Neural Model Extraction for Model-Based Control of a Neural Network Forward Model

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Shuhei Ikemoto ◽  
Kazuma Takahara ◽  
Taiki Kumi ◽  
Koh Hosoda
Keyword(s):  
Neural Network ◽  
Neural Model ◽  
Forward Model ◽  
Model Based Control ◽  
Model Extraction ◽  
Model Based

Elevating Model Predictive Control Using Feedforward Artificial Neural Networks: A Review

2009 ◽  
Vol 4 (1) ◽  
Author(s):  
Senthil Kumar Arumugasamy ◽  
Zainal Ahmad
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Model Based Control ◽  
Model Based ◽  
Non Linear ◽  
Artificial Neural ◽  
Neural Predictive Control

Process control in the field of chemical engineering has always been a challenging task for the chemical engineers. Hence, the majority of processes found in the chemical industries are non-linear and in these cases the performance of the linear models can be inadequate. Recently a promising alternative modelling technique, artificial neural networks (ANNs), has found numerous applications in representing non-linear functional relationships between variables. A feedforward multi-layered neural network is a highly connected set of elementary non-linear neurons. Model-based control techniques were developed to obtain tighter control. Many model-based control schemes have been proposed to incorporate a process model into a control system. Among them, model predictive control (MPC) is the most common scheme. MPC is a general and mathematically feasible scheme to integrate our knowledge about a target, process controller design and operation, which allows flexible and efficient exploitation of our understanding of a target, and thus produces optimal performance of a system under various constraints. The need to handle some difficult control problems has led us to use ANN in MPC and has recently attracted a great deal of attention. The efficacy of the neural predictive control with the ability to perform comparably to the non linear neural network strategy in both set point tracking and disturbance rejection proves to have less computation expense for the neural predictive control. The neural network model predictive control (NNMPC) method has less perturbations and oscillations when dealing with noise as compared to the PI controllers.

Neural Network Modeling in Model-Based Control of a Data Center

2015 ◽  
Author(s):  
Nachiket Kansara ◽  
Rohit Katti ◽  
Kourosh Nemati ◽  
Alan P. Bowling ◽  
Bahgat Sammakia
Keyword(s):  
Neural Network ◽  
Data Center ◽  
Sensor Data ◽  
Neural Network Modeling ◽  
Test Bed ◽  
Model Based Control ◽  
Storage Devices ◽  
University Of Texas ◽  
Model Based ◽  
Using Data

This paper presents the development of a neural network model of the server temperature to be used in model-based control of a data center. Data centers provide the optimal environments for operation of servers and storage devices. Conventionally, computational fluid dynamics (CFD) has been used to model the dynamic and complex environment of the data center. However, the drawback of this approach is its computational inefficiency. The effects of changing a single input may take an entire day to compute. Thus the CFD model is not well suited for model-based control. Instead, we propose to use an artificial Neural Network (NN) model which predicts server temperatures in significantly less time. In addition, this NN model has the capability of learning the environment in the data center by adapting its parameters in real time based on sensor data continuously taken from the data center. This work discusses the current development of the neural network, work being done at the University of Texas at Arlington, to include modeling of transient conditions, or time related changes, using data generated in a test bed Data Center at SUNY Binghamton.

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