Optimized Fuzzy-Based Wavelet Neural Network Controller for a Non-Linear Process Control System

Deepa, S. N.; Jayalakshmi, N.

doi:10.1080/03772063.2020.1865212

Cited by 7 publications

(3 citation statements)

References 20 publications

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“…In response to the above analysis, a set of cooling devices for cabinets in the computer roomadaptive control ventilation floor, when the temperature reaches the set value of the ventilation floor will increase the opening to deliver fresh air so as to dissipate heat to the equipment in the cabinet [7] . In the development of advanced control theory, fuzzy logic controller is one of the hotspots of control research, for the problem of low precision of mathematical model, fuzzy control is suitable for the controlled object with lag and complexity [8][9][10] . Based on the above analysis, a set of fuzzy logic rules applicable to the computer room system is designed, and fuzzy logic rules are introduced into the temperature control to increase the control precision, and the ventilated floor is prompted to automatically adjust the floor opening to achieve good cooling effect and energy saving purpose.…”

Section: Introductionmentioning

confidence: 99%

Energy Saving Research on Temperature Control in Data Center Server Rooms

Su,

2024

J. Phys.: Conf. Ser.

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To tackle the challenge of inefficient traditional control methods and energy wastage caused by temperature disparities within the data center’s server room cooling system, this research initiative initially installed temperature and humidity sensors in each cabinet to gather comprehensive data. Subsequently, a detailed mathematical model of the room’s temperature system was formulated to assess the influence of air supply airflow distribution on the temperature distribution. Following this, through the integration of fuzzy theory with the PID control strategy, a sophisticated fuzzy PID controller was developed. Utilizing Simulink, an in-depth simulation analysis of both PID and fuzzy PID temperature control strategies was executed. The outcomes of the simulations underscored that fuzzy PID control effectively addresses the time delay and stability concerns present in conventional PID temperature control systems. Lastly, empirical validation confirmed that the fuzzy PID control algorithm can proficiently manage the server room’s temperature while concurrently decreasing the intake of cold air, thus promoting energy conservation. This control approach showcases commendable adaptability and robust performance characteristics.

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Section: Introductionmentioning

confidence: 99%

Energy Saving Research on Temperature Control in Data Center Server Rooms

Su,

2024

J. Phys.: Conf. Ser.

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“…At present, biometric identification technology is widely used in daily life. For example, nowadays, almost all cell phones have fingerprint unlocking and face unlocking, and when using a cell phone, more and more people no longer enter the unlocking password but press the fingerprint or use the face to the camera can be unlocked directly [14][15][16]. Gradually, the password unlocking method on cell phones has been replaced by two unlocking methods: fingerprint unlocking and face unlocking.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Criminal Suspect Characteristics with Application of Wavelet Neural Networks

Lei,

Huang

2023

Applied Mathematics and Nonlinear Sciences

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Crime incidents grow at a certain rate every year and become more complicated day by day, but at this stage, the development of the field of crime analysis and prediction is still lagging, so this paper applies a wavelet neural network to predict the characteristics of criminal suspects. Analyze the applicability of the WNN model, explore the principle of criminal suspect feature prediction, and study the basis of wavelet neural network analysis. Establish the face feature recognition framework, use the Morlet function and Mexican Hat function pair to optimize and improve the wavelet neural network, and construct the model evaluation index for evaluating the model prediction effect. Preprocessing the suspect feature point data by using the grayscale reflection in Haar features. Simulation experiments are used to analyze the performance of different algorithms and the prediction effect of criminal suspect features on wavelet neural networks. The proposed algorithm in this study is superior and has a lesser number of parameters than ResNet. From the loss curves of different algorithms, the algorithm proposed in this study has a faster-descending loss curve and a smaller loss rate in the epoch value of [100, 200] interval. The overall range of the evaluation indexes of the algorithms proposed in this study is above 88%, and the highest accuracy rate can reach 94.234%, which is a good performance of the algorithms and accurate prediction.

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“…Artificial neural networks (ANNs) are inspired by the biological structure of animal brains, and have several applications in manufacturing [1][2][3], process control [4], pattern recognition [5], medical diagnosis [6], and environmental problems [7].…”

Section: Introductionmentioning

confidence: 99%

A Comparison Between Two Approaches to Optimize Weights of Connections in Artificial Neural Networks

Teymourifar¹

2021

ujam

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Artificial neural networks (ANNs) have been used for estimation in numerous areas. Raising the accuracy of ANNs is always one of the important challenges, which is generally defined as a non-linear optimization problem. The aim of this optimization is to find better values for the weights of the connections and biases in ANN because they seriously affect the efficiency. This study uses two approaches to do such optimization in an ANN. For this aim, we create a feed-forward backpropagation ANN using the functions of MATLAB's deep learning toolbox. To improve its accuracy, in the first approach, we use the Levenberg -Marquardt algorithm (LMA) for training, which is available in MATLAB's deep learning toolbox. In the second approach, we optimize the values of weights and biases of ANN with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), available in MATLAB's global optimization toolbox. Then, we assess the accuracy of estimation for the trained ANNs. In this way, for the first time in the literature, we compare these methods for the optimization of an ANN. The used data sets are also available in MATLAB. Based on the acquired results, in some data sets, training with LMA, and for some others training with PSO cause the best results, however, training with LMA is faster, significantly. Although the used approaches and the obtained conclusions are beneficial for researchers that work in this field, they have some limitations. For instance, since only the functions and data sets from MATLAB are used, it can only serve as an example for researchers.

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Optimized Fuzzy-Based Wavelet Neural Network Controller for a Non-Linear Process Control System

Cited by 7 publications

References 20 publications

Energy Saving Research on Temperature Control in Data Center Server Rooms

Energy Saving Research on Temperature Control in Data Center Server Rooms

Prediction of Criminal Suspect Characteristics with Application of Wavelet Neural Networks

A Comparison Between Two Approaches to Optimize Weights of Connections in Artificial Neural Networks

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