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

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

Computation Approaches for Continuous Reinforcement Learning Problems

Optimisation theory is at the heart of any control process, where we seek to control the behaviour of a system through a set of actions. Linear control problems have been extensively studied, and optimal control laws have been identified. But the world around us is highly non-linear and unpredictable. For these dynamic systems, which don’t possess the nice mathematical properties of the linear counterpart, the classic control theory breaks and other methods have to be employed. But nature thrives by optimising non-linear and over-complicated systems. Evolutionary Computing (EC) methods exploit nature’s way by imitating the evolution process and avoid to solve the control problem analytically. Reinforcement Learning (RL) from the other side regards the optimal control problem as a sequential one. In every discrete time step an action is applied. The transition of the system to a new state is accompanied by a sole numerical value, the “reward” that designate the quality of the control action. Even though the amount of feedback information is limited into a sole real number, the introduction of the Temporal Difference method made possible to have accurate predictions of the value-functions. This paved the way to optimise complex structures, like the Neural Networks, which are used to approximate the value functions. In this thesis we investigate the solution of continuous Reinforcement Learning control problems by EC methodologies. The accumulated reward of such problems throughout an episode suffices as information to formulate the required measure, fitness, in order to optimise a population of candidate solutions. Especially, we explore the limits of applicability of a specific branch of EC, that of Genetic Programming (GP). The evolving population in the GP case is comprised from individuals, which are immediately translated to mathematical functions, which can serve as a control law. The major contribution of this thesis is the proposed unification of these disparate Artificial Intelligence paradigms. The provided information from the systems are exploited by a step by step basis from the RL part of the proposed scheme and by an episodic basis from GP. This makes possible to augment the function set of the GP scheme with adaptable Neural Networks. In the quest to achieve stable behaviour of the RL part of the system a modification of the Actor-Critic algorithm has been implemented. Finally we successfully apply the GP method in multi-action control problems extending the spectrum of the problems that this method has been proved to solve. Also we investigated the capability of GP in relation to problems from the food industry. These type of problems exhibit also non-linearity and there is no definite model describing its behaviour

Neuro-Fuzzy Based Intelligent Approaches to Nonlinear System Identification and Forecasting

Nearly three decades back nonlinear system identification consisted of several ad-hoc approaches, which were restricted to a very limited class of systems. However, with the advent of the various soft computing methodologies like neural networks and the fuzzy logic combined with optimization techniques, a wider class of systems can be handled at present. Complex systems may be of diverse characteristics and nature. These systems may be linear or nonlinear, continuous or discrete, time varying or time invariant, static or dynamic, short term or long term, central or distributed, predictable or unpredictable, ill or well defined. Neurofuzzy hybrid modelling approaches have been developed as an ideal technique for utilising linguistic values and numerical data. This Thesis is focused on the development of advanced neurofuzzy modelling architectures and their application to real case studies. Three potential requirements have been identified as desirable characteristics for such design: A model needs to have minimum number of rules; a model needs to be generic acting either as Multi-Input-Single-Output (MISO) or Multi-Input-Multi-Output (MIMO) identification model; a model needs to have a versatile nonlinear membership function. Initially, a MIMO Adaptive Fuzzy Logic System (AFLS) model which incorporates a prototype defuzzification scheme, while utilising an efficient, compared to the Takagi–Sugeno–Kang (TSK) based systems, fuzzification layer has been developed for the detection of meat spoilage using Fourier transform infrared (FTIR) spectroscopy. The identification strategy involved not only the classification of beef fillet samples in their respective quality class (i.e. fresh, semi-fresh and spoiled), but also the simultaneous prediction of their associated microbiological population directly from FTIR spectra. In the case of AFLS, the number of memberships for each input variable was directly associated to the number of rules, hence, the “curse of dimensionality” problem was significantly reduced. Results confirmed the advantage of the proposed scheme against Adaptive Neurofuzzy Inference System (ANFIS), Multilayer Perceptron (MLP) and Partial Least Squares (PLS) techniques used in the same case study. In the case of MISO systems, the TSK based structure, has been utilized in many neurofuzzy systems, like ANFIS. At the next stage of research, an Adaptive Fuzzy Inference Neural Network (AFINN) has been developed for the monitoring the spoilage of minced beef utilising multispectral imaging information. This model, which follows the TSK structure, incorporates a clustering pre-processing stage for the definition of fuzzy rules, while its final fuzzy rule base is determined by competitive learning. In this specific case study, AFINN model was also able to predict for the first time in the literature, the beef’s temperature directly from imaging information. Results again proved the superiority of the adopted model. By extending the line of research and adopting specific design concepts from the previous case studies, the Asymmetric Gaussian Fuzzy Inference Neural Network (AGFINN) architecture has been developed. This architecture has been designed based on the above design principles. A clustering preprocessing scheme has been applied to minimise the number of fuzzy rules. AGFINN incorporates features from the AFLS concept, by having the same number of rules as well as fuzzy memberships. In spite of the extensive use of the standard symmetric Gaussian membership functions, AGFINN utilizes an asymmetric function acting as input linguistic node. Since the asymmetric Gaussian membership function’s variability and flexibility are higher than the traditional one, it can partition the input space more effectively. AGFINN can be built either as an MISO or as an MIMO system. In the MISO case, a TSK defuzzification scheme has been implemented, while two different learning algorithms have been implemented. AGFINN has been tested on real datasets related to electricity price forecasting for the ISO New England Power Distribution System. Its performance was compared against a number of alternative models, including ANFIS, AFLS, MLP and Wavelet Neural Network (WNN), and proved to be superior. The concept of asymmetric functions proved to be a valid hypothesis and certainly it can find application to other architectures, such as in Fuzzy Wavelet Neural Network models, by designing a suitable flexible wavelet membership function. AGFINN’s MIMO characteristics also make the proposed architecture suitable for a larger range of applications/problems

