Artificial Neural Networks - Industrial and Control Engineering Applications

doi:10.5772/2041

ARTIFICIAL NEURAL NETWORKS INDUSTRIAL AND CONTROL ENGINEERING APPLICATIONS

Kenji Suzuki

https://doi.org/10.5772/2041

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490 pages

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Abstract

Artificial neural networks may probably be the single most successful technology in the last two decades which has been widely used in a large variety of applications. The purpose of this book is to provide recent advances of artificial neural networks in industrial and control engineering applications. The book begins with a review of applications of artificial neural networks in textile industries. Particular applications in textile industries follow. Parts continue with applications in materials science and industry such as material identification, and estimation of material property and state, food industry such as meat, electric and power industry such as batteries and power systems, mechanical engineering such as engines and machines, and control and robotic engineering such as system control and identification, fault diagnosis systems, and robot manipulation. Thus, this book will be a fundamental source of recent advances and applications of artificial neural networks in industrial and control engineering areas. The target audience includes professors and students in engineering schools, and researchers and engineers in industries.

Figures (404)

Table 1. Properties of jute, polypropylene and polyester fibres

Artificial Neural Networks - Industrial and Control Engineering Applications

tig. 1. Neural architecture of the fabric property

Table 3. Fabric samples for development of Emperical and ANN models

Modelling of Needle-Punched Nonwoven Fabric Properties Using Artificial Neural Network

Modelling of Needle-Punched Nonwoven Fabric Properties Using Artificial Neural Network
Table 7. Experimental and predicted initial modulus values by empirical and ANN models — Modelling of Needle-Punched Nonwoven Fabric Properties Using Artificial Neural Network Table 7. Experimental and predicted initial modulus values by empirical and ANN models

Table 8. Experimental verification of predicted results (tenacity and initial modulus)

Artificial Neural Networks - Industrial and Control Engineering Applications
Table 8. Experimental verification of predicted results (tenacity and initial modulus) — Artificial Neural Networks - Industrial and Control Engineering Applications Table 8. Experimental verification of predicted results (tenacity and initial modulus)

Table 11. Experimental verification of predicted results of air permeability values

Artificial Neural Networks - Industrial and Control Engineering Applications
Table 11. Experimental verification of predicted results of air permeability values — Artificial Neural Networks - Industrial and Control Engineering Applications Table 11. Experimental verification of predicted results of air permeability values

Fig. 1. Experimental configuration of a LIBS system.

Fig. 3. Examples of LIBS spectra for materials with similar composition.

Fig. 4. Basic structure of an artificial neural network.

Artificial Neural Networks for Material Identification, Mineralogy and
Analytical Geochemistry Based on Laser-Induced Breakdown Spectroscop
Fig. 7. Natural rock & mineral samples and their powder tablets counterparts. — Artificial Neural Networks for Material Identification, Mineralogy and Analytical Geochemistry Based on Laser-Induced Breakdown Spectroscop Fig. 7. Natural rock & mineral samples and their powder tablets counterparts.

Fig. 7. Natural rock & mineral samples and their powder tablets counterparts.

more detailed quantitative analysis, possibly, in conjunction with data from other sensors.

Artificial Neural Networks - Industrial and Control Engineering Applications
Fig. 15. A comparison of the validation performance between a typical ANN with
conventional training (a) and the ANN with constructive training (b). — Artificial Neural Networks - Industrial and Control Engineering Applications Fig. 15. A comparison of the validation performance between a typical ANN with conventional training (a) and the ANN with constructive training (b).

For the calibration curve method, the deviation 1s mainly due to the serious matrix effects of

\rtificial Neural Networks for Material Identification, Mineralogy and
\nalytical Geochemistry Based on Laser-Induced Breakdown Spectroscopy — \rtificial Neural Networks for Material Identification, Mineralogy and \nalytical Geochemistry Based on Laser-Induced Breakdown Spectroscopy

Fig. 6. Regression analysis of of approximated K' based on Ea. 7 for test data.

