Recent changes to About ASPI Kit

About ASPI Kit modified by aspicc

aspicc — Mon, 30 Jun 2014 22:20:15 -0000

--- v23
+++ v24
@@ -79,9 +79,9 @@
 [[img src=Picture4.jpg]]
    **Principal of PCA Analysis - Process Scheme**

-For this given process, the output is not only influenced by the process input, but also due to the addition of certain pertubations within the process output. In this case these perturbations can be considered in the form of e.g. signal errors, thus the goal here is to analyse how the output of the process is influenced by the process input and for the purposes of adaptive modelling of such process behaviour, to rebuild the NN model with an input vector that most significantly resembles the main dynamics of the process with supressed effects due to these perturbations, hence the purpose of using this PCA module. For more comprehensive theory behind the principle of PCA analysis in the sense of adaptive modeling, please refer to the references section of this documentation.
+For this given process, the output is not only influenced by the process input, but also due to the addition of certain pertubations within the process output. In this case these perturbations can be considered in the form of e.g. signal errors, thus the goal here is to analyse how the output of the process is influenced by the process input and for the purposes of adaptive modelling of such process behaviour, to rebuild the NN model with an input vector that most significantly resembles the main dynamics of the process with supressed effects due to these perturbations, hence the purpose of using this PCA module. For more comprehensive theory behind the principle of PCA analysis in the sense of adaptive modelling, please refer to the references section of this documentation.

-On loading the respective data for the process under investigation, the next step is to setup the calculation and modeling of the process via PCA. The edits nu and ny, are enabled to set the previous amount of samples that are considered into the NN model. Tnu represents the time delay between the process input and output, with the ratio of dt/dthu, being the sampling rate to which the NN model is trained in modeling the behaviour of the considered process. For consideration in the PCA algorithm, the "neig" edit controls how many eigenvectors are taken for construction of the X back matrix or reconstructed input matrix for the NN model. After all the parameters are filled, the "Data after PCA Analysis" button provides a comparison of the NN model resembling the process output with that of the original data, with effects due to the additional perturbations within the process.
+On loading the respective data for the process under investigation, the next step is to setup the calculation and modelling of the process via PCA. The edits nu and ny, are enabled to set the previous amount of samples that are considered into the NN model. Tnu represents the time delay between the process input and output, with the ratio of dt/dthu, being the sampling rate to which the NN model is trained in modelling the behaviour of the considered process. For consideration in the PCA algorithm, the "neig" edit controls how many eigenvectors are taken for construction of the X back matrix or reconstructed input matrix for the NN model. After all the parameters are filled, the "Data after PCA Analysis" button provides a comparison of the NN model resembling the process output with that of the original data, with effects due to the additional perturbations within the process.

 3.1.2 PCA Analysis for Multiple Input Multiple Output (MIMO) Processes
 ------------------------------------------------------------------
@@ -92,7 +92,7 @@

 Thus, the ASPI Kit features two key components for analysis of such MIMO processes. The first element is the Plant Identification. Here all process data is loaded with definition of the individual time delays for the process inputs with respect to a given process output (represented in samples). Further to this, any individual output delays i.e. due to sensor delay, is defined. The "neig" edit adjusts the number of eigenvectors considered for the reconstruction of the NN input vector. 

-It is important that the "Data After PCA Analysis" button is pressed first, before and further functions are used. This is due to the input vector x, not yet being defined as the goal is to first process the data for an input vector of the NN model, such to suppress the effects in the process output due to the pertubations within the process. Once computed, the NN architectures via LNU and QNU may be visualised to analyse modeling of the engineering process both with and without the effects of the PCA data processing.
+It is important that the "Data After PCA Analysis" button is pressed first, before and further functions are used. This is due to the input vector x, not yet being defined as the goal is to first process the data for an input vector of the NN model, such to suppress the effects in the process output due to the pertubations within the process. Once computed, the NN architectures via LNU and QNU may be visualised to analyse modelling of the engineering process both with and without the effects of the PCA data processing.

