it is significant to balance the relationship between power supply and demand, and further stabilize the two in a reasonable scope. To achieve balance, a prediction of electricity generation in India is required. Thus, in this study, five methods, the metabolism grey model, autoregressive integrated moving average, metabolic grey model-auto regressive integrated moving average model, non-linear metabolic grey model and non-linear metabolic grey model-auto regressive integrated moving average model, are applied. We combine the characteristics of linear and nonlinear models, making a prediction and comparison of Indian power generation. In this way, we enrich methods for prediction research on electrical energy, which avoids large errors in trends of electricity generation due to those accidental factors when a single predictive model is used. In terms of prediction outcomes, the average relative errors from five models above are 1.67%, 1.62%, 0.84%, 1.84%, and 1.37%, respectively, which indicates high accuracy and reference value of these methods. In conclusion, India’s power generation will continue to grow with an average annual growth rate of 5.17% in the next five years (2018–2022).

A Structural Identification Method Based on Recurrent Neural Network and Auto-Regressive and Moving Average Model

2012 ◽  
Vol 256-259 ◽  
pp. 2261-2265
Author(s):  
Jing Xu ◽  
Xiu Li Wang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Moving Average ◽  
Structural Identification ◽  
Modal Parameters ◽  
Average Model ◽  
Identification Method ◽  
Moving Average Model ◽  
Nonlinear Relation ◽  
Auto Regressive

The work presented a structural identification method based on recurrent neural network and auto-regressive and moving average model. The proposed approach involves two steps. The first step is to build a recurrent neural network to map the complex nonlinear relation between the excitations and responses of the structure-unknown system by on-line learning . The second step is to propose a procedure to determine the modal parameters of the structure from the trained neural networks. The dynamic characteristics of the structure are directly evaluated from the weighting matrices of the trained recurrent neural network. Furthermore, a illustrative example demonstrates the feasibility of using the proposed method to identify modal parameters of structure-unknown systems.

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