Artificial Neural Networks - Industrial and Control Engineering Applications
Table 1. The compositions and mechanical properties of ZrO2/TiBz/ Al,O3 ceramic material — Artificial Neural Networks - Industrial and Control Engineering Applications Table 1. The compositions and mechanical properties of ZrO2/TiBz/ Al,O3 ceramic material

Table 1. The compositions and mechanical properties of ZrO2/TiBz/ Al,O3 ceramic material

Table 4. The predicted results of standard BP algorithm in compositions optimization

Artificial Neural Networks - Industrial and Control Engineering Applications
Table 4. The predicted results of standard BP algorithm in compositions optimization — Artificial Neural Networks - Industrial and Control Engineering Applications Table 4. The predicted results of standard BP algorithm in compositions optimization

Table 5. The predicted results of GA-BP II algorithm in compositions optimization

Fig. 3. The curve of square error and fitness of GA-BP II in compositions optimization

Artificial Neural Networks - Industrial and Control Engineering Applications
Fig. 3. The curve of square error and fitness of GA-BP II in compositions optimization — Artificial Neural Networks - Industrial and Control Engineering Applications Fig. 3. The curve of square error and fitness of GA-BP II in compositions optimization

idicates that the BP neural network has 8 iterations convergence to the specified accuracy.
he elapsed-time is 129.939s and MSE is 0.1491. — idicates that the BP neural network has 8 iterations convergence to the specified accuracy. he elapsed-time is 129.939s and MSE is 0.1491.

Optimum Design and Application of Nano-Micro-Composite Ceramic Tool and
Die Materials with Improved Back Propagation Neural Network — Optimum Design and Application of Nano-Micro-Composite Ceramic Tool and Die Materials with Improved Back Propagation Neural Network

Artificial Neural Networks - Industrial and Control Engineering Applications
Fig. 3. Schematic architecture of Artificial Neural Network model. — Artificial Neural Networks - Industrial and Control Engineering Applications Fig. 3. Schematic architecture of Artificial Neural Network model.

Fig. 4. One sample of data, microstructure of produced steel with ASTM grain size no. 9.0.

Application of Bayesian Neural Networks to
Predict Strength and Grain Size of Hot Strip Low Carbon Steel:
Table 1. Input and output parameter information — Application of Bayesian Neural Networks to Predict Strength and Grain Size of Hot Strip Low Carbon Steel: Table 1. Input and output parameter information

Table 1. Input and output parameter information

Fig. 5. Behavior of tensile strength model on (a) training data (b) test data.

Adaptive Neuro-Fuzzy Inference System Prediction
of Calorific Value Based on the Analysis of U.S. Coal:
Table 1. Number of samples and range of GCV (as-received) for different U.S. states — Adaptive Neuro-Fuzzy Inference System Prediction of Calorific Value Based on the Analysis of U.S. Coal: Table 1. Number of samples and range of GCV (as-received) for different U.S. states

Table 1. Number of samples and range of GCV (as-received) for different U.S. states
Table 2. Ranges of proximate and ultimate analyses of coal samples (as-received) — Table 1. Number of samples and range of GCV (as-received) for different U.S. states Table 2. Ranges of proximate and ultimate analyses of coal samples (as-received)

Estimated deviations of GCV from target values for equations (4) through (6) are shown il
Table 3. — Estimated deviations of GCV from target values for equations (4) through (6) are shown il Table 3.

Adaptive Neuro-Fuzzy Inference System Prediction
of Calorific Value Based on the Analysis of U.S. Coals — Adaptive Neuro-Fuzzy Inference System Prediction of Calorific Value Based on the Analysis of U.S. Coals

Table 6. Estimation of deviations of GCV from target values for neuro-fuzzy models

Artificial Neural Network Applied for Detecting the
Saturation Level in the Magnetic Core of a Welding Transforme — Artificial Neural Network Applied for Detecting the Saturation Level in the Magnetic Core of a Welding Transforme

Table 1. Parameters of RSWS dynamic model

Artificial Neural Network Applied for Detecting the
Saturation Level in the Magnetic Core of a Welding Transforme
Table 2. Nonlinear characteristics of the output rectifier diodes Dj - (dip1) and D2 - (dip2) — Artificial Neural Network Applied for Detecting the Saturation Level in the Magnetic Core of a Welding Transforme Table 2. Nonlinear characteristics of the output rectifier diodes Dj - (dip1) and D2 - (dip2)

Table 3. Parameters for symmetrical behaviour of the resistance spot welding system