 [[img src=PCAMIMO.jpg]]
    **Features of the PCA Analysis for MIMO Process Data Module**

About ASPI Kit modified by aspicc

aspicc — Mon, 30 Jun 2014 20:38:33 -0000

--- v22
+++ v23
@@ -87,9 +87,15 @@
 ------------------------------------------------------------------
 Apart from analysis of SISO engineering processes, the ASPI Kit is also aimed towards PCA analysis in the sense of MIMO systems as well. This module considers that for a given process output affected by the addition of perturbations i.e. due to sensor error, due to various process inputs, each process input may have its own respective time delay in relation to the process response i.e. process output. Further to this, the process output in itself may also have certain delays i.e. sensor delay.

+[[img src=PCAMIMO2.jpg]]
+   **Concept of Considered MIMO process for PCA Analysis**
+
 Thus, the ASPI Kit features two key components for analysis of such MIMO processes. The first element is the Plant Identification. Here all process data is loaded with definition of the individual time delays for the process inputs with respect to a given process output (represented in samples). Further to this, any individual output delays i.e. due to sensor delay, is defined. The "neig" edit adjusts the number of eigenvectors considered for the reconstruction of the NN input vector. 

-It is important that the "Data After PCA Analysis" button is pressed first, before and further functions are used. This is due to the input vector x, not yet being defined as the goal is to first process the data for an input vector of the NN model, such to suppress the effects in the process output due to the pertubations within the process. Once computed, the NN architectures via LNU and QNU may be visualised to analyse modeling of the engineering process both with and without the effects of the PCA data processing. 
+It is important that the "Data After PCA Analysis" button is pressed first, before and further functions are used. This is due to the input vector x, not yet being defined as the goal is to first process the data for an input vector of the NN model, such to suppress the effects in the process output due to the pertubations within the process. Once computed, the NN architectures via LNU and QNU may be visualised to analyse modeling of the engineering process both with and without the effects of the PCA data processing.
+
+[[img src=PCAMIMO.jpg]]
+   **Features of the PCA Analysis for MIMO Process Data Module** 

 The second key field is the "Plot Data" functions. After the NN models are computed, their respective NN weights are saved in within the ASPI Kit program. If the user wishes to understand how each individual process input effects a particular process output value according to the respective magnitude of the particular process input and at which sample of the process output. The "Visualise Expert System" function for all above NN architecture setups is incorporated.

About ASPI Kit modified by aspicc

aspicc — Mon, 30 Jun 2014 20:34:45 -0000

--- v21
+++ v22
@@ -106,7 +106,7 @@
 9. P.M.Beneš: “Software Application for Adaptive Identification and Controller Tuning”, STČ Conference CTU Prague, Czech Republic, 2013. (Received Best Paper + BOSCH Award). Link: http://stc.fs.cvut.cz/pdf13/2611.pdf?_=1364170340
 10. Bukovsky, I. Learning Entropy: Multiscale Measure for Incremental Learning. Entropy 2013, 15, 4159-4187. (Full text pdf: http://www.mdpi.com/1099-4300/15/10/4159 ). 
 11. Bukovsky, I., Kinsner, W., Bila, J.: „Multiscale Analysis Approach for Novelty Detection in Adaptation Plot“, 3rd Sensor Signal Processing for Defence 2012 (SSPD 2012), Imperial College London, UK, September 24-27, 2012, doi: 10.1049/ic.2012.0114, E-ISBN: 978-1-84919-712-0. 
-12.Bukovsky, I., Bila. J: “Adaptive Evaluation of Complex Dynamic Systems using Low-Dimensional Neural Architectures”, in Advances in Cognitive Informatics and Cognitive Computing, Series: Studies in Computational Intelligence, Vol. 323/2010, eds. D. Zhang, Y. Wang, W. Kinsner, Springer-Verlag Berlin Heidelberg, 2010, ISBN: 978-3-642-16082-0, pp.33-57. 
+12. Bukovsky, I., Bila. J: “Adaptive Evaluation of Complex Dynamic Systems using Low-Dimensional Neural Architectures”, in Advances in Cognitive Informatics and Cognitive Computing, Series: Studies in Computational Intelligence, Vol. 323/2010, eds. D. Zhang, Y. Wang, W. Kinsner, Springer-Verlag Berlin Heidelberg, 2010, ISBN: 978-3-642-16082-0, pp.33-57. 
 13. Bukovsky, I. : Modeling of Complex Dynamic Systems by Nonconventional Artificial Neural Architectures and Adaptive Approach to Evaluation of Chaotic Time Series, Ph.D. THESIS, Faculty of Mechanical Engineering, Czech Technical University in Prague, 2007. 
 14. P. M. Benes et al., "Neural Network Approach to Railway Stand Lateral Skew Control" in Computer Science & Information Technology (CS& IT), Sydney, NSW, Australia, AIRCC, 2014, pp. 327-339
 15. Bukovsky, I. Cejnek, M. Oswald, C. Beneš, P.M.:"Learning Entropy for Novelty Detection - A Cognitive Approach for Adaptive Filters", Sensor Signal Processing for Defence (SSPD), Edinburgh, Scotland, 8th-9th September 2014.

