A genetic based neuro-fuzzy controller for thermal processes

This paper presents a neuro-fuzzy network where all its parameters can be tuned simultaneously using Genetic Algorithms. The approach combines the merits of fuzzy logic theory, neural networks and genetic algorithms. The proposed neuro-fuzzy network does not require a priori knowledge about the system and eliminates the need for complicated design steps like manual tuning of input-output membership functions, and selection of fuzzy rule base. Although, only conventional genetic algorithms have been used, convergence results are very encouraging. A well known numerical example derived from literature is used to evaluate and compare the performance of the network with other modelling approaches. The network is further implemented as controller for two simulated thermal processes and their performances are compared with other existing controllers. Simulation results show that the proposed neuro-fuzzy controller whose all parameters have been tuned simultaneously using GAs, offers advantages over existing controllers and has improved performance.Facultad de Informátic

A genetic based neuro-fuzzy controller for thermal processes

This paper presents a neuro-fuzzy network where all its parameters can be tuned simultaneously using Genetic Algorithms. The approach combines the merits of fuzzy logic theory, neural networks and genetic algorithms. The proposed neuro-fuzzy network does not require a priori knowledge about the system and eliminates the need for complicated design steps like manual tuning of input-output membership functions, and selection of fuzzy rule base. Although, only conventional genetic algorithms have been used, convergence results are very encouraging. A well known numerical example derived from literature is used to evaluate and compare the performance of the network with other modelling approaches. The network is further implemented as controller for two simulated thermal processes and their performances are compared with other existing controllers. Simulation results show that the proposed neuro-fuzzy controller whose all parameters have been tuned simultaneously using GAs, offers advantages over existing controllers and has improved performance.Facultad de Informátic

A hybrid unsupervised approach toward EEG epileptic spikes detection

Epileptic spikes are complementary sources of information in EEG to diagnose and localize the origin of epilepsy. However, not only is visual inspection of EEG labor intensive, time consuming, and prone to human error, but it also needs long-term training to acquire the level of skill required for identifying epileptic discharges. Therefore, computer-aided approaches were employed for the purpose of saving time and increasing the detection and source localization accuracy. One of the most important artifacts that may be confused as an epileptic spike, due to morphological resemblance, is eye blink. Only a few studies consider removal of this artifact prior to detection, and most of them used either visual inspection or computer-aided approaches, which need expert supervision. Consequently, in this paper, an unsupervised and EEG-based system with embedded eye blink artifact remover is developed to detect epileptic spikes. The proposed system includes three stages: eye blink artifact removal, feature extraction, and classification. Wavelet transform was employed for both artifact removal and feature extraction steps, and adaptive neuro-fuzzy inference system for classification purpose. The proposed method is verified using a publicly available EEG dataset. The results show the efficiency of this algorithm in detecting epileptic spikes using low-resolution EEG with least computational complexity, highest sensitivity, and lesser human interaction compared to similar studies. Moreover, since epileptic spike detection is a vital component of epilepsy source localization, therefore this algorithm can be utilized for EEG-based pre-surgical evaluation of epilepsy

Heuristic design of fuzzy inference systems: a review of three decades of research

This paper provides an in-depth review of the optimal design of type-1 and type-2 fuzzy inference systems (FIS) using five well known computational frameworks: genetic-fuzzy systems (GFS), neuro-fuzzy systems (NFS), hierarchical fuzzy systems (HFS), evolving fuzzy systems (EFS), and multi-objective fuzzy systems (MFS), which is in view that some of them are linked to each other. The heuristic design of GFS uses evolutionary algorithms for optimizing both Mamdani-type and Takagi–Sugeno–Kang-type fuzzy systems. Whereas, the NFS combines the FIS with neural network learning systems to improve the approximation ability. An HFS combines two or more low-dimensional fuzzy logic units in a hierarchical design to overcome the curse of dimensionality. An EFS solves the data streaming issues by evolving the system incrementally, and an MFS solves the multi-objective trade-offs like the simultaneous maximization of both interpretability and accuracy. This paper offers a synthesis of these dimensions and explores their potentials, challenges, and opportunities in FIS research. This review also examines the complex relations among these dimensions and the possibilities of combining one or more computational frameworks adding another dimension: deep fuzzy systems