Fig. 3. Symmetrical behaviour of the resistance spot welding system

Artificial Neural Networks - Industrial and Control Engineering Applications
Fig. 3. Symmetrical behaviour of the resistance spot welding system — Artificial Neural Networks - Industrial and Control Engineering Applications Fig. 3. Symmetrical behaviour of the resistance spot welding system

Fig. 6. The dynamic inductance and maenetically nonlinear iron core characteristic

Fig, 3. Experimental and predicted values for recovery of fiber from DSF

Table 2. Range of variables selected for study of cellulase & xylanase production

Table 3. ANN based _ predicted combinations for optimized production of enzymes by T. reesei
Table 4. ANN based predicted combinations for optimized roduction of enzymes by A.niger, — Table 3. ANN based _ predicted combinations for optimized production of enzymes by T. reesei Table 4. ANN based predicted combinations for optimized roduction of enzymes by A.niger,

Table 3. ANN based _ predicted combinations for optimized production of enzymes by T. reesei

Application of Artificial Neural Networks to Food and Fermentation Technology

Radial basis function networks (RBF networks) represent a special type of ANN, which are
closely related to density estimation methods. A thorough mathematical description of RBF
Fig. 1. The structure of the a) Kohonen ANN, b) CP-ANN, c) BP-ANN and d) RBF network — Radial basis function networks (RBF networks) represent a special type of ANN, which are closely related to density estimation methods. A thorough mathematical description of RBF Fig. 1. The structure of the a) Kohonen ANN, b) CP-ANN, c) BP-ANN and d) RBF network

Application of Artificial Neural Networks in Meat Production and Technology

Table 4. Other applications of ANN in meat science and technology

State of Charge Estimation of Ni-MH battery pack by using ANN

Fig. 6. The output result waveform of the checking sample is input.
Table 1. Different network’s average erro! — Fig. 6. The output result waveform of the checking sample is input. Table 1. Different network’s average erro!

State of Charge Estimation of Ni-MH battery pack by using ANN
Fig. 6. The output result waveform of the checking sample is input. — State of Charge Estimation of Ni-MH battery pack by using ANN Fig. 6. The output result waveform of the checking sample is input.

Fig. 8. Actual structure of neural network

‘ia, 9. The forecasting result of ANN as set the different SOC initial value

Figure 1 depicts the ADALINE structure to track fundamental frequency which is a
proposed in this section.
\ssume that the voltage waveform of power system comprises unknown fundamental,
larmonics and decaying DC offset components as: — Figure 1 depicts the ADALINE structure to track fundamental frequency which is a proposed in this section. \ssume that the voltage waveform of power system comprises unknown fundamental, larmonics and decaying DC offset components as:

Artificial Neural Networks - Industrial and Control Engineering Applications
Fig. 2. Impulse response of the Hamming window with 20 Hz cut frequency — Artificial Neural Networks - Industrial and Control Engineering Applications Fig. 2. Impulse response of the Hamming window with 20 Hz cut frequency

The weight matrix W in this case has only one row. The network output is

have estimation with less than one percent error 1s represented in lable I.
Fig. 8. Average convergence time (cycles) to track static frequency changes — have estimation with less than one percent error 1s represented in lable I. Fig. 8. Average convergence time (cycles) to track static frequency changes

Table I Samples needed to estimate with 1 percent error for 50-70 frequency range

Fig. 13. Tracked frequency (Hz), case V.C.

Fig. 15. Complex normalized rotating state vector (An, ), case V.C.

Main information on the Marvdasht 230 /66 (kV) station and other substation supplied by
this station is given in Tables II and III.
Table Il Marvdasht substation capacities — Main information on the Marvdasht 230 /66 (kV) station and other substation supplied by this station is given in Tables II and III. Table Il Marvdasht substation capacities

Table Il Marvdasht substation capacities

Application of ANN to Real and Reactive Power Allocation Scheme
Fig. 1. Flow chart of the proposed real and reactive power allocation method — Application of ANN to Real and Reactive Power Allocation Scheme Fig. 1. Flow chart of the proposed real and reactive power allocation method

Table 1. Description of inputs and outputs of the training data for each ANN for real power

Application of ANN to Real and Reactive Power Allocation Scheme

Table 3. The number of samples for training, validation and test set

Fig. 7. Real power allocation method daily load curves for different buses
Fic. 8. Reactive power allocation method daily load curves for different buses — Fig. 7. Real power allocation method daily load curves for different buses Fic. 8. Reactive power allocation method daily load curves for different buses

Fic. 8. Reactive power allocation method daily load curves for different buses

Artificial Neural Networks - Industrial and Control Engineering Applications
Fig. 7. Real power allocation method daily load curves for different buses — Artificial Neural Networks - Industrial and Control Engineering Applications Fig. 7. Real power allocation method daily load curves for different buses

function of individual generators voltage.

