Grey prediction fuzzy control for pH processes in the food industry

Chung, Chao-Chin; Chen, Ho‐Hsien; Ting, Chou‐Chik

doi:10.1016/j.jfoodeng.2009.09.004

Cited by 28 publications

(16 citation statements)

References 43 publications

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“…A very challenging problem in food processing is quality and safety control of food products, in such away much time and effort are spent on methods for this goal accordingly (Harker et al, 2008;Chung et al, 2010;Goni and Purlis, 2010;Bozkurt and Icier, 2010). Trained human sensory panels evaluating quality parameters are often employed but, however, this approach suffers from some drawbacks.…”

Section: Application Importance In Food Controlmentioning

confidence: 99%

Biomimetic-based odor and taste sensing systems to food quality and safety characterization: An overview on basic principles and recent achievements

Ghasemi-Varnamkhasti

Mohtasebi

Siadat

2010

Journal of Food Engineering

131

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Section: Application Importance In Food Controlmentioning

confidence: 99%

Biomimetic-based odor and taste sensing systems to food quality and safety characterization: An overview on basic principles and recent achievements

Ghasemi-Varnamkhasti

Mohtasebi

Siadat

2010

Journal of Food Engineering

131

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“…A grey-fuzzy predictive controller design method was presented in [19]. The idea in [19] was adopted in [7] to design a grey prediction fuzzy control for pH processes.…”

Section: Introductionmentioning

confidence: 99%

Optimal gray PID controller design for automatic voltage regulator system via imperialist competitive algorithm

Tang

Zhao

Han

et al. 2015

Int. J. Mach. Learn. & Cyber.

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Automatic voltage regulator (AVR) is an equipment maintaining the terminal voltage of generators to a specific level all the time and under any load conditions. Many controllers for AVR system are designed based on a linearized normal model of AVR system and they are not robust enough against uncertainties such as parameter variation and load change in the system. In this paper, a gray PID (GPID) controller is designed for AVR system. The GPID controller consists of two parts, i.e. a conventional PID controller together with a gray compensation controller. In GPID controller, gray GM (0, N) model is used to estimate the uncertainties online, and a gray compensation controller is constructed according the estimation results to eliminate the effect of uncertainties. To further improve its performance, the GPID controller's parameters are optimized through a new evolutionary algorithm, i.e., imperialist competitive algorithm (ICA). The proposed GPID controller can effectively deal with the uncertainties in AVR system. Simulation results illustrate its effectiveness of the proposed control scheme.

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“…Nevertheless, in real market, the lead-time is short and the applicable data is limited [14]. This situation will increase the difficult of prediction management and reduce the precision levels of forecasting models [2], [3].…”

Section: Introductionmentioning

confidence: 99%

“…However, in dynamic web-market, the product lifecycle is short and the applicable data is limited [14]. These obstacles will strongly affect the precision degree of forecasting models [2], [3]. In recent years, various prediction models of grey theory have verified that these proposed models can significantly improve the accuracy of limited data forecasts.…”

Section: Introductionmentioning

confidence: 99%

A N-Order Grey-GA Optimizer to Forecast Taiwan Pollution Trends

Tsai

2013

2013 IEEE 10th International Conference on E-Business Engineering

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This research designs a novel n-order grey model (GM) to predict import and export effects on air pollution trends in Taiwan. We also present an optimal refiner of Grey-Genetic Algorithms (GGA) to improve GM efficiency. In our test-beds, we propose several GM models for the prediction of air pollution trends. The simple model (GM(1, 1)) and the n-order multiple model (GM(1, N) and RGM(1, N)) are utilized in air pollution prediction that was evaluated on their performances. These GM models are compared with those of the import and export data from Taiwan manufacturers. The results show that the prediction accuracy of the (GM(1, N) and RGM(1, N)) model presented the better accuracy than GM(1,1). The experimental results also show that the GGA controller can refine the prediction accuracy of GM models. This research is successfully applied these N-order multiple GM models for producing more accuracy information for pollution control.

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Grey prediction fuzzy control for pH processes in the food industry

Cited by 28 publications

References 43 publications

Biomimetic-based odor and taste sensing systems to food quality and safety characterization: An overview on basic principles and recent achievements

Biomimetic-based odor and taste sensing systems to food quality and safety characterization: An overview on basic principles and recent achievements

Optimal gray PID controller design for automatic voltage regulator system via imperialist competitive algorithm

A N-Order Grey-GA Optimizer to Forecast Taiwan Pollution Trends

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