Doctor of Philosophy

dissertationTemporal reasoning denotes the modeling of causal relationships between different variables across different instances of time, and the prediction of future events or the explanation of past events. Temporal reasoning helps in modeling and understanding interactions between human pathophysiological processes, and in predicting future outcomes such as response to treatment or complications. Dynamic Bayesian Networks (DBN) support modeling changes in patients' condition over time due to both diseases and treatments, using probabilistic relationships between different clinical variables, both within and across different points in time. We describe temporal reasoning and representation in general and DBN in particular, with special attention to DBN parameter learning and inference. We also describe temporal data preparation (aggregation, consolidation, and abstraction) techniques that are applicable to medical data that were used in our research. We describe and evaluate various data discretization methods that are applicable to medical data. Projeny, an opensource probabilistic temporal reasoning toolkit developed as part of this research, is also described. We apply these methods, techniques, and algorithms to two disease processes modeled as Dynamic Bayesian Networks. The first test case is hyperglycemia due to severe illness in patients treated in the Intensive Care Unit (ICU). We model the patients' serum glucose and insulin drip rates using Dynamic Bayesian Networks, and recommend insulin drip rates to maintain the patients' serum glucose within a normal range. The model's safety and efficacy are proven by comparing it to the current gold standard. The second test case is the early prediction of sepsis in the emergency department. Sepsis is an acute life threatening condition that requires timely diagnosis and treatment. We present various DBN models and data preparation techniques that detect sepsis with very high accuracy within two hours after the patients' admission to the emergency department. We also discuss factors affecting the computational tractability of the models and appropriate optimization techniques. In this dissertation, we present a guide to temporal reasoning, evaluation of various data preparation, discretization, learning and inference methods, proofs using two test cases using real clinical data, an open-source toolkit, and recommend methods and techniques for temporal reasoning in medicine

Una nueva aplicación de las redes neuronales y la lógica difusa a la optimización del proceso de la fabricación del arrabio en un horno alto

Esta tesis se centra en el estudio de la aplicación de sistemas, basados en redes neuronales y lógica difusa, para la fabricación de arrabio. En particular el control relativo a los parámetros del horno alto, tales como temperatura del metal liquido, nivel térmico del horno así como sistemas que ayuden al conocimiento de las relaciones entre los mapas de la temperatura, y otros factores como una ayuda a la optimización del uso de combustibles y coque en el proceso. La metodología propuesta para atacar estos problemas es evaluar y hacer predicciones con modelos basados en redes neuronales artificiales e inferencia difusa

Setting Target Rates for Construction Activity Analysis Categories

This thesis is focused on increasing productive actions in construction by a procedure known as Activity Analysis. Activity Analysis is a continuous productivity improvement tool for identifying barriers to site productivity with the goal of decreasing them and thereby increasing the direct work rate. A preceding study validated this approach, however it had two limitations. No reevaluation was conducted on projects in Canada by the authors, and not enough resources or data were available to understand behaviour of the activity rates in absolute value terms across many projects. Based on three case studies and data collected over 17 days by the author and a colleague, Activity Analysis was validated as being applicable in Canadian conditions. A desired value, known as a target rate, was then studied in order to be able to set expectations with respect to the productivity to be achieved in each cycle. The premise behind setting a “target rate” is that 100% direct work is neither possible nor desirable, since some time must always be spent on communications and planning. However, a higher direct work rate is generally better than a lower rate. Thus, a target rate is needed. A mathematical model called ANFIS was developed as a means of setting the desired level of activities. Through consideration of a variety of factors that affect labour productivity, the developed model was trained based on 65 data points. The model was found to be easy to use and flexible enough to be appropriate for all of the factors considered. Based on the data points available from 5 different past projects and 3 recent projects and the experience associated with these projects, three additional methods of defining the target rate were developed. The impact of these results is that companies now have appropriate methods and an initial data set for industrial construction in order to establish target rates for direct work and supporting activities. This should help reduce project costs and improve productivity