Fig. 14. Distribution of reactive power from generator at bus 14 to loads within 168 hours
Table 7. Analysis of reactive power allocation for the 25-bus equivalent system — Fig. 14. Distribution of reactive power from generator at bus 14 to loads within 168 hours Table 7. Analysis of reactive power allocation for the 25-bus equivalent system

Application of ANN to Real and Reactive Power Allocation Scheme
Fig. 14. Distribution of reactive power from generator at bus 14 to loads within 168 hours — Application of ANN to Real and Reactive Power Allocation Scheme Fig. 14. Distribution of reactive power from generator at bus 14 to loads within 168 hours

Fig. 19. Distribution of reactive power from generator at bus 69 to loads within 24 hours
Table 12. Analysis of reactive power allocation for selected generators in the IEEE 118 bu
system — Fig. 19. Distribution of reactive power from generator at bus 69 to loads within 24 hours Table 12. Analysis of reactive power allocation for selected generators in the IEEE 118 bu system

Fig. 19. Distribution of reactive power from generator at bus 69 to loads within 24 hours

Artificial Neural Networks - Industrial and Control Engineering Applications
Table 13. Comparative computational time for MNE, ANN, and RBFEN methods for different
bus system — Artificial Neural Networks - Industrial and Control Engineering Applications Table 13. Comparative computational time for MNE, ANN, and RBFEN methods for different bus system

Artificial Neural Networks - Industrial and Control Engineering Applications
he NLARX model training can be cast as a non-linear unconstrained optimization problem: — Artificial Neural Networks - Industrial and Control Engineering Applications he NLARX model training can be cast as a non-linear unconstrained optimization problem:

Fie. 3. Data set I- NO, emission output generated in NRTC mode

Fig. 5. Pre-processed NO, output signal. Rearranged and composed training and validation set

Fig. 5. Pre-processed NO, output signal. Rearranged and composed training and validation set
‘ig. 6. Data set II training cycle of NO, target outpt — Fig. 5. Pre-processed NO, output signal. Rearranged and composed training and validation set ‘ig. 6. Data set II training cycle of NO, target outpt

Fig. 7. Correlation of measured NO, output with predicted neural network signal

tie. 8. Correlation between measured target output and predicted output with an R2 = 0.95

Fig. 10. Random signal of SOI for training and validation

Table 1. Results for NLARX models for random signal training
Fig. 12. Correlation of engine exhaust temperature with predicted neural network signal — Table 1. Results for NLARX models for random signal training Fig. 12. Correlation of engine exhaust temperature with predicted neural network signal

Fig. 13. Correlation of engine compressor mass-air flow with predicted neural network signal

Fig. 13. Correlation of engine compressor mass-air flow with predicted neural network signal
Fig, 14. Correlation of engine exhaust pressure with predicted neural network signal — Fig. 13. Correlation of engine compressor mass-air flow with predicted neural network signal Fig, 14. Correlation of engine exhaust pressure with predicted neural network signal

Artificial Neural Networks - Industrial and Control Engineering Applications
Fig. 15. Correlation of engine NOx with predicted neural network signal — Artificial Neural Networks - Industrial and Control Engineering Applications Fig. 15. Correlation of engine NOx with predicted neural network signal

Fig. 16. Schematic drawing of a diesel engine and auxiliaries tig. 17. Valve-Lift profile for inlet and exhaust valve

‘ia, 18. Correlation of engine intake manifold pressure with predicted neural network signal

The Applications of Artificial Neural Networks to Engines
Fig. 21. Single NLARX model: measured output signal correlated versus predicted output
signal — The Applications of Artificial Neural Networks to Engines Fig. 21. Single NLARX model: measured output signal correlated versus predicted output signal

The Applications of Artificial Neural Networks to Engines

Artificial Neural Networks - Industrial and Control Engineering Applications
Fig. 1a. Structure of SFF model
Fig. 1b. Structure of ANN model — Artificial Neural Networks - Industrial and Control Engineering Applications Fig. 1a. Structure of SFF model Fig. 1b. Structure of ANN model

