Neural Modeling of Phaser and Flanging Effects

Alec Wright; Vesa Välimäki

doi:10.17743/jaes.2021.0029

Neural Modeling of the Distribution of Protein, Water and Gluten in Wheat Grains during Storage

Sustainability ◽

10.3390/su12125050 ◽

2020 ◽

Vol 12 (12) ◽

pp. 5050

Author(s):

Katarzyna Szwedziak ◽

Ewa Polańczyk ◽

Żaneta Grzywacz ◽

Gniewko Niedbała ◽

Wiktoria Wojtkiewicz

Keyword(s):

Total Protein ◽

Near Infrared ◽

Linear Regression Analysis ◽

Multiple Linear Regression Analysis ◽

Quality Parameters ◽

Neural Modeling ◽

Infrared Spectrometry ◽

Stored Grain ◽

Stored Wheat

An important requirement in the grain industry is to obtain fast information on the quality of purchased and stored grain. Therefore, it is of great importance to search for innovative solutions aimed at the monitoring and fast assessment of quality parameters of stored wheat The results of the evaluation of total protein, water and gluten content by means of near infrared spectrometry are presented in the paper. Multiple linear regression analysis (MLR) and neural modeling were used to analyze the obtained results. The results obtained show no significant changes in total protein (13.13 ± 0.15), water (10.63 ± 0.16) or gluten (30.56 ± 0.54) content during storage. On the basis of the collected data, a model artificial neural network (ANN) MLP 52-6-3 was created, which, with the use of four independent features, allows us to determine changes in the content of water, protein and gluten in stored wheat. The chosen network returned good error values: learning, below 0.001; testing, 0.015; and validation, 0.008. The obtained results and their interpretation are an important element in the warehouse industry. The information obtained in this way about the state of the quality of stored grain will allow for a fast reaction in case of the threat of lowering the quality parameters of the stored grain.

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Hybrid-neural modeling of a complex industrial process

IMTC 2001. Proceedings of the 18th IEEE Instrumentation and Measurement Technology Conference. Rediscovering Measurement in the Age of Informatics (Cat. No.01CH 37188) ◽

10.1109/imtc.2001.929439 ◽

2002 ◽

Cited By ~ 2

Author(s):

P. Berenyi ◽

G. Horvath ◽

B. Pataki ◽

G. Strausz

Keyword(s):

Industrial Process ◽

Neural Modeling

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NEURAL MODELING AND CONTROL OF DYNAMIC SYSTEMS WITH HYSTERESIS

Transactions of the Canadian Society for Mechanical Engineering ◽

10.1139/tcsme-2007-0008 ◽

2007 ◽

Vol 31 (1) ◽

pp. 127-141

Author(s):

Yonghong Tan ◽

Xinlong Zhao

Keyword(s):

Dynamic Systems ◽

Control Strategy ◽

Neural Modeling ◽

Modeling And Control ◽

Control Scheme ◽

The Neural Networks ◽

Adaptive Control Strategy ◽

Set Up ◽

Modeling Strategy ◽

And Control

A hysteretic operator is proposed to set up an expanded input space so as to transform the multi-valued mapping of hysteresis to a one-to-one mapping so that the neural networks can be applied to model of the behavior of hysteresis. Based on the proposed neural modeling strategy for hysteresis, a pseudo control scheme is developed to handle the control of nonlinear dynamic systems with hysteresis. A neural estimator is constructed to predict the system residual so that it avoids constructing the inverse model of hysteresis. Thus, the control strategy can be used for the case where the output of hysteresis is unmeasurable directly. Then, the corresponding adaptive control strategy is presented. The application of the novel modeling approach to hysteresis in a piezoelectric actuator is illustrated. Then a numerical example of using the proposed control strategy for a nonlinear system with hysteresis is presented.

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