About ASPI Kit modified by aspicc

aspicc — Mon, 30 Jun 2014 19:57:38 -0000

--- v20
+++ v21
@@ -104,5 +104,10 @@
 7. Rodriguez , R., Bukovsky, I., Homma, N.: “Potentials of Quadratic Neural Unit for Applications”, in International Journal of Software Science and Computational Intelligence (IJSSCI) ,vol 3, issue 3, IGI Global, Publishing, Hershey PA, USA ISSN 
 8. Ladislav Smetana:  Nonlinear Neuro-Controller for Automatic Control Laboratory System, Master’s Thesis (sup. Ivo Bukovsky), Czech Tech. Univ. in Prague, 2008
 9. P.M.Beneš: “Software Application for Adaptive Identification and Controller Tuning”, STČ Conference CTU Prague, Czech Republic, 2013. (Received Best Paper + BOSCH Award). Link: http://stc.fs.cvut.cz/pdf13/2611.pdf?_=1364170340
-
+10. Bukovsky, I. Learning Entropy: Multiscale Measure for Incremental Learning. Entropy 2013, 15, 4159-4187. (Full text pdf: http://www.mdpi.com/1099-4300/15/10/4159 ). 
+11. Bukovsky, I., Kinsner, W., Bila, J.: „Multiscale Analysis Approach for Novelty Detection in Adaptation Plot“, 3rd Sensor Signal Processing for Defence 2012 (SSPD 2012), Imperial College London, UK, September 24-27, 2012, doi: 10.1049/ic.2012.0114, E-ISBN: 978-1-84919-712-0. 
+12.Bukovsky, I., Bila. J: “Adaptive Evaluation of Complex Dynamic Systems using Low-Dimensional Neural Architectures”, in Advances in Cognitive Informatics and Cognitive Computing, Series: Studies in Computational Intelligence, Vol. 323/2010, eds. D. Zhang, Y. Wang, W. Kinsner, Springer-Verlag Berlin Heidelberg, 2010, ISBN: 978-3-642-16082-0, pp.33-57. 
+13. Bukovsky, I. : Modeling of Complex Dynamic Systems by Nonconventional Artificial Neural Architectures and Adaptive Approach to Evaluation of Chaotic Time Series, Ph.D. THESIS, Faculty of Mechanical Engineering, Czech Technical University in Prague, 2007. 
+14. P. M. Benes et al., "Neural Network Approach to Railway Stand Lateral Skew Control" in Computer Science & Information Technology (CS& IT), Sydney, NSW, Australia, AIRCC, 2014, pp. 327-339
+15. Bukovsky, I. Cejnek, M. Oswald, C. Beneš, P.M.:"Learning Entropy for Novelty Detection - A Cognitive Approach for Adaptive Filters", Sensor Signal Processing for Defence (SSPD), Edinburgh, Scotland, 8th-9th September 2014.