Table 1. Fuzzy expressions for inputs and outputs

Fig. 4. Soeed membership function for HSS tool

Fig. 3. Depth of cut membership function
Fig. 4. Soeed membership function for HSS tool — Fig. 3. Depth of cut membership function Fig. 4. Soeed membership function for HSS tool

Artificial Neural Networks - Industrial and Control Engineering Applications
tig, 2. Hardness membership functior — Artificial Neural Networks - Industrial and Control Engineering Applications tig, 2. Hardness membership functior

Fig. 7. Feed membership function (BHN=175-275, HSS)

Fig. 6. Feed membership function (BHN=85-175,HSS)
Fig. 7. Feed membership function (BHN=175-275, HSS) — Fig. 6. Feed membership function (BHN=85-175,HSS) Fig. 7. Feed membership function (BHN=175-275, HSS)

Fig. 6. Feed membership function (BHN=85-175,HSS)

Rule 36: IF hardness is very hard AND depth of cut is very deep THEN speed is very slow and very fast.

Fig. 12. User-friendly viewer for the SFF model (from MATLAB)

A Comparison of Speed-Feed Fuzzy Intelligent
System and ANN for Machinability Data Selection of CNC Machine:
Table 5. Comparison of the results from SFF model with MDH for carbide too. — A Comparison of Speed-Feed Fuzzy Intelligent System and ANN for Machinability Data Selection of CNC Machine: Table 5. Comparison of the results from SFF model with MDH for carbide too.

Fig. 13. Cutting speed for high speed steel

Fig. 13. Cutting speed for high speed steel
Fig, 14. Feed for high speed steel — Fig. 13. Cutting speed for high speed steel Fig, 14. Feed for high speed steel

A Comparison of Speed-Feed Fuzzy Intelligent
System and ANN for Machinability Data Selection of CNC Machine — A Comparison of Speed-Feed Fuzzy Intelligent System and ANN for Machinability Data Selection of CNC Machine

Fig. 17. Performance curves for best tested ANN models

Artificial Neural Networks - Industrial and Control Engineering Applications
Table 11. Comparison of outputs for HSS tool — Artificial Neural Networks - Industrial and Control Engineering Applications Table 11. Comparison of outputs for HSS tool

Table 11. Comparison of outputs for HSS tool

Fig. 20. Comparison of speed values between SFF, ANN and MDH
Fig, 21. Comparison of feed values between SFF, ANN and MDH — Fig. 20. Comparison of speed values between SFF, ANN and MDH Fig, 21. Comparison of feed values between SFF, ANN and MDH

Artificial Neural Networks - Industrial and Control Engineering Applications
Fig. 20. Comparison of speed values between SFF, ANN and MDH — Artificial Neural Networks - Industrial and Control Engineering Applications Fig. 20. Comparison of speed values between SFF, ANN and MDH

Artificial Neural Networks - Industrial and Control Engineering Applications
Fig. 20. Comparison of speed values between SFF, ANN and MDH
Fig, 21. Comparison of feed values between SFF, ANN and MDH — Artificial Neural Networks - Industrial and Control Engineering Applications Fig. 20. Comparison of speed values between SFF, ANN and MDH Fig, 21. Comparison of feed values between SFF, ANN and MDH

presented in [Rivera et al., 1986]. IMC for nonlinear systems is introduced in [Economou et al.,
1986] and IMC with neural networks is described e.g. in [Norgaard et al., 2000]. — presented in [Rivera et al., 1986]. IMC for nonlinear systems is introduced in [Economou et al., 1986] and IMC with neural networks is described e.g. in [Norgaard et al., 2000].

Artificial Neural Networks - Industrial and Control Engineering Applications
Output prediction y(k+i) is possible to calculate by multiplying the equation (9) by the
inverse matrix A -1, equation (10). — Artificial Neural Networks - Industrial and Control Engineering Applications Output prediction y(k+i) is possible to calculate by multiplying the equation (9) by the inverse matrix A -1, equation (10).

