scholarly journals Feedforward Compensation Analysis of Piezoelectric Actuators Using Artificial Neural Networks with Conventional PID Controller and Single-Neuron PID Based on Hebb Learning Rules

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3929 ◽  
Author(s):  
Cristian Napole ◽  
Oscar Barambones ◽  
Isidro Calvo ◽  
Javier Velasco
Keyword(s):  
Pid Control ◽  
Single Neuron ◽  
Absolute Error ◽  
Linear Effect ◽  
Learning Rules ◽  
Artificial Neural ◽  
Single Neuron Pid ◽  
Artificial Neural Network Ann ◽  
And Control ◽  
Device Operation

This paper presents a deep analysis of different feed-forward (FF) techniques combined with two different proportional-integral-derivative (PID) control to guide a real piezoelectric actuator (PEA). These devices are well known for a non-linear effect called “hysteresis” which generates an undesirable performance during the device operation. First, the PEA was analysed under real experiments to determine the response with different frequencies and voltages. Secondly, a voltage and frequency inputs were chosen and a study of different control approaches was performed using a conventional PID in close-loop, adding a linear compensation and a FF with the same PID and an artificial neural network (ANN). Finally, the best result was contrasted with an adaptive PID which used a single neuron (SNPID) combined with Hebbs rule to update its parameters. Results were analysed in terms of guidance, error and control signal whereas the performance was evaluated with the integral of the absolute error (IAE). Experiments showed that the FF-ANN compensation combined with an SNPID was the most efficient.

Artificial Neural Networks Used As a Rule Selector for Fuzzy Logic Controllers

1993 ◽  
Author(s):  
Shou-Heng Huang ◽  
Ron M. Nelson
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Absolute Error ◽  
Operating Conditions ◽  
Delta Rule ◽  
Rule Sets ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Set Up ◽  
And Control

Abstract A feedforward, three-layer, partially-connected artificial neural network (ANN) is proposed to be used as a rule selector for a rule-based fuzzy logic controller. This will allow the controller to adapt to various control modes and operating conditions for different plants. A principal advantage of an ANN over a look up table is that the ANN can make good estimates to fill in for missing data. The control modes, operating conditions, and control rule sets are encoded into binary numbers as the inputs and outputs for the ANN. The General Delta Rule is used in the backpropagation learning process to update the ANN weights. The proposed ANN has a simple topological structure and results in a simple analysis and relatively easy implementation. The average square error and the maximal absolute error are used to judge if the correct connections between neurons are set up. Computer simulations are used to demonstrate the effectiveness of this ANN as a rule selector.

scholarly journals Artificial Neural Network Based shafts surface pressures Analysis

2021 ◽  
Vol 2089 (1) ◽  
pp. 012046
Author(s):  
B V Ramana Murthy ◽  
Vuppu Padmakar ◽  
B N S M Chandrika ◽  
Satya Prasad Lanka
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Neural System ◽  
Scientific Model ◽  
System Models ◽  
Learning Rules ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Forecast Quality

Abstract This paper exhibits a development of an Artificial Neural Network (ANN) as an instrument for investigation of various parameters of a framework. ANN comprises of various layers of straightforward handling components called as neurons. The neuron performs two capacities, to be specific, assortment of sources of info and age of a yield. Utilization of ANN gives diagram of the hypothesis, learning rules, and uses of the most significant neural system models, definitions and style of Computation. The scientific model of system illuminates the idea of sources of info, loads, adding capacity, actuation work and yields. At that point ANN chooses the sort of learning for modification of loads with change in parameters. At long last the examination of a framework is finished by ANN execution and ANN preparing and forecast quality.

Determination of number of check dams by artificial neural networks in arid regions of Iran

2015 ◽  
Vol 72 (6) ◽  
pp. 952-959 ◽  
Author(s):  
Seyed Ali Asghar Hashemi ◽  
Hamed Kashi
Keyword(s):  
Curve Number ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Effective Parameters ◽  
Check Dams ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Mlp Model

An artificial neural network (ANN) model with six hydrological factors including time of concentration (TC), curve number, slope, imperviousness, area and input discharge as input parameters and number of check dams (NCD) as output parameters was developed and created using GIS and field surveys. The performance of this model was assessed by the coefficient of determination R2, root mean square error (RMSE), values account and mean absolute error (MAE). The results showed that the computed values of NCD using ANN with a multi-layer perceptron (MLP) model regarding RMSE, MAE, values adjustment factor (VAF), and R2 (1.75, 1.25, 90.74, and 0.97) for training, (1.34, 0.89, 97.52, and 0.99) for validation and (0.53, 0.8, 98.32, and 0.99) for test stage, respectively, were in close agreement with their respective values in the watershed. Finally, the sensitivity analysis showed that the area, TC and curve number were the most effective parameters in estimating the number of check dams.

