Statistical Genetic Interval-Valued Type-2 Fuzzy System and its Application

In recent years, the type-2 fuzzy sets theory has been used to model and minimize the effects of uncertainties in rule-base fuzzy logic system. In order to make the type-2 fuzzy logic system reasonable and reliable, a new simple and novel statistical method to decide interval-valued fuzzy membership functions and a new probability type reduced reasoning method for the interval-valued fuzzy logic system are proposed in this thesis. In order to optimize this particle system’s performance, we adopt genetic algorithm (GA) to adjust parameters. The applications for the new system are performed and results have shown that the developed method is more accurate and robust to design a reliable fuzzy logic system than type-1 method and the computation of our proposed method is more efficient

An Extended Fuzzy Discrete Event System For Hiv/aids Treatment Regimen Selection

HIV/AIDS is a global problem. Its treatment is dependent on the physician experts\u27 opinion. A system which is capable of supporting the treatment decision will be desired. Recently, the HIV/AIDS treatment regimen selection system appeared in literature that utilized theory of fuzzy discrete event system (FDES) to capture the meaning of experts\u27 knowledge; a form of consensus involving estimated points and type-1 fuzzy sets. The goal was to assign exact matching regimens as close as possible to those regimens preferred by the experts for patients. The system performance was 80% of satisfaction level with the 35 retrospective patients. Extracting experts\u27 knowledge into the consensus forms would not be possible without being compromised by the experts. With equal respective experts, if one insists on his/her values, then the consensus would not be achieved. Conversely, the FDES theory would be no longer to handle such conflict. The theory of extended fuzzy discrete event system (EFDES) extended the FDES theory that type-2 fuzzy sets would be allowed to be used in the system. This dissertation is to apply the EFDES theory to the HIV/AIDS treatment regimen selection system. Seven scenarios of the diversity of experts\u27 knowledge representation were categorized for the system. The MATLAB was implemented to model the system. Genetic algorithm in MATLAB\u27s Direct Search Toolbox was used to search an optimal vector of 26 weights for system parameters regarding the experts\u27 regimen-choices. As the same input of the retrospective patient data for the FDES-based system, the overall means of simulation results of EFDES-based system demonstrated the degree of matching regimens being 80%. That result would be the same performance level of the FDES-based system as well. The EFDES-based system performance with self-learning provided the overall satisfaction level of above 80%. Moreover, the EFDES-based system with use of the type-2 fuzzy set gained the benefit on the extraction of diverse and uncertainty experts\u27 knowledge and expertise

Neutrosophic rule-based prediction system for toxicity effects assessment of biotransformed hepatic drugs

Measuring toxicity is an important step in drug development. However, the current experimental meth- ods which are used to estimate the drug toxicity are expensive and need high computational efforts. Therefore, these methods are not suitable for large-scale evaluation of drug toxicity. As a consequence, there is a high demand to implement computational models that can predict drug toxicity risks. In this paper, we used a dataset that consists of 553 drugs that biotransformed in the liver

EEG-Analysis for Cognitive Failure Detection in Driving Using Type-2 Fuzzy Classifiers

The paper aims at detecting on-line cognitive failures in driving by decoding the EEG signals acquired during visual alertness, motor-planning and motor-execution phases of the driver. Visual alertness of the driver is detected by classifying the pre-processed EEG signals obtained from his pre-frontal and frontal lobes into two classes: alert and non-alert. Motor-planning performed by the driver using the pre-processed parietal signals is classified into four classes: braking, acceleration, steering control and no operation. Cognitive failures in motor-planning are determined by comparing the classified motor-planning class of the driver with the ground truth class obtained from the co-pilot through a hand-held rotary switch. Lastly, failure in motor execution is detected, when the time-delay between the onset of motor imagination and the EMG response exceeds a predefined duration. The most important aspect of the present research lies in cognitive failure classification during the planning phase. The complexity in subjective plan classification arises due to possible overlap of signal features involved in braking, acceleration and steering control. A specialized interval/general type-2 fuzzy set induced neural classifier is employed to eliminate the uncertainty in classification of motor-planning. Experiments undertaken reveal that the proposed neuro-fuzzy classifier outperforms traditional techniques in presence of external disturbances to the driver. Decoding of visual alertness and motor-execution are performed with kernelized support vector machine classifiers. An analysis reveals that at a driving speed of 64 km/hr, the lead-time is over 600 milliseconds, which offer a safe distance of 10.66 meters

Advances in Reinforcement Learning

Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This book brings together many different aspects of the current research on several fields associated to RL which has been growing rapidly, producing a wide variety of learning algorithms for different applications. Based on 24 Chapters, it covers a very broad variety of topics in RL and their application in autonomous systems. A set of chapters in this book provide a general overview of RL while other chapters focus mostly on the applications of RL paradigms: Game Theory, Multi-Agent Theory, Robotic, Networking Technologies, Vehicular Navigation, Medicine and Industrial Logistic