Artificial Neural Network — Possible Approach to Nonlinear System Contro

Control law can be transformed to equation (16).

tig. 4. Controller tuning online using neural network

Artificial Neural Network — Possible Approach to Nonlinear System Contro
Fig. 5. Control performance with PI controller - Training set experiment — Artificial Neural Network — Possible Approach to Nonlinear System Contro Fig. 5. Control performance with PI controller - Training set experiment

Fig. 5. Control performance with PI controller - Training set experiment

Artificial Neural Network — Possible Approach to Nonlinear System Control

Fig. 10. Neural IMC control response
Fig. 11. Classical IMC control response — Fig. 10. Neural IMC control response Fig. 11. Classical IMC control response

Artificial Neural Network — Possible Approach to Nonlinear System Contro
Fig. 10. Neural IMC control response — Artificial Neural Network — Possible Approach to Nonlinear System Contro Fig. 10. Neural IMC control response

Fig. 12. Neural Network Predictive control response
Fig. 13. Piecewise Linearized Model Predictive control response — Fig. 12. Neural Network Predictive control response Fig. 13. Piecewise Linearized Model Predictive control response

Artificial Neural Networks - Industrial and Control Engineering Applications
Fig. 12. Neural Network Predictive control response — Artificial Neural Networks - Industrial and Control Engineering Applications Fig. 12. Neural Network Predictive control response

Artificial Neural Network — Possible Approach to Nonlinear System Contro
Fig. 14. Discrete controller tuned online — Artificial Neural Network — Possible Approach to Nonlinear System Contro Fig. 14. Discrete controller tuned online

Direct Neural Network Control via Inverse Modelling: Application on Induction Motors

Fig. 12. Speed error due to a step reference signal
tig. 13. System response to a square wave reference signa — Fig. 12. Speed error due to a step reference signal tig. 13. System response to a square wave reference signa

Fig. 12. Speed error due to a step reference signal

Fig. 14. System response to a sine wave reference signal

Fig. 16. Speed response under load torque conditior

System Identification of NN-based Model Reference Control of RUAV during Hover
Fig. 2. UAV Architecture: Hardware-in-the-loop Simulation — System Identification of NN-based Model Reference Control of RUAV during Hover Fig. 2. UAV Architecture: Hardware-in-the-loop Simulation

Artificial Neural Networks - Industrial and Control Engineering Applications
Fig. 5. Flow of Control System Design with System Identification — Artificial Neural Networks - Industrial and Control Engineering Applications Fig. 5. Flow of Control System Design with System Identification

Fig. 5. Flow of Control System Design with System Identification

Fig. 7. Control of a MIMO plant using NN controller

System Identification of NN-based Model Reference Control of RUAV during Hover

Artificial Neural Networks - Industrial and Control Engineering Applications
Table 1. Model states. — Artificial Neural Networks - Industrial and Control Engineering Applications Table 1. Model states.

System Identification of NN-based Model Reference Control of RUAV during Hove!
The descriptions of all parameters used in Eq. 19-20 are shown in Table 3 — System Identification of NN-based Model Reference Control of RUAV during Hove! The descriptions of all parameters used in Eq. 19-20 are shown in Table 3

The descriptions of all parameters used in Eq. 19-20 are shown in Table 3

Table 4. Eignevalues of the helicopter system

System Identification of NN-based Model Reference Control of RUAV during Hover
Fig. 13. Output response with network response in Longitudinal dynamics mode — System Identification of NN-based Model Reference Control of RUAV during Hover Fig. 13. Output response with network response in Longitudinal dynamics mode

ANG O;,;, Ong OUt the Gependency rate 1s very less.

System Identification of NN-based Model Reference Control of RUAV during Hover
The histogram of prediction error is shown in Fig. 18. — System Identification of NN-based Model Reference Control of RUAV during Hover The histogram of prediction error is shown in Fig. 18.

Artificial Neural Networks - Industrial and Control Engineering Applications
Fig, 18. Histogram of Prediction errors in Longitudinal Mode — Artificial Neural Networks - Industrial and Control Engineering Applications Fig, 18. Histogram of Prediction errors in Longitudinal Mode

Intelligent Vibration Signal Diagnostic System Using Artificial Neural Network

Intelligent Vibration Signal Diagnostic System Using Artificial Neural Network
Table 1. Test ball bearings — Intelligent Vibration Signal Diagnostic System Using Artificial Neural Network Table 1. Test ball bearings

Fig. 8. AR parameters patterns of defective bearings

(Adapted from Carpenter and Grossberg, 1987b

Conditioning Monitoring and Fault Diagnosis for a
Servo-Pneumatic System with Artificial Neural Network Algorithn
Fig. 5. Gripper Place Sensor — Conditioning Monitoring and Fault Diagnosis for a Servo-Pneumatic System with Artificial Neural Network Algorithn Fig. 5. Gripper Place Sensor

Fig. 10. The FFN type ANN estimations for the training cases

Fig. 11. The FFN type ANN estimations for the test cases.

