Particle Swarm Optimization and Intelligence: Advances and Applications examines modern intelligent optimization algorithms proven as very efficient in applications from various scientific and technological fields. Providing distinguished and unique research, this innovative publication offers a compendium of leading field experiences as well as theoretical analyses and complementary techniques useful to academicians and practitioners.

Particle Swarm Optimization and Intelligence: Advances and Applications

Genetic learning of fuzzy cognitive maps

Transfer-function measurements using sweeps as excitation signals rather than pseudonoise signals show significantly higher immunity against distortion and time variance. Capturing binaural room impulse responses for high-quality auralization purposes requires a signal-to-noise ratio (SNR) greater than 90 dB, which is unattainable with maximum-length sequence (MLS) measurements because of loudspeaker nonlinearity, but it is fairly easy to reach with sweeps due to the possibility of complete rejection of harmonic distortion. Before investigating the differences and practical problems of measurements with MLS and sweeps and arguing why sweeps are the preferable choice for the majority of measurement tasks, the existing methods of obtaining transfer functions are reviewed. The continual need to use preemphasized excitation signals in acoustical measurements is also addressed. A method to create sweeps with arbitrary spectral contents, but constant or prescribed frequency-dependent temporal envelope, is presented. Finally, the possibility of simultaneously analyzing transfer functions and harmonics is investigated.

Transfer-Function Measurement with Sweeps

This research deals with the soft computing methodology of fuzzy cognitive map (FCM). Here a mathematical description of FCM is presented and a new methodology based on fuzzy logic techniques for developing the FCM is examined. The capability and usefulness of FCM in modeling complex systems and the application of FCM to modeling and describing the behavior of a heat exchanger system is presented. The applicability of FCM to model the supervisor of complex systems is discussed and the FCM-supervisor for evaluating the performance of a system is constructed; simulation results are presented and discussed.

Modeling complex systems using fuzzy cognitive maps

This survey makes a review of the most recent applications and trends on fuzzy cognitive maps (FCMs) over the past decade. FCMs are inference networks, using cyclic digraphs, for knowledge representation and reasoning. Over the past decade, FCMs have gained considerable research interest and are widely used to analyze causal complex systems, which have originated from the combination of fuzzy logic and neural networks. FCMs have been applied in diverse application domains, such as computer science, engineering, environmental sciences, behavioral sciences, medicine, business, information systems, and information technology. Their dynamic characteristics and learning capabilities make them essential for a number of tasks such as modeling, analysis, decision making, forecast, etc. Overall, this paper summarizes the current state of knowledge of the topic of FCMs. It creates an understanding of the topic for the reader by discussing the findings presented in recent research papers. A survey on FCM studies concentrated on FCM applications on diverse scientific areas, where the FCMs emerged with a high degree of applicability, has also been done during the past ten years.

A Review of Fuzzy Cognitive Maps Research During the Last Decade

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

This paper investigates a novel hybrid fuzzy neural system, fuzzy cognitive map (FCM), and its implementation in distributed systems and control problems. The description and the methodology of this system will be examined and then it will be shown the application of FCM in a process control problem, which will reveal the characteristics and qualities of FCM. There is an oncoming need for more autonomous and intelligent systems, which could be satisfied with the application of FCM in the field of systems and control.

Introducing the theory of fuzzy cognitive maps in distributed systems

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.

Fuzzy Cognitive Map Approach to Process Control Systems Chrysostomos

We investigated whether an expert's consultation provided via telemedicine could improve the quality of care for patients with dysphagia. A trained clinician completed videofluoroscopic swallowing studies (VFSS) of 17 consecutive patients in a Greek hospital. The videofluoroscopic images were then stored on a website for independent review by an expert Speech and Language Pathologist in the US. An extra Rater evaluated 20% of all data for additional reliability testing. Eight diagnostic indicators of swallowing impairment and an overall subjective severity index were recorded for each study. Clinicians were also asked to choose from ten common treatment options for patients with dysphagia. There was good inter-rater agreement for most of the diagnostic indicators examined (ranging from 78% to 90%; kappa = 0.52-0.71) between all three Raters. Agreement on overall severity ratings was exact for more than half of the patients and within one-point on the 4-point scale for all other patients except one. However, the quality of care would have been substandard for more than half of the patients if teleconsultation had not been employed. In settings where a swallowing expert is not available and real-time telemedicine is not feasible, the use of asynchronous teleconsultation can produce better quality of care for patients with dysphagia.

Voula C. Georgopoulos

Papers

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

Fuzzy cognitive map architectures for medical decision support systems

Introducing the theory of fuzzy cognitive maps in distributed systems

Fuzzy Cognitive Map Approach to Process Control Systems Chrysostomos

An international pilot study of asynchronous teleconsultation for oropharyngeal dysphagia