Fuzzy Mathematics

This book provides a timely overview of topics in fuzzy mathematics. It lays the foundation for further research and applications in a broad range of areas. It contains break-through analysis on how results from the many variations and extensions of fuzzy set theory can be obtained from known results of traditional fuzzy set theory. The book contains not only theoretical results, but a wide range of applications in areas such as decision analysis, optimal allocation in possibilistics and mixed models, pattern classification, credibility measures, algorithms for modeling uncertain data, and numerical methods for solving fuzzy linear systems. The book offers an excellent reference for advanced undergraduate and graduate students in applied and theoretical fuzzy mathematics. Researchers and referees in fuzzy set theory will find the book to be of extreme value

Developing A Machine Learning Based Approach For Fractured Zone Detection By Using Petrophysical Logs

Oil reservoirs are divided into three categories: carbonate (fractured), sandstone and unconventional reservoirs. Identification and modeling of fractures in fractured reservoirs are so important due to geomechanical issues, fluid flood simulation and enhanced oil recovery.Image and petrophysical logs are individual tools, run inside oil wells, to achieve physical characteristics of reservoirs, e.g. geological rock types, porosity, and permeability. Fractures could be distinguished using image logs because of their higher resolution. Image logs are an expensive and newly developed tool, so they have run in limited wells, whereas petrophysical logs are usually run inside the wells. Lack of image logs makes huge difficulties in fracture detection, as well as fracture studies. In the last decade, a few studies were done to distinguish fractured zones in oil wells, by applying data mining methods over petrophysical logs. The goal of this study was also discrimination of fractured/non-fractured zones by using machine learning techniques and petrophysical logs. To do that, interpretation of image logs was utilized to label reservoir depth of studied wells as 0 (non-fractured zone) and 1 (fractured zone). We developed four classifiers (Deep Learning, Support Vector Machine, Decision Tree, and Random Forest) and applied them to petrophysics logs to discriminate fractured/non-fractured zones. Ordered Weighted Averaging was the data fusion method that we utilized to integrate outputs of classifiers in order to achieve unique and more reliable results. Overall, the frequency of non-fractured zones is about two times of fractured zones. This leads to an imbalanced condition between two classes. Therefore, the aforementioned procedure relied on the balance/imbalance data to investigate the influence of creating a balanced situation between classes. Results showed that Random Forest and Support Vector Machines are better classifiers with above 95 percent accuracy in discrimination of fractured/non-fractured zones. Meanwhile, making a balanced situation in the wells by a higher imbalance index helps to distinguish either non-fractured or fractured zones. Through imbalance data, non-fractured zones (dominant class) could be perfectly distinguished, while a significant percentage of fractured zones were also labeled as non-fractured ones

Computational intelligence based complex adaptive system-of-systems architecture evolution strategy

The dynamic planning for a system-of-systems (SoS) is a challenging endeavor. Large scale organizations and operations constantly face challenges to incorporate new systems and upgrade existing systems over a period of time under threats, constrained budget and uncertainty. It is therefore necessary for the program managers to be able to look at the future scenarios and critically assess the impact of technology and stakeholder changes. Managers and engineers are always looking for options that signify affordable acquisition selections and lessen the cycle time for early acquisition and new technology addition. This research helps in analyzing sequential decisions in an evolving SoS architecture based on the wave model through three key features namely; meta-architecture generation, architecture assessment and architecture implementation. Meta-architectures are generated using evolutionary algorithms and assessed using type II fuzzy nets. The approach can accommodate diverse stakeholder views and convert them to key performance parameters (KPP) and use them for architecture assessment. On the other hand, it is not possible to implement such architecture without persuading the systems to participate into the meta-architecture. To address this issue a negotiation model is proposed which helps the SoS manger to adapt his strategy based on system owners behavior. This work helps in capturing the varied differences in the resources required by systems to prepare for participation. The viewpoints of multiple stakeholders are aggregated to assess the overall mission effectiveness of the overarching objective. An SAR SoS example problem illustrates application of the method. Also a dynamic programing approach can be used for generating meta-architectures based on the wave model. --Abstract, page iii