The efficacy and safety of high-pressure processing of food

High-pressure processing (HPP) is a non-thermal treatment in which, for microbial inactivation, foods are subjected to isostatic pressures (P) of 400–600 MPa with common holding times (t) from 1.5 to 6 min. The main factors that influence the efficacy (log10 reduction of vegetative microorganisms) of HPP when applied to foodstuffs are intrinsic (e.g. water activity and pH), extrinsic (P and t) and microorganism-related (type, taxonomic unit, strain and physiological state). It was concluded that HPP of food will not present any additional microbial or chemical food safety concerns when compared to other routinely applied treatments (e.g. pasteurisation). Pathogen reductions in milk/colostrum caused by the current HPP conditions applied by the industry are lower than those achieved by the legal requirements for thermal pasteurisation. However, HPP minimum requirements (P/t combinations) could be identified to achieve specific log10 reductions of relevant hazards based on performance criteria (PC) proposed by international standard agencies (5–8 log10 reductions). The most stringent HPP conditions used industrially (600 MPa, 6 min) would achieve the above-mentioned PC, except for Staphylococcus aureus. Alkaline phosphatase (ALP), the endogenous milk enzyme that is widely used to verify adequate thermal pasteurisation of cows’ milk, is relatively pressure resistant and its use would be limited to that of an overprocessing indicator. Current data are not robust enough to support the proposal of an appropriate indicator to verify the efficacy of HPP under the current HPP conditions applied by the industry. Minimum HPP requirements to reduce Listeria monocytogenes levels by specific log10 reductions could be identified when HPP is applied to ready-to-eat (RTE) cooked meat products, but not for other types of RTE foods. These identified minimum requirements would result in the inactivation of other relevant pathogens (Salmonella and Escherichia coli) in these RTE foods to a similar or higher extent.info:eu-repo/semantics/publishedVersio