Conditioning Monitoring and Fault Diagnosis for a
Servo-Pneumatic System with Artificial Neural Network Algorithrr
Table 2. The estimated categories with the ART 2 algorithm
ART2 - Number of Categories at different vigilances — Conditioning Monitoring and Fault Diagnosis for a Servo-Pneumatic System with Artificial Neural Network Algorithrr Table 2. The estimated categories with the ART 2 algorithm ART2 - Number of Categories at different vigilances

Table 2. The estimated categories with the ART 2 algorithm
ART2 - Number of Categories at different vigilances
Fig. 13. The graphical presentation of the ART2 results in the Table 1. — Table 2. The estimated categories with the ART 2 algorithm ART2 - Number of Categories at different vigilances Fig. 13. The graphical presentation of the ART2 results in the Table 1.

Fuzzy ART - Number of Categories at different vigilances
Fig. 13. The estimations of the Fuzzy ART — Fuzzy ART - Number of Categories at different vigilances Fig. 13. The estimations of the Fuzzy ART

Fig. 2. Schematic diagram for the biological neuron

Neural Networks’ Based Inverse Kinematics Solution
for Serial Robot Manipulators Passing Through Singularities
Fig. 2. Schematic diagram for the biological neuron — Neural Networks’ Based Inverse Kinematics Solution for Serial Robot Manipulators Passing Through Singularities Fig. 2. Schematic diagram for the biological neuron

Artificial Neural Networks - Industrial and Control Engineering Applications
Fig, 4. Schematic diagram of a multilayer feedforward neural network — Artificial Neural Networks - Industrial and Control Engineering Applications Fig, 4. Schematic diagram of a multilayer feedforward neural network

Neural Networks’ Based Inverse Kinematics Solution
for Serial Robot Manipulators Passing Through Singularities — Neural Networks’ Based Inverse Kinematics Solution for Serial Robot Manipulators Passing Through Singularities

Fig. 6. Information flow through a backpropagation network

Fig. 8. The learning curve for the first configuration

Artificial Neural Networks - Industrial and Control Engineering Applications
‘ig. 9. The topology of the second configuration network — Artificial Neural Networks - Industrial and Control Engineering Applications ‘ig. 9. The topology of the second configuration network

‘ig. 9. The topology of the second configuration network

Artificial Neural Networks - Industrial and Control Engineering Applications
Fig. 12. Trajectory tracking for both configurations compared to each other after the training
was finished for the Y coordinate — Artificial Neural Networks - Industrial and Control Engineering Applications Fig. 12. Trajectory tracking for both configurations compared to each other after the training was finished for the Y coordinate

Fig. 12. Trajectory tracking for both configurations compared to each other after the training
was finished for the Y coordinate — Fig. 12. Trajectory tracking for both configurations compared to each other after the training was finished for the Y coordinate

Table 3 shows the percentages of error for the testing data set for each joint during
simulation stage.
Table 3. Error percentages obtained for testing data through simulation stage — Table 3 shows the percentages of error for the testing data set for each joint during simulation stage. Table 3. Error percentages obtained for testing data through simulation stage

Artificial Neural Networks - Industrial and Control Engineering Applications
Fig. 14. Predicted trajectory for the Y coordinate — Artificial Neural Networks - Industrial and Control Engineering Applications Fig. 14. Predicted trajectory for the Y coordinate

Fig. 14. Predicted trajectory for the Y coordinate
Fig. 15. Predicted trajectory for the Z coordinate — Fig. 14. Predicted trajectory for the Y coordinate Fig. 15. Predicted trajectory for the Z coordinate

The error percentages in the experimental data are shown in table 4.
Table 4. Error percentages obtained for testing data through experimental stage — The error percentages in the experimental data are shown in table 4. Table 4. Error percentages obtained for testing data through experimental stage

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