A novel algorithm for wavelet neural networks with application to enhanced PID controller design

Zhao, Yang; Du, Xue; Xia, Genglei; Wu, Ligang

doi:10.1016/j.neucom.2015.01.015

Cited by 24 publications

(13 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While the original concepts and theory of the artificial neuron and the neural network were developed during the 1940s-1970s, and later refined and improved upon during the 1980s and 1990s [45], the advent and increased availability of powerful computational hardware has led to large increase in the application of ANN in the past 10-20 years. Nowadays, ANN are utilized for many different applications in a wide variety of fields [46][47][48][49][50][51][52]. This chapter aims to give an introduction into the theory and mathematics behind ANN, explain in more detail one specific type of AN, and finally discuss the main methods of training ANN.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Solving vapor-liquid flash problems using artificial neural networks

Poort

Ramdin

Kranendonk³

et al. 2019

Fluid Phase Equilibria

View full text Add to dashboard Cite

Vapor-liquid phase equilibrium (flash) calculations largely contribute to the total computation time of many process simulation models. As a result, process simulations, especially dynamic cases, are limited in the amount of detail that can be included due to time restrictions. In addition, under certain conditions flash calculations can fail to provide acceptable results. In this work, artificial neural networks were investigated as a potentially faster and more robust alternative to conventional flash calculation methods. Classification neural networks were used to determine the phase stability of a given mixture of fluids, while regression networks were used to make predictions of thermodynamic property values. In addition to conventional flash types such as the constant pressure, constant temperature (P T ), and constant pressure, constant entropy (P S) flash, neural networks are used to develop two concept flash types: a constant entropy, constant volume (SV ), and a constant enthalpy, constant volume (HV ) flash. All neural networks were trained on, and compared to, data generated using the P T -flash algorithm from the Thermodynamics for Engineering Applications (TEA) property calculator. Data was generated for mixtures of water and methanol over a wide range of pressures and temperatures. The artificial neural networks showed speed improvements over TEA of up to 35 times for phase classification and 15 times for property predictions. Overall phase classification accuracy scores of around 97% were achieved. Average property value prediction errors range between 0.5% and 7% when compared to the spread in magnitude of the test data, and R 2 scores were in the general order of 0.95 and higher, although classification and property prediction in the two-phase region showed markedly higher errors than properties in the pure liquid or vapor regions. Moreover, thermodynamic consistency and the stability of a system consisting of multiple neural network flash types both still require considerable improvement. Finally, this work shows that artificial neural networks can be used to create unconventional flash types such a the SV -and HV -flash.i

show abstract

Section: Artificial Neural Networkmentioning

confidence: 99%

Solving vapor-liquid flash problems using artificial neural networks

Poort

Ramdin

Kranendonk³

et al. 2019

Fluid Phase Equilibria

View full text Add to dashboard Cite

show abstract

“…We make use of optimized gradient descent method to adjust the neural network weights, this method adjusts the parameters along the opposite direction of the error-performance function gradient until the error of the desired network output and actual network output satisfies the performance index required [34][35][36][37]. In general, BP neural network consists of input layer, hidden layer and output layer.…”

Section: Bp Neural Network Based Constrained Controller Designmentioning

confidence: 99%

“…the calculation of Eq (35),. the function of weight coefficient in hidden layer is redefined as Adding inertia item can make system leave the flat region rapidly.…”

mentioning

confidence: 99%

A novel adaptive neural network constrained control for solid oxide fuel cells via dynamic anti-windup

Liu

2016

Neurocomputing

View full text Add to dashboard Cite

“…Recently, PIDNN controller is one of the popular methods used for control complexes systems. Several robust and auto tuning techniques have been proposed in order to further improve the control and robust performance of the PIDNN controller [1,2,3,4]. In [5], an adaptive PIDNN controller was presented.…”

Section: Introductionmentioning

confidence: 99%

A New PID Neural Network Controller Design for Nonlinear Processes

Zribi¹,

Chtourou²,

Djemel³

2017

J CIRCUIT SYST COMP

View full text Add to dashboard Cite

In this paper, a novel adaptive tuning method of PID neural network (PIDNN) controller for nonlinear process is proposed. The method utilizes an improved gradient descent method to adjust PIDNN parameters where the margin stability will be employed to get high tracking performance and robustness with regard to external load disturbance and parameter variation. Simulation results show the effectiveness of the proposed algorithm compared with other well-known learning methods.

show abstract

A novel algorithm for wavelet neural networks with application to enhanced PID controller design

Cited by 24 publications

References 27 publications

Solving vapor-liquid flash problems using artificial neural networks

Solving vapor-liquid flash problems using artificial neural networks

A novel adaptive neural network constrained control for solid oxide fuel cells via dynamic anti-windup

A New PID Neural Network Controller Design for Nonlinear Processes

Contact Info

Product

Resources

About