The efficacy and safety of high-pressure processing of food

High-pressure processing (HPP) is a non-thermal treatment in which, for microbial inactivation, foodsare subjected to isostatic pressures (P) of 400–600 MPa with common holding times (t) from 1.5 to6 min. The main factors that influence the efficacy (log10reduction of vegetative microorganisms) ofHPP when applied to foodstuffs are intrinsic (e.g. water activity and pH), extrinsic (P and t) andmicroorganism-related (type, taxonomic unit, strain and physiological state). It was concluded thatHPP of food will not present any additional microbial or chemical food safety concerns when comparedto other routinely applied treatments (e.g. pasteurisation). Pathogen reductions in milk/colostrumcaused by the current HPP conditions applied by the industry are lower than those achieved by thelegal requirements for thermal pasteurisation. However, HPP minimum requirements (P/t combinations)could be identified to achieve specific log10reductions of relevant hazards based on performancecriteria (PC) proposed by international standard agencies (5–8 log10reductions). The most stringentHPP conditions used industrially (600 MPa, 6 min) would achieve the above-mentioned PC, except forStaphylococcus aureus. Alkaline phosphatase (ALP), the endogenous milk enzyme that is widely used to verify adequate thermal pasteurisation of cows’milk, is relatively pressure resistant and its usewould be limited to that of an overprocessing indicator. Current data are not robust enough to supportthe proposal of an appropriate indicator to verify the efficacy of HPP under the current HPP conditionsapplied by the industry. Minimum HPP requirements to reduceListeria monocytogeneslevels byspecific log10reductions could be identified when HPP is applied to ready-to-eat (RTE) cooked meatproducts, but not for other types of RTE foods. These identified minimum requirements would result inthe inactivation of other relevant pathogens (SalmonellaandEscherichia coli) in these RTE foods to asimilar or higher extent.info:eu-repo/semantics/publishedVersio

Modelos de inactivación de Listeria monocytogenes en embutidos crudo-curados tratados con altas presiones hidrostáticas y su aplicación en la Evaluación Cuantitativa del Riesgo Microbiológico

Listeria monocytogenes is a foodborne pathogen of special concern in raw-cured sausages due to its capacity to survive at low pH and water activity. The ability of the pathogen to adhere and form biofilms in food-contact surfaces is also a matter of concern since cross-contamination is likely to occur in Ready-to-Eat products that are not submitted to a lethality treatment prior to consumption. In this thesis, the high hydrostatic pressure (HHP) processing technology is studied, by means of Predictive Microbiology models, as an effective non-thermal pasteurization technology to assure the compliance of the current European microbiological criteria concerning L. monocytogenes in raw-cured sausages. To this end, in the first instance, two systematic reviews were carried out, shedding light upon the most important factors governing cross-contamination phenomena and HHP lethality, and whose results are presented in Chapters I and II of this thesis. Based on the reviews and generated experimental data, predictive microbiology models describing the inactivation of L. monocytogenes by HHP in food model systems of raw-cured sausages and chorizo were developed as a function of the technological parameters and intrinsic factors (Chapters III and IV). These models were combined with existing scientific data and predictive models and integrated into a microbial risk assessment framework, to quantitatively assess the real impacts of nitrite reduction, crosscontamination and HHP technology application on L. monocytogenes levels in raw-cured sausages throughout the production and distribution chain (Chapters V and VI). Results of this thesis provides with novel and relevant information that can be used as basis to assist the application of HHP technology at the industrial level and to provide food business operators with suitable quantitative tools to ensure the microbiological safety of raw-cured sausages.Listeria monocytogenes es un patógeno alimentario de especial relevancia para los embutidos crudo-curados debido a su resistencia a condiciones de pH y actividad de agua baja. Asimismo, la alta capacidad del patógeno para adherirse y formar biofilms en las superficies de contacto con alimentos lo sitúa como un patógeno susceptible de contaminación cruzada en productos listos para el consumo, como es el caso de los embutidos crudo-curados. En esta tesis se ha abordado, mediante modelos de Microbiología Predictiva, el estudio de la tecnología de altas presiones hidrostáticas (APH), como un tratamiento postproceso eficaz para garantizar el cumplimiento, en embutidos crudo-curados, del criterio microbiológico sobre L. monocytogenes establecido por la legislación Europea. Con el fin de establecer una base de conocimiento previa se ha llevado a cabo sendas revisiones sobre modelos de APH y contaminación cruzada incluidas en los Capítulo I y II de esta tesis. Además, basados en las revisiones y datos experimentales generados en la tesis se desarrollaron modelos predictivos de inactivación de L. monocytogenes por APH en sistemas modelo de embutidos crudo-curados y en chorizo, considerando como variables los parámetros tecnológicos y los factores intrínsecos de dichos productos (Capítulos III y IV). Estos modelos, junto a datos y modelos predictivos extraídos de la literatura científica se integraron en un esquema de Evaluación del Riesgo Microbiológico para estimar el riesgo de exposición a L. monocytogenes en embutidos crudo-curados a lo largo de la cadena de producción y distribución, considerando el impacto de la reducción de nitritos, la contaminación cruzada y la aplicación de la tecnología de APH (Capítulos V y VI). Los resultados de este estudio proporcionan una fuente de información relevante y novedosa que puede utilizarse como base para la optimización de la aplicación de la tecnología de APH a nivel industrial proporcionando a los operadores de empresas alimentarias herramientas cuantitativas útiles para garantizar la seguridad microbiológica de los embutidos crudo-curados

