Active Hebbian learning algorithm to train fuzzy cognitive maps

In this study, heartbeat time series are classified using support vector machines (SVMs). Statistical methods and signal analysis techniques are used to extract features from the signals. The SVM classifier is favorably compared to other neural network-based classification approaches by performing leave-one-out cross validation. The performance of the SVM with respect to other state-of-the-art classifiers is also confirmed by the classification of signals presenting very low signal-to-noise ratio. Finally, the influence of the number of features to the classification rate was also investigated for two real datasets. The first dataset consists of long-term ECG recordings of young and elderly healthy subjects. The second dataset consists of long-term ECG recordings of normal subjects and subjects suffering from coronary artery disease.

Heartbeat Time Series Classification With Support Vector Machines

Fuzzy Cognitive Map (FCM) is a soft computing technique for modeling systems. It combines synergistically the theories of neural networks and fuzzy logic. The methodology of developing FCMs is easily adaptable but relies on human experience and knowledge, and thus FCMs exhibit weaknesses and dependence on human experts. The critical dependence on the expert’s opinion and knowledge, and the potential convergence to undesired steady states are deficiencies of FCMs. In order to overcome these deficiencies and improve the efficiency and robustness of FCM a possible solution is the utilization of learning methods. This research work proposes the utilization of the unsupervised Hebbian algorithm to nonlinear units for training FCMs. Using the proposed learning procedure, the FCM modifies its fuzzy causal web as causal patterns change and as experts update their causal knowledge.

Fuzzy Cognitive Map Learning Based on Nonlinear Hebbian Rule

A fuzzy cognitive map approach to differential diagnosis of specific language impairment

Medical decision support systems can provide assistance in crucial clinical judgments, particularly for inexperienced medical professionals. Fuzzy cognitive maps (FCMs) is a soft computing technique for modeling complex systems, which follows an approach similar to human reasoning and the human decision-making process. FCMs can successfully represent knowledge and human experience, introducing concepts to represent the essential elements and the cause and effect relationships among the concepts to model the behavior of any system. Medical decision systems are complex systems that can be decomposed to non-related and related subsystems and elements, where many factors have to be taken into consideration that may be complementary, contradictory, and competitive; these factors influence each other and determine the overall clinical decision with a different degree. Thus, FCMs are suitable for medical decision support systems and appropriate FCM architectures are proposed and developed as well as the corresponding examples from two medical disciplines, i.e. speech and language pathology and obstetrics, are described.

Fuzzy cognitive map architectures for medical decision support systems

Cardiotocography is the main method used for fetal assessment in everyday clinical practice for the last 30 years. Many attempts have been made to increase the effectiveness of the evaluation of cardiotocographic recordings and minimize the variations of their interpretation utilizing technological advances. This research work proposes and focuses on an advanced method able to identify fetuses compromised and suspicious of developing metabolic acidosis. The core of the proposed method is the introduction of a support vector machine to "foresee" undesirable and risky situations for the fetus, based on features extracted from the fetal heart rate signal at the time and frequency domains along with some morphological features. This method has been tested successfully on a data set of intrapartum recordings, achieving better and balanced overall performance compared to other classification methods, constituting,therefore, a promising new automatic methodology for the prediction of metabolicacidosis

Predicting the risk of metabolic acidosis for newborns based on fetal heart rate signal classification using support vector machines

Corrventional control has signhicantly contributed to the solution of many control problems, but its contribution to solutions of increasingly complex dynamical systems has practical dWiculties. Requirements in control and in supervisory control cannot be met with existing conventional control theory and new methods are required that exploit past experience, can learn, and provide failure detection and identification. Soft computing thus becomes an important alternative to corrventional control. Fuzzy cognitive map (FCM) usage for control and modeling systems is expected to contribute much to the effort to create more intedigent control systems. FCM describes and models a system symbolically, using concepts to illustrate ditiferent aspects of system behavior that interact, showing system dynamics. A FCM integrates experienoe and knowledge on system operation due to how it is constructed, i.e., using human experts that know system operation and its behavior in ditiferent circumstances. Due to their dynamic nature, FCMs are exploited to represent and conduct system control. Political scientist R. Axelrodi) introduced cognitive maps for representing social scientific knowledge and describing methods used for decision making in social and political systems. B. Kosko6'\" enhanced cognitive maps considering fuzzy values for conoepts of the cognitive map and fUzzy degrees of interrelationships between conoepts. After this pioneering work, FCMs attracted the attention of scientists in many fields and have been used in durerent scientific problems. New FCMs have been proposed such as the extended FCM5) and the neural cognitive maps9). FCMs have been used for planning and making decisions in international relations and political developmentsM and have been proposed for generic decision analysis20) and disuibuted cooperative agents2i), FcMs have been used to analyze electrical circuitsi4) and to construct vimal worlds2). ln control themes, FCMs have been used to model and support plant contro14), represent failure models and effects analysis for a system modeliiNi2), and to model the control system supervisori5-i6). The objective of this paper is to define and constmet FCMs models for describing complex systems. Section 2 describes Fems and proposes a calculation rule. Section 3 proposes a soft computing methodology for constructing and developing FCMs. Section 4 implements FCM to model and control a chemical process. Section 5 suggest the use of two-level FCMs to conduct supervisory control and discusses the failure part of a supervisor-FCM. Section 6 gives conclusions and prospects.

D. Stylios

Papers

Predicting the risk of metabolic acidosis for newborns based on fetal heart rate signal classification using support vector machines

Fuzzy Cognitive Map Approach to Process Control Systems Chrysostomos