ROBUST DETECTION OF CORONARY HEART DISEASE USING MACHINE LEARNING ALGORITHMS

Predicting whether or not someone will get heart or cardiac disease is now one of the most difficult jobs in the area of medicine. Heart disease is responsible for the deaths of about one person per minute in the contemporary age. Processing the vast amounts of data that are generated in the field of healthcare is an important application for data science. Because predicting cardiac disease is a difficult undertaking, there is a pressing need to automate the prediction process to minimize the dangers that are connected with it and provide the patient with timely warning. The chapter one in this thesis report highlights the importance of this problem and identifies the need to augment the current technological efforts to produce relatively more accurate system in facilitating the timely decision about the problem. The chapter one also presents the current literature about the theories and systems developed and assessed in this direction.This thesis work makes use of the dataset on cardiac illness that can be found in the machine learning repository at UCI. Using a variety of data mining strategies, such as Naive Bayes, Decision Tree, Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), and Random Forest, the work that has been reported in this thesis estimates the likelihood that a patient would develop heart disease and can categorize the patient\u27s degree of risk. The performance of chosen classifiers is tested on chosen feature space with help of feature selection algorithm. On Cleveland heart datasets of heart disease, the models were placed for training and testing. To assess the usefulness and strength of each model, several performance metrics are utilized, including sensitivity, accuracy, AUC, specificity, ROC curve and F1-score. The effort behind this research leads to conduct a comparative analysis by computing the performance of several machine learning algorithms. The results of the experiment demonstrate that the Random Forest and Support Vector machine algorithms achieved the best level of accuracy (94.50% and 91.73% respectively) on selected feature space when compared to the other machine learning methods that were employed. Thus, these two classifiers turned out to be promising classifiers for heart disease prediction. The computational complexity of each classifier was also investigated. Based on the computational complexity and comparative experimental results, a robust heart disease prediction is proposed for an embedded platform, where benefits of multiple classifiers are accumulated. The system proposes that heart disease detection is possible with higher confidence if and only if many of these classifiers detect it. In the end, results of experimental work are concluded and possible future strategies in enhancing this effort are discussed

AN ARTIFICIAL INTELLIGENCE APPROACH TO THE PROCESSING OF RADAR RETURN SIGNALS FOR TARGET DETECTION

Most of the operating vessel traffic management systems experience problems, such as track loss and track swap, which may cause confusion to the traffic regulators and lead to potential hazards in the harbour operation. The reason is mainly due to the limited adaptive capabilities of the algorithms used in the detection process. The decision on whether a target is present is usually based on the magnitude of the returning echoes. Such a method has a low efficiency in discriminating between the target and clutter, especially when the signal to noise ratio is low. The performance of radar target detection depends on the features, which can be used to discriminate between clutter and targets. To have a significant improvement in the detection of weak targets, more obvious discriminating features must be identified and extracted. This research investigates conventional Constant False Alarm Rate (CFAR) algorithms and introduces the approach of applying ar1ificial intelligence methods to the target detection problems. Previous research has been unde11aken to improve the detection capability of the radar system in the heavy clutter environment and many new CFAR algorithms, which are based on amplitude information only, have been developed. This research studies these algorithms and proposes that it is feasible to design and develop an advanced target detection system that is capable of discriminating targets from clutters by learning the .different features extracted from radar returns. The approach adopted for this further work into target detection was the use of neural networks. Results presented show that such a network is able to learn particular features of specific radar return signals, e.g. rain clutter, sea clutter, target, and to decide if a target is present in a finite window of data. The work includes a study of the characteristics of radar signals and identification of the features that can be used in the process of effective detection. The use of a general purpose marine radar has allowed the collection of live signals from the Plymouth harbour for analysis, training and validation. The approach of using data from the real environment has enabled the developed detection system to be exposed to real clutter conditions that cannot be obtained when using simulated data. The performance of the neural network detection system is evaluated with further recorded data and the results obtained are compared with the conventional CFAR algorithms. It is shown that the neural system can learn the features of specific radar signals and provide a superior performance in detecting targets from clutters. Areas for further research and development arc presented; these include the use of a sophisticated recording system, high speed processors and the potential for target classification

Applications of fuzzy counterpropagation neural networks to non-linear function approximation and background noise elimination

An adaptive filter which can operate in an unknown environment by performing a learning mechanism that is suitable for the speech enhancement process. This research develops a novel ANN model which incorporates the fuzzy set approach and which can perform a non-linear function approximation. The model is used as the basic structure of an adaptive filter. The learning capability of ANN is expected to be able to reduce the development time and cost of the designing adaptive filters based on fuzzy set approach. A combination of both techniques may result in a learnable system that can tackle the vagueness problem of a changing environment where the adaptive filter operates. This proposed model is called Fuzzy Counterpropagation Network (Fuzzy CPN). It has fast learning capability and self-growing structure. This model is applied to non-linear function approximation, chaotic time series prediction and background noise elimination