About ASPI Kit modified by aspicc

aspicc — Mon, 30 Jun 2014 19:40:54 -0000

--- v19
+++ v20
@@ -79,14 +79,19 @@
 [[img src=Picture4.jpg]]
    **Principal of PCA Analysis - Process Scheme**

-For this given process, the output is not only influenced by the process input, but also due to the addition of certain pertubations within the process output. Thus the goal here is to analyse how the output of the process is influenced by the process input and for the purposes of adaptive modelling of such process behaviour, to rebuild the NN model with an input vector that most significantly resembles the main dynamics of the process and hence the purpose of using this PCA module. For more comprehensive theory behind the principle of PCA analysis in the sense of adaptive modeling, please refer to the references section of this documentation.
+For this given process, the output is not only influenced by the process input, but also due to the addition of certain pertubations within the process output. In this case these perturbations can be considered in the form of e.g. signal errors, thus the goal here is to analyse how the output of the process is influenced by the process input and for the purposes of adaptive modelling of such process behaviour, to rebuild the NN model with an input vector that most significantly resembles the main dynamics of the process with supressed effects due to these perturbations, hence the purpose of using this PCA module. For more comprehensive theory behind the principle of PCA analysis in the sense of adaptive modeling, please refer to the references section of this documentation.

-On loading the respective data for the process under investigation, the next step is to setup the calculation and modeling of the process via PCA. The edits nu and ny, are enabled to set the previous amount of samples that are considered into the NN model. Tnu represents the time delay between the process input and output, with the ratio of dt/dthu, being the sampling rate to which the NN model is training in modeling the behaviour of the considered process. After all the parameters are filled, the "Data after PCA Analysis" button provides a comparison of the NN model resembling the process output with that of the original data, with effects due to the additional perturbations within the process.
+On loading the respective data for the process under investigation, the next step is to setup the calculation and modeling of the process via PCA. The edits nu and ny, are enabled to set the previous amount of samples that are considered into the NN model. Tnu represents the time delay between the process input and output, with the ratio of dt/dthu, being the sampling rate to which the NN model is trained in modeling the behaviour of the considered process. For consideration in the PCA algorithm, the "neig" edit controls how many eigenvectors are taken for construction of the X back matrix or reconstructed input matrix for the NN model. After all the parameters are filled, the "Data after PCA Analysis" button provides a comparison of the NN model resembling the process output with that of the original data, with effects due to the additional perturbations within the process.

+3.1.2 PCA Analysis for Multiple Input Multiple Output (MIMO) Processes
+------------------------------------------------------------------
+Apart from analysis of SISO engineering processes, the ASPI Kit is also aimed towards PCA analysis in the sense of MIMO systems as well. This module considers that for a given process output affected by the addition of perturbations i.e. due to sensor error, due to various process inputs, each process input may have its own respective time delay in relation to the process response i.e. process output. Further to this, the process output in itself may also have certain delays i.e. sensor delay. 

+Thus, the ASPI Kit features two key components for analysis of such MIMO processes. The first element is the Plant Identification. Here all process data is loaded with definition of the individual time delays for the process inputs with respect to a given process output (represented in samples). Further to this, any individual output delays i.e. due to sensor delay, is defined. The "neig" edit adjusts the number of eigenvectors considered for the reconstruction of the NN input vector. 

-3.1.2 PCA Analysis for Single Input Single Output (SISO) Processes
-------------------------------------------------------------------
+It is important that the "Data After PCA Analysis" button is pressed first, before and further functions are used. This is due to the input vector x, not yet being defined as the goal is to first process the data for an input vector of the NN model, such to suppress the effects in the process output due to the pertubations within the process. Once computed, the NN architectures via LNU and QNU may be visualised to analyse modeling of the engineering process both with and without the effects of the PCA data processing. 
+
+The second key field is the "Plot Data" functions. After the NN models are computed, their respective NN weights are saved in within the ASPI Kit program. If the user wishes to understand how each individual process input effects a particular process output value according to the respective magnitude of the particular process input and at which sample of the process output. The "Visualise Expert System" function for all above NN architecture setups is incorporated.