Design of a cognitive neural predictive controller for mobile robot

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel UniversityIn this thesis, a cognitive neural predictive controller system has been designed to guide a nonholonomic wheeled mobile robot during continuous and non-continuous trajectory tracking and to navigate through static obstacles with collision-free and minimum tracking error. The structure of the controller consists of two layers; the first layer is a neural network system that controls the mobile robot actuators in order to track a desired path. The second layer of the controller is cognitive layer that collects information from the environment and plans the optimal path. In addition to this, it detects if there is any obstacle in the path so it can be avoided by re-planning the trajectory using particle swarm optimisation (PSO) technique. Two neural networks models are used: the first model is modified Elman recurrent neural network model that describes the kinematic and dynamic model of the mobile robot and it is trained off-line and on-line stages to guarantee that the outputs of the model will accurately represent the actual outputs of the mobile robot system. The trained neural model acts as the position and orientation identifier. The second model is feedforward multi-layer perceptron neural network that describes a feedforward neural controller and it is trained off-line and its weights are adapted on-line to find the reference torques, which controls the steady-state outputs of the mobile robot system. The feedback neural controller is based on the posture neural identifier and quadratic performance index predictive optimisation algorithm for N step-ahead prediction in order to find the optimal torque action in the transient to stabilise the tracking error of the mobile robot system when the trajectory of the robot is drifted from the desired path during transient state. Three controller methodologies were developed: the first is the feedback neural controller; the second is the nonlinear PID neural feedback controller and the third is nonlinear inverse dynamic neural feedback controller, based on the back-stepping method and Lyapunov criterion. The main advantages of the presented approaches are to plan an optimal path for itself avoiding obstructions by using intelligent (PSO) technique as well as the analytically derived control law, which has significantly high computational accuracy with predictive optimisation technique to obtain the optimal torques control action and lead to minimum tracking error of the mobile robot for different types of trajectories. The proposed control algorithm has been applied to monitor a nonholonomic wheeled mobile robot, has demonstrated the capability of tracking different trajectories with continuous gradients (lemniscates and circular) or non-continuous gradients (square) with bounded external disturbances and static obstacles. Simulations results and experimental work showed the effectiveness of the proposed cognitive neural predictive control algorithm; this is demonstrated by the minimised tracking error to less than (1 cm) and obtained smoothness of the torque control signal less than maximum torque (0.236 N.m), especially when external disturbances are applied and navigating through static obstacles. Results show that the five steps-ahead prediction algorithm has better performance compared to one step-ahead for all the control methodologies because of a more complex control structure and taking into account future values of the desired one, not only the current value, as with one step-ahead method. The mean-square error method is used for each component of the state error vector to compare between each of the performance control methodologies in order to give better control results

Identification of the Listeria monocytogenes survival curves in UHT whole milk utilising local linear wavelet neural networks

The aim of the present work is to investigate the capabilities of a wavelet neural network for describing the inactivation pattern of Listeria monocytogenes by high hydrostatic pressure in milk, and to compare its performance against classic neural network architectures and models utilised in food microbiology. A new wavelet network is being proposed that includes a “product operation” layer between wavelet functions and output layer, while the connection output-layer weights have been replaced by a local linear model. Milk was artificially inoculated with an initial population of the pathogen and exposed to a range of high pressures (350, 450, 550, 600 MPa) for up to 40 min at ambient temperature (25 °C). Models were validated at 400 and 500 MPa with independent experimental data. First or second order polynomial models were employed to relate the inactivation parameters to pressure, whereas all learning-based networks were utilised in a standard identification approach. The prediction performance of the proposed local linear wavelet network was better at both validation pressures. The development of accurate models to describe the survival curves of microorganisms in high pressure treatment would be very important to the food industry for process optimisation, food safety and would eventually expand the applicability of this non-thermal process