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

Simulations in statistical physics and biology: some applications

One of the most active areas of physics in the last decades has been that of critical phenomena, and Monte Carlo simulations have played an important role as a guide for the validation and prediction of system properties close to the critical points. The kind of phase transitions occurring for the Betts lattice (lattice constructed removing 1/7 of the sites from the triangular lattice) have been studied before with the Potts model for the values q=3, ferromagnetic and antiferromagnetic regime. Here, we add up to this research line the ferromagnetic case for q=4 and 5. In the first case, the critical exponents are estimated for the second order transition, whereas for the latter case the histogram method is applied for the occurring first order transition. Additionally, Domany's Monte Carlo based clustering technique mainly used to group genes similar in their expression levels is reviewed. Finally, a control theory tool --an adaptive observer-- is applied to estimate the exponent parameter involved in the well-known Gompertz curve. By treating all these subjects our aim is to stress the importance of cooperation between distinct disciplines in addressing the complex problems arising in biology. Contents: Chapter 1 - Monte Carlo simulations in stat. physics; Chapter 2: MC simulations in biology; Chapter 3: Gompertz equationComment: 82 pages, 33 figures, 4 tables, somewhat reduced version of the M.Sc. thesis defended in Jan. 2006 at IPICyT, San Luis Potosi, Mx. (Supervisers: Drs. R. Lopez-Sandoval and H.C. Rosu). Last sections 3.3 and 3.4 can be found at http://lanl.arxiv.org/abs/physics/041108

Função discriminatória de lógica Fuzzy para avaliação de cabras expostas a ocorrência de verminose quanto à resistência, resiliência ou sensibilidade ao parasitismo.

A incidência de verminose é um dos principais obstáculos para a caprinocultura nos trópicos. A variação individual da resposta do animal à enfermidade existe, mas precisa ser determinado o seu componente genético e estabelecer o manejo zootécnico dos rebanhos, priorizando a seleção de animais mais resistente ao parasitismo. Objetivou-se nesse estudo avaliar a resposta de cabras à incidência de verminose sob condições de infecção natural a campo, com informações de ovos por grama de fezes (OPG), escore da condição corporal (ECC) e grau de coloração da mucosa conjuntiva (FAMACHA©), recorrendo a utilização de análise de agrupamento e a aplicação de inteligência artificial (IA). Foram utilizadas 3.839 informações de 200 indivíduos em um rebanho experimental de caprinos no Piauí. Considerou-se como resposta ao parasitismo a expressão fenotípica de resistência, sensibilidade e resiliência a verminose, submetidos a três métodos de agrupamento: Ward, Average e K-means, comparado com a lógica Fuzzy, obtidos com o software web CAPRIOVI. Os resultados demonstraram que os grupos de animais resistente, resiliente e sensível ao parasitismo foram estatisticamente distintos (P<0,05). As cabras durante a gestação e o periparto foram identificadas como fases de maior sensibilidade ao parasitismo (P<0,05). O CAPRIOVI aplica a lógica Fuzzy e apresentou o menor percentual de acerto global (77,00%), enquanto os métodos estatísticos tradicionais se destacaram com percentual de acerto global superior a 90,00%, demonstrando excelência estatística com esse fim. Os métodos de agrupamentos apresentaram semelhança na eficiência, mas diferiram quanto à distribuição de animais por agrupamento, com tendência de maior quantidade na categoria resistente. A aplicação da lógica Fuzzy contornou essa limitação ao direcionar a formação dos grupos visando atender o interesse do produtor, inserindo consistência em termos de resposta dos animais a verminose, qualificando o software com potencial para adequação ao manejo sanitário de caprinos.Título em Inglês: Fuzzy logic discriminant function for evaluating goats exposed to verminosis occurrence regarding resistance, resilience, or sensitivity to parasitism

Innovation Technology

Comprise definition of 1500 terms. Innovation from A to Z presents a glossary, including: Terms, older terms whose meanings have changed, acronyms, synonyms, famous names, selected abbreviations, and cross-references. A highly interdisciplinary approach incorporating strategy and entrepreneurship with technology and engineering sciences, economics, marketing, organizational behavior and theory. Ideal for engineers, managers, sales people and economists. Innovation Technology from A to Z Glossary of terms, including acronyms, synonyms, abbreviations, cross-references 1500 terms supplemented by figures and tables that clearly demonstrate the state-of-the-art in Innovation Technolog

Função discriminatória de lógica Fuzzy para avaliação de cabras expostas a ocorrência de verminose quanto à resistência, resiliência ou sensibilidade ao parasitismo