 Research Papers by the ASPICC group (References)
 ------------------------------------------------

About ASPI Kit modified by aspicc

aspicc — Mon, 30 Jun 2014 18:45:49 -0000

--- v18
+++ v19
@@ -79,6 +79,9 @@
 [[img src=Picture4.jpg]]
    **Principal of PCA Analysis - Process Scheme**

+For this given process, the output is not only influenced by the process input, but also due to the addition of certain pertubations within the process output. Thus the goal here is to analyse how the output of the process is influenced by the process input and for the purposes of adaptive modelling of such process behaviour, to rebuild the NN model with an input vector that most significantly resembles the main dynamics of the process and hence the purpose of using this PCA module. For more comprehensive theory behind the principle of PCA analysis in the sense of adaptive modeling, please refer to the references section of this documentation.
+
+On loading the respective data for the process under investigation, the next step is to setup the calculation and modeling of the process via PCA. The edits nu and ny, are enabled to set the previous amount of samples that are considered into the NN model. Tnu represents the time delay between the process input and output, with the ratio of dt/dthu, being the sampling rate to which the NN model is training in modeling the behaviour of the considered process. After all the parameters are filled, the "Data after PCA Analysis" button provides a comparison of the NN model resembling the process output with that of the original data, with effects due to the additional perturbations within the process.

About ASPI Kit modified by aspicc

aspicc — Mon, 30 Jun 2014 18:17:10 -0000

--- v17
+++ v18
@@ -61,6 +61,30 @@
 [[img src=ControlP.jpg]]
    **Adaptive Control of Bathyscaphe Engineering Process with (ASPI Kit Output of Adaptive Neuro-Controller)**

+3.Signal Processing Module
+--------------------------
+
+A key area of research within the ASPICC group is not only to use such adaptive methods in the sense of engineering process control, but also as an experimental tool for signal analysis! 
+
+3.1 PCA Process Data Analysis
+-----------------------------
+
+Often in more complex processes there is a need to detect random anomalies within the process data for the purposes distinguishing the main dynamics of a signal or even detection of particular errors within an engineering process. Or furthermore, the need to filter out effects due to additional perturbations within a signal for the purposes of process identification. Thus, the ASPI Kit features such signal analysis tool in the form of a PCA analysis module, with further tools to be added shortly with ongoing research provided by the ASPICC group.
+
+3.1.1 PCA Analysis for Single Input Single Output (SISO) Processes
+------------------------------------------------------------------
+
+This section of the ASPI Kit is focused on analysis of SISO processes. Consider the following scheme:
+
+[[img src=Picture4.jpg]]
+   **Principal of PCA Analysis - Process Scheme**
+
+
+
+
+3.1.2 PCA Analysis for Single Input Single Output (SISO) Processes
+------------------------------------------------------------------
+
 Research Papers by the ASPICC group (References)
 ------------------------------------------------
 1. Gupta, M.M., Liang, J. and Homma, N. (2003), “Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory,” IEEE Press and Wiley-Interscience, published by John Wiley & Sons, Inc.

About ASPI Kit modified by aspicc

aspicc — Sun, 22 Jun 2014 15:32:12 -0000

About ASPI Kit modified by aspicc

aspicc — Sun, 22 Jun 2014 15:30:57 -0000

--- v15
+++ v16
@@ -32,12 +32,15 @@

 The following panels of this program are thus divided into “Plant Identification” and “Neuro- Controller (as a state feedback controller)”. Here the user must fill in the learning rate for usage of gradient descent algorithm and furthermore epochs to tune the respective training algorithms. As the values of the learning rate and number of epochs differ between the RTRL and BPTT methods, separate edits are placed for each within each panel. There is also a feature for the user to define the length of the variables used in the model polynomial equations for the respective controller.