Fuzzy machine vision based inspection

Machine vision system has been fostered to solve many realistic problems in various fields. Its role in achieving superior quality and productivity is of paramount importance. But, for such system to be attractive, it needs to be fast, accurate and cost-effective. This dissertation is based on a number of practical machine vision based inspection projects obtained from the automotive industry. It presents a collection of developed efficient fuzzy machine vision approaches endorsed with experimental results. It also covers the conceptual design, development and testing of various fuzzy machine vision based inspection approaches for different industrial applications. To assist in developing and evaluating the performance of the proposed approaches, several parts are tested under varying lighting conditions. This research deals with two important aspects of machine vision based inspection. In the first part, it concentrates on the topics of component detection and component orientation identification. The components used in this part are metal clips mounted on a dash panel frame that is installed in the door of trucks. Therefore, we propose a fuzzy machine vision based clip detection model and a fuzzy machine vision based clip orientation identification model to inspect the proper placement of clips on dash panels. Both models are efficient and fast in terms of accuracy and processing time. In the second part of the research, we are dealing with machined part defects such as broken edge, porosity and tool marks. The se defects occur on the surface of die cast aluminum automotive pump housings. As a result, an automated fuzzy machine vision based broken edge detection method, an efficient fuzzy machine vision based porosity detection technique and a neuro-fuzzy part classification model based on tool marks are developed. Computational results show that the proposed approaches are effective in yielding satisfactory results to the tested image databases. There are four main contributions to this work. The first contribution is the development of the concept of composite matrices in conjunction with XOR feature extractor using fuzzy subtractive clustering for clip detection. The second contribution is about a proposed model based on grouping and counting pixels in pre-selective areas which tracks pixel colors in separated RGB channels to determine whether the orientation of the clip is acceptable or not. The construction of three novel edge based features embedded in fuzzy C-means clustering for broken edge detection marks the third contribution. At last, the fourth contribution presents the core of porosity candidates concept and its correlation with twelve developed matrices. This, in turn, results in the development of five different features used in our fuzzy machine vision based porosity detection approach

Intelligent Control Strategies for an Autonomous Underwater Vehicle

The dynamic characteristics of autonomous underwater vehicles (AUVs) present a control problem that classical methods cannot often accommodate easily. Fundamentally, AUV dynamics are highly non-linear, and the relative similarity between the linear and angular velocities about each degree of freedom means that control schemes employed within other flight vehicles are not always applicable. In such instances, intelligent control strategies offer a more sophisticated approach to the design of the control algorithm. Neurofuzzy control is one such technique, which fuses the beneficial properties of neural networks and fuzzy logic in a hybrid control architecture. Such an approach is highly suited to development of an autopilot for an AUV. Specifically, the adaptive network-based fuzzy inference system (ANFIS) is discussed in Chapter 4 as an effective new approach for neurally tuning course-changing fuzzy autopilots. However, the limitation of this technique is that it cannot be used for developing multivariable fuzzy structures. Consequently, the co-active ANFIS (CANFIS) architecture is developed and employed as a novel multi variable AUV autopilot within Chapter 5, whereby simultaneous control of the AUV yaw and roll channels is achieved. Moreover, this structure is flexible in that it is extended in Chapter 6 to perform on-line control of the AUV leading to a novel autopilot design that can accommodate changing vehicle pay loads and environmental disturbances. Whilst the typical ANFIS and CANFIS structures prove effective for AUV control system design, the well known properties of radial basis function networks (RBFN) offer a more flexible controller architecture. Chapter 7 presents a new approach to fuzzy modelling and employs both ANFIS and CANFIS structures with non-linear consequent functions of composite Gaussian form. This merger of CANFIS and a RBFN lends itself naturally to tuning with an extended form of the hybrid learning rule, and provides a very effective approach to intelligent controller development.The Sea Systems and Platform Integration Sector, Defence Evaluation and Research Agency, Winfrit