A incidência de verminose é um dos principais obstáculos para a caprinocultura nos trópicos. A variação individual da resposta do animal à enfermidade existe, mas precisa ser determinado o seu componente genético e estabelecer o manejo zootécnico dos rebanhos, priorizando a seleção de animais mais resistente ao parasitismo. Objetivou-se nesse estudo avaliar a resposta de cabras à incidência de verminose sob condições de infecção natural a campo, com informações de ovos por grama de fezes (OPG), escore da condição corporal (ECC) e grau de coloração da mucosa conjuntiva (FAMACHA©), recorrendo a utilização de análise de agrupamento e a aplicação de inteligência artificial (IA). Foram utilizadas 3.839 informações de 200 indivíduos em um rebanho experimental de caprinos no Piauí. Considerou-se como resposta ao parasitismo a expressão fenotípica de resistência, sensibilidade e resiliência a verminose, submetidos a três métodos de agrupamento: Ward, Average e K-means, comparado com a lógica Fuzzy, obtidos com o software web CAPRIOVI. Os resultados demonstraram que os grupos de animais resistente, resiliente e sensível ao parasitismo foram estatisticamente distintos (P<0,05). As cabras durante a gestação e o periparto foram identificadas como fases de maior sensibilidade ao parasitismo (P<0,05). O CAPRIOVI aplica a lógica Fuzzy e apresentou o menor percentual de acerto global (77,00%), enquanto os métodos estatísticos tradicionais se destacaram com percentual de acerto global superior a 90,00%, demonstrando excelência estatística com esse fim. Os métodos de agrupamentos apresentaram semelhança na eficiência, mas diferiram quanto à distribuição de animais por agrupamento, com tendência de maior quantidade na categoria resistente. A aplicação da lógica Fuzzy contornou essa limitação ao direcionar a formação dos grupos visando atender o interesse do produtor, inserindo consistência em termos de resposta dos animais a verminose, qualificando o software com potencial para adequação ao manejo sanitário de caprinos. Palavras-chave: análise descriminante; condição corporal; FAMACHA©; inteligência artificia

Towards stochastic simulation of crop yield: a case study of fruit set in sweet pepper

Crop growth simulation models are widely used in research and education, and their use in commercial practice is increasing. Usually these models are deterministic: one set of input values always gives the same output of the model. In reality, however, variation exists between plants of the same crop. A simulation model taking this variation into account is therefore more realistic. The aim of this thesis is to introduce a stochastic component into a dynamic crop simulation model. As case study, fruit set in sweet pepper was used, because large variation in fruit set between the plants exists. Competition with fast growing fruits causes abortion of flowers and young fruits, which results in periods with high and low fruit set, and consequently periods of high and low fruit yield. A literature review showed that most factors influencing fruit abortion can be expressed in the terms source and sink strength. Source strength is the supply of assimilates; a higher source strength increases fruit set. Source strength takes into account leaf area, radiation, and CO2 level and temperature. Sink strength is the demand for assimilates of the fruits and vegetative parts. It is quantified by the potential growth rate, i.e. the growth rate under non-limiting assimilate supply. Assimilate demand of the fruits depends on their number, age, and cultivar. If the total fruit sink strength of a plant is low, fruit set is high. Vulnerable for abortion were very small buds, buds close to anthesis and flowers and young fruits up to 14 days after anthesis. An experiment with six Capsicum cultivars with fruit sizes ranging between 20 and 205g fresh weight showed that variation in weekly fruit yield is highly correlated with variation in weekly fruit set. Fruit yield patterns resembled fruit set patterns, with a lag time being equal to the average fruit growth duration. Further investigation showed that the cultivars not only differed in sink strength of the individual fruits, but also that the source-sink ratio above which fruit set occurred was higher in cultivars with larger fruits. In the second half of the thesis, flower and fruit abortion was modelled. Survival analysis was used as the method to derive the abortion function. Source and sink strength were used as the factors influencing abortion. Their effect on the probability of abortion per day was non-linear: at high values of source and sink strength an increase did not further decrease or increase the probability of abortion, respectively. Flowers on the side shoots turned out to have a higher probability of abortion than flowers on the main shoot. Most flowers and young fruits aborted around 100°Cd after anthesis. The obtained function was used in a crop simulation model for sweet pepper. After calibration the model was able to simulate the observed fruit set pattern, although fruit abortion was not properly simulated when low source strength was combined with high sink strength. Validation with three independent data sets gave reasonable to good results. Survival analysis proved to be a good method for introducing stochasticity in crop simulation models. A case study with constant source strength showed asynchronisation of fruit set between the plants, indicating that fluctuations in source strength are an important factor causing synchronisation between individual plants. <br/