-[[img src=Ident.jpg]]
+[[img src=Howtouse2.jpg]]
+   **ASPI Kit- Features of the AC Kit Module**
+
+[[img src=Ident2.jpg]]
    **Adaptive Identification of Theoretical 2nd order Engineering Process with Noise (ASPI Kit Output of Adaptive Identification)**

 The control may be calculated for the following options : DLNU Plant Identification with LNU Neuro-Controller, DLNU Plant Identification with QNU Neuro-Controller or DQNU Plant Identification with QNU Neuro-Controller. This thus gives the user the option to compare behaviour of the different methods, furthermore allowing the user to tune for most optimal control to their process data.

-[[img src=Control.jpg]]
+[[img src=Control2.jpg]]
    **Adaptive Control of Theoretical 2nd order Engineering Process with Noise (ASPI Kit Output of Adaptive Neuro-Controller)**

About ASPI Kit modified by aspicc

aspicc — Sun, 22 Jun 2014 13:18:22 -0000

--- v14
+++ v15
@@ -32,13 +32,13 @@

 The following panels of this program are thus divided into “Plant Identification” and “Neuro- Controller (as a state feedback controller)”. Here the user must fill in the learning rate for usage of gradient descent algorithm and furthermore epochs to tune the respective training algorithms. As the values of the learning rate and number of epochs differ between the RTRL and BPTT methods, separate edits are placed for each within each panel. There is also a feature for the user to define the length of the variables used in the model polynomial equations for the respective controller.

-[[img src=Ident.jpg]]
-   **Adaptive Identification of Theoretical 2nd order Engineering Process with Noise (ASPI Kit Output of Adaptive Identification)**
+[[img src=Ident.jpg]]
+   **Adaptive Identification of Theoretical 2nd order Engineering Process with Noise (ASPI Kit Output of Adaptive Identification)**

 The control may be calculated for the following options : DLNU Plant Identification with LNU Neuro-Controller, DLNU Plant Identification with QNU Neuro-Controller or DQNU Plant Identification with QNU Neuro-Controller. This thus gives the user the option to compare behaviour of the different methods, furthermore allowing the user to tune for most optimal control to their process data.

-[[img src=Control.jpg]]
-   **Adaptive Control of Theoretical 2nd order Engineering Process with Noise (ASPI Kit Output of Adaptive Neuro-Controller)**
+[[img src=Control.jpg]]
+   **Adaptive Control of Theoretical 2nd order Engineering Process with Noise (ASPI Kit Output of Adaptive Neuro-Controller)**

 2.1.2 AC Kit - Control Optimisation of Plant (Process)
@@ -50,13 +50,13 @@

 An adaptive gain parameter ro, may also be tuned for advantage to certain engineering processes. This parameter is thus multiplied with the newly calculated process input i.e. u = ro.(d - q). If it's desired to disable the adaptive gain parameter, it may be achieved by simply setting the "muro" edit to zero (i.e. muro = 0). 

-[[img src=Adaptcontrolloop.jpg]]
-   **Adaptive Control Loop Principal Implemented in the ASPI Kit**
+[[img src=Adaptcontrolloop.jpg]]
+   **Adaptive Control Loop Principal Implemented in the ASPI Kit**

 Considering the above, the user must save the respective data as follows: "uoutP.txt" - for the process input, "dP.txt" for the desired process behaviour and "youtP.txt" - corresponding to the controlled output of the engineering system. Following this you may then investigate in the same manner whether the engineering process under investigation may be successfully modeled via NN based approaches and furthermore controlled.

-[[img src=ControlP.jpg]]
-   **Adaptive Control of Bathyscaphe Engineering Process with (ASPI Kit Output of Adaptive Neuro-Controller)**
+[[img src=ControlP.jpg]]
+   **Adaptive Control of Bathyscaphe Engineering Process with (ASPI Kit Output of Adaptive Neuro-Controller)**

 Research Papers by the ASPICC group (References)
 ------------------------------------------------