Summary: Neural Networks are information processing systems inspired by the ability of human brain to learn from observations and to generalize by abstraction. It has been used in diverse fields such as pattern recognition, speech processing, control, medical applications and more. In recent years, it has emerged as an attractive vehicle in the RF/microwave CAD community to address the challenges in high-frequency electronic modeling and design. Neural networks can learn and generalize from data allowing model development even when component formulas are unavailable. Neural network models are universal approximators allowing re-use of the same modeling technology for both linear and nonlinear problems and at both device and circuit levels.

Summary: This course covers the rapidly evolving field of Computational Intelligence and focuses on the current understanding of the fundamental principles of working the mind, their computational implementations, and practical applications. This tutorial covers mind mechanisms, including concepts, emotions, instincts, behavior, language, cognition, understanding, thinking, intuitions, conscious and unconscious, abilities for formation of symbols and aesthetic feelings. Computational techniques are given for these mechanisms and abilities. The goal of this tutorial is to provide a basic mathematical understanding of the working of the mind. Its secondary goal is to demonstrate practical applications of these mechanisms for pattern recognition, tracking, fusion, search engines, and for integrated systems combining sensor signals and communication data. Lastly, this tutorial will outline future research directions. Historical and current difficulties in developing intelligent systems (IS) and applications will be discussed along with how the mind and new computational techniques overcome these difficulties.

Summary: The Cooperative Control of Multiagent Systems course will illustrate the various attributes needed in such systems and the complexity inherent to the design. Cooperative systems are currently limited in capacity and in availability, partly due to this so-called complexity and to the multifaceted nature of design and analysis. This course will focus on the well-known problem of multiagent path planning, with brief discussions of advanced techniques for formation flight health management. The optimization problem and its solution will be cast in the framework of dynamic programming and Markov decision processes, typical of problems of optimization under uncertainty. A discussion of the results of numerical simulations, integrating decision-making with closed-loop dynamics of the air vehicles, for both formation flight and path planning, will conclude the course.

Summary: Computational Intelligence techniques are a powerful and adaptable approach to tackle problems and cases for which the conventional technologies have not been proved sufficiently effective. These results are achieved by mimicking some aspects of the knowledge representation and processing performed by the brain. The computational efforts implied by these approaches are usually quite relevant.

Summary: This course examines Information Theory and our efforts to develop an information-theoretic criterion which can be utilized in adaptive filtering and neurocomputing. The main aim of our research is to develop new signal processing techniques by going beyond the basic assumptions of Linearity, Gaussianity and Stationarity. By capturing higher order statistics of data using Information Theory, we solve a variety of problems in Biomedical Signal Processing, Communications and Machine Learning.

Summary: The course introduces multilayer perceptrons in a self-contained way by providing motivations, architectural issues, and the main ideas behind the Backpropagation learning algorithm. In addition, the course shows how multilayer perceptrons can be successfully used in real-world applications

Summary: This course will provide an introduction to and an overview of type-2 fuzzy sets (T2 FSs) and systems. It will locate type-2 fuzzy sets and systems in an educational taxonomy, so that the student will appreciate from the onset the importance of studying such fuzzy sets; explain what a T2 FS is, how it is different from a type-1 FS, and why it is needed; provide careful definitions and pictures of the new terminology of T2 FSs; explain the importance of interval type-2 fuzzy sets over more general T2 FSs; explain important representations for a T2 FS (one is very good for computing, and another is very good for quickly developing the structure of the solution to a new theoretical problem); explain how T2 FSs are used in a rule-based system (a fuzzy logic system-FLS); describe the detailed computations that are used for an interval T2 FLS, relying mostly on graphical pictures; compare those computations with their type-1 counterparts; explain the major obstacle to using a T2 FLS in a real-time application and how that obstacle has been overcome; and wrap up the course with a plug for the applications course and a short reading list.

Summary: There are rapidly emerging needs to deal with distributed sources of data (sensors and sensor networks, web sites, databases). While recognizing their limited accessibility at a global level (associated with technical constraints and/or privacy issues) and fully acknowledging benefits of collaborative processing, we propose a concept of Collaborative Computational Intelligence (CI), and collaborative fuzzy models, in particular. The variety of possible mechanisms of interaction is organized into a setting of the C³ interaction paradigm (communication - collaboration - consensus). This helps us offer a coherent taxonomy of various schemes of interaction which in the sequel implies a certain development of a suite of algorithms. In this setting, the role granular information in the establishing of the mechanisms of interaction plays a pivotal role.
We consider distributed fuzzy models and fuzzy modeling. In particular, we elaborate on the key design issues concerning fuzzy rule-based systems with local functional models occurring at their conclusion parts and show how the fundamental modes of interaction are exploited here. It will be demonstrated that more advanced constructs such as type-2 fuzzy sets emerge naturally in distributed fuzzy modeling and come with a well-defined semantics of their membership functions by being fully reflective of the character of the underlying distributed data.
In the context of collaborative fuzzy modeling, we bring forward a concept experience-consistent fuzzy system identification showing how fuzzy models built on a basis of limited data can benefit from taking advantage of the past experience conveyed in the form of previously constructed fuzzy models. Detailed algorithmic considerations embrace several design scenarios in which we apply the mechanism of experience consistency at the level of conditions and conclusions of the rules. We also show that a level of achieved experience-driven consistency can be quantified through fuzzy sets (fuzzy numbers) of the parameters of the local models standing in the conclusion parts of the rules this leading to the emergence of granular constructs of fuzzy modeling.

Summary: This course will have a large impact on a large audience as handling uncertainties will be a very important challenge to any real world application that operate in real world changing and dynamic environments. The course will present the theoretical aspects of type-2 FLCs and how to build a type-2 FLC. The course will also present many applications in different areas ranging from Control of Marine Diesel Engines, Autonomous Outdoor mobile Robots as well as Embedded Agents and Ambient Intelligent Environments which deals with how we can embed very efficient computational intelligence and type-2 techniques in small computing and memory platforms. The course will present a very clear description of type-2 Fuzzy Logic Controllers (FLCs), their design and their various application in handling the uncertainties in various real world applications. Different examples will be provided.