Stability Analysis of Bridge Crane Hoists

2014 ◽  
Vol 543-547 ◽  
pp. 111-114
Author(s):  
Yun Yan Song
Keyword(s):  
Control Strategy ◽  
Pid Control ◽  
Single Neuron ◽  
Bridge Crane ◽  
Matlab Simulation ◽  
Factors Affecting ◽  
Overhead Cranes ◽  
Depth Study ◽  
Main Factors ◽  
Single Neuron Pid

Hoist bridge crane swinging phenomenon often occurs during the operation, making the work more difficult, there is a certain danger. To solve this problem, anti-sway system for overhead cranes depth study to identify the main factors affecting heavy lifting swinging; proposed single neuron PID control strategy, while using MATLAB simulation used to verify the reliability of the method.

Application of Single Neuron PID Control Based on Variable Scale Algorithm in Tar-Ammonia Separation

2010 ◽  
Vol 139-141 ◽  
pp. 1945-1949
Author(s):  
Tian Pei Zhou ◽  
Wen Fang Huang
Keyword(s):  
Pid Control ◽  
Control Algorithm ◽  
Single Neuron ◽  
Control Method ◽  
Optimization Method ◽  
Separation Process ◽  
Control Effect ◽  
Environment Change ◽  
Good Convergence ◽  
Single Neuron Pid

In the process of recycling chemical product in coking object, ammonia and tar were indispensable both metallurgy and agriculture, so the control of separation process for tar-ammonia was one of the most important control problems. Due to the density difference between the tar and ammonia was greater, easier to separate, the control method based on PID was used in field at present. But the control effect of traditional PID was not good because of environment change and fluctuation in material composition. Separation process for tar-ammonia was analyzed firstly, in view of the shortcoming of traditional PID control algorithm, single neuron PID control algorithm based on variable scale method was adopted through using optimization method. Detailed algorithm steps were designed and applied to tar-ammonia separation system. Simulation results show that by comparison with traditional PID algorithm, the algorithm have the following advantages: faster learning speed, shorter adjusted time and good convergence performance.

scholarly journals Dengue Outbreak Prediction Based on Artificial Neural Networking Model Using Climatic Parameters

2020 ◽  
Author(s):  
Biplab Ghosh ◽  
Monika Soni
Keyword(s):  
Dengue Fever ◽  
Meteorological Parameters ◽  
Ann Model ◽  
Data Set ◽  
Long Run ◽  
Modelling Techniques ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
And Control ◽  
The Relationship

Abstract Background: Dengue fever is a vector-borne tropical disease radically amplified by 30 times in occurrence between 1960 and 2010. The upsurge is considered to be because of urbanization, population growth and climate change. Therefore, Meteorological parameters (temperature, precipitation and relative humidity) have impact on the occurrence and outbreaks of dengue fever. There are not many studies that enumerate the relationship between the dengue cases in a particular locality and the meteorological parameters. This study explores the relationship between the dengue cases and the meteorological parameters. In prevalent localities, it is essential to alleviate the outbreaks using modelling techniques for better disease control.Methods: An artificial neural network (ANN) model was developed for predicting the number of dengue cases by knowing the meteorological parameters. The model was trained with 7 years of dengue fever data of Kamrup and Lakhimpur district of Assam, India. The practicality of the model was corroborated using independent data set with satisfactory outcomes. Findings: It was apparent from the sensitivity analysis that precipitation is more sensitive to the number of dengue cases than other meteorological parameters. Conclusion: This model would assist dengue fever alleviation and control in the long run.

Alzheimer’s Disease Recognition with Artificial Neural Networks

2013 ◽  
pp. 102-118 ◽  
Author(s):  
Pedro Miguel Rodrigues ◽  
João Paulo Teixeira
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Memory Loss ◽  
Eeg Signals ◽  
Intellectual Abilities ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Short Time ◽  
And Control ◽  
Electroencephalogram Eeg

Alzheimer’s Disease (AD) is the most common cause of dementia, and is well known for its affect on memory loss and other intellectual abilities. The Electroencephalogram (EEG) has been used as a diagnosis tool for dementia for several decades. The main objective of this work was to develop an Artificial Neural Network (ANN) to classify EEG signals between AD patients and control subjects. For this purpose, two different methodologies and variations were used. The Short Time Fourier Transform (STFT) was applied to one of the methodologies and the Wavelet Transform (WT) was applied to the other methodology. The studied features of the EEG signals were the Relative Power in conventional EEG bands and their associated Spectral Ratios (r1, r2, r3, and r4). The best classification was performed by the ANN using the WT Biorthogonal 3.5 with AROC of 0.97, Sensitivity of 92.1%, Specificity of 90.8%, and 91.5% of Accuracy.

Sign in / Sign up

Export Citation Format

Share Document