Dynamic non-linear system modelling using wavelet-based soft computing techniques

Amina, M. 2011. Dynamic non-linear system modelling using wavelet-based soft computing techniques. PhD thesis University of Westminster School of Electronics and Computer Science

TitleDynamic non-linear system modelling using wavelet-based soft computing techniques
TypePhD thesis
AuthorsAmina, M.
Abstract

The enormous number of complex systems results in the necessity of high-level and cost-efficient

modelling structures for the operators and system designers. Model-based approaches offer a very

challenging way to integrate a priori knowledge into the procedure. Soft computing based models

in particular, can successfully be applied in cases of highly nonlinear problems. A further reason

for dealing with so called soft computational model based techniques is that in real-world cases,

many times only partial, uncertain and/or inaccurate data is available.

Wavelet-Based soft computing techniques are considered, as one of the latest trends in system

identification/modelling. This thesis provides a comprehensive synopsis of the main wavelet-based

approaches to model the non-linear dynamical systems in real world problems in conjunction with

possible twists and novelties aiming for more accurate and less complex modelling structure.

Initially, an on-line structure and parameter design has been considered in an adaptive Neuro-

Fuzzy (NF) scheme. The problem of redundant membership functions and consequently fuzzy

rules is circumvented by applying an adaptive structure. The growth of a special type of Fungus

(Monascus ruber van Tieghem) is examined against several other approaches for further

justification of the proposed methodology.

By extending the line of research, two Morlet Wavelet Neural Network (WNN) structures have

been introduced. Increasing the accuracy and decreasing the computational cost are both the

primary targets of proposed novelties. Modifying the synoptic weights by replacing them with

Linear Combination Weights (LCW) and also imposing a Hybrid Learning Algorithm (HLA)

comprising of Gradient Descent (GD) and Recursive Least Square (RLS), are the tools utilised for

the above challenges. These two models differ from the point of view of structure while they share

the same HLA scheme. The second approach contains an additional Multiplication layer, plus its

hidden layer contains several sub-WNNs for each input dimension. The practical superiority of

these extensions is demonstrated by simulation and experimental results on real non-linear

dynamic system; Listeria Monocytogenes survival curves in Ultra-High Temperature (UHT)

whole milk, and consolidated with comprehensive comparison with other suggested schemes.

At the next stage, the extended clustering-based fuzzy version of the proposed WNN schemes, is

presented as the ultimate structure in this thesis. The proposed Fuzzy Wavelet Neural network

(FWNN) benefitted from Gaussian Mixture Models (GMMs) clustering feature, updated by a

modified Expectation-Maximization (EM) algorithm. One of the main aims of this thesis is to illustrate how the GMM-EM scheme could be used not only for detecting useful knowledge from

the data by building accurate regression, but also for the identification of complex systems.

The structure of FWNN is based on the basis of fuzzy rules including wavelet functions in the

consequent parts of rules. In order to improve the function approximation accuracy and general

capability of the FWNN system, an efficient hybrid learning approach is used to adjust the

parameters of dilation, translation, weights, and membership. Extended Kalman Filter (EKF) is

employed for wavelet parameters adjustment together with Weighted Least Square (WLS) which

is dedicated for the Linear Combination Weights fine-tuning. The results of a real-world

application of Short Time Load Forecasting (STLF) further re-enforced the plausibility of the

above technique.

Year2011
FileMahdi_AMINA.pdf
Publication dates
Completed2011

Related outputs

A clustering-based fuzzy-wavelet neural network model for short-term load forecasting
Kodogiannis, V., Amina, M. and Petrounias, I. 2013. A clustering-based fuzzy-wavelet neural network model for short-term load forecasting. International Journal of Neural Systems. 23 (5), p. 1350024.

A hybrid intelligent approach for the prediction of electricity consumption
Amina, M., Kodogiannis, V., Petrounias, I. and Tomtsis, D. 2012. A hybrid intelligent approach for the prediction of electricity consumption. International Journal of Electrical Power and Energy Systems. 43 (1), pp. 99-108.

Identification of the Listeria monocytogenes survival curves in UHT whole milk utilising local linear wavelet neural networks
Amina, M., Kodogiannis, V., Petrounias, I., Lygouras, J.N. and Nychas, G.J.E. 2012. Identification of the Listeria monocytogenes survival curves in UHT whole milk utilising local linear wavelet neural networks. Expert Systems with Applications. 39 (1), pp. 1435-1450.

Application of wavelet neural networks as a non-linear modelling technique in food microbiology
Kodogiannis, V., Amina, M., Lygouras, J.N. and Nychas, G.J.E. 2011. Application of wavelet neural networks as a non-linear modelling technique in food microbiology. in: Borgearo, S.R. (ed.) Animal feed: types, nutrition, and safety Nova Science Publishers. pp. 127-154

Power load forecasting using extended normalised radial basis function networks
Kodogiannis, V., Amina, M. and Lygouras, J.N. 2011. Power load forecasting using extended normalised radial basis function networks. Journal of Computational Methods in Sciences and Engineering. 11 (4), pp. 243-255.

Load forecasting using fuzzy wavelet neural networks
Amina, M. and Kodogiannis, V. 2011. Load forecasting using fuzzy wavelet neural networks. in: Proceedings of the 2011 IEEE International Conference on Fuzzy Systems (FUZZ), Taipei, 27-30 June 2011 IEEE . pp. 1033-1040

Wavelet neural networks for modelling high pressure inactivation kinetics of Listeria monocytogenes in UHT whole milk
Amina, M., Panagou, E.Z., Kodogiannis, V. and Nychas, G.J.E. 2010. Wavelet neural networks for modelling high pressure inactivation kinetics of Listeria monocytogenes in UHT whole milk. Chemometrics and Intelligent Laboratory Systems. 103 (2), pp. 170-183.

Modeling the Listeria monocytogenes survival/death curves using wavelet neural networks
Amina, M., Kodogiannis, V., Revett, K. and Lygouras, J.N. 2010. Modeling the Listeria monocytogenes survival/death curves using wavelet neural networks. in: 2010 International Joint Conference on Neural Networks (IJCNN), Barcelona, Spain, 18-23 July 2010 IEEE . pp. 1-8

Predictive modeling in food mycology using adaptive neuro-fuzzy systems
Amina, M., Kodogiannis, V. and Tarczynski, A. 2009. Predictive modeling in food mycology using adaptive neuro-fuzzy systems. in: IEEE/ACS International Conference on Computer Systems and Applications (AICCSA 2009) IEEE . pp. 821-828

Permalink - https://westminsterresearch.westminster.ac.uk/item/8zwwz/dynamic-non-linear-system-modelling-using-wavelet-based-soft-computing-techniques


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