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Klappentext Neural Networks and Intellect: Using Model-Based Concepts describes a new mathematical concept of modeling field theory and its applications to a variety of problems. Examining the relationships among mathematics! computations in neural networks! signs and symbols in semiotics! and ideas ofmind in psychology and philosophy! this unique text discusses deep philosophical questions in detail and relates them to mathematics and the engineering of intelligence. Ideal for courses in neural networks! modern pattern recognition! and mathematical concepts of intelligence! it will also be ofinterest to anyone working in a variety of fields including neural networks! AI! cognitive science! fuzzy systems! pattern recognition and machine/computer vision! data mining! robotics! target tracking! and financial forecasting. Neural Networks and Intellect describes model-based neural networks that utilize the intriguing concept of an internal "world" model! an idea that originated in artificial intelligence and cognitive psychology but whose roots date back to Plato and Aristotle. Combining the a priori knowledgewith adaptive learning! the new mathematical concept addresses the most perplexing problems in the field of neural networks: fast learning and robust generalization. The author provides an overview of computational intelligence and neural networks! relating hundreds of seemingly disparate techniquesto several fundamental mathematical concepts! which are in turn linked to concepts of mind in philosophy! psychology! and linguistics. Topics covered include the hierarchical and heterarchical organization of intelligent systems! statistical learning theory! genetic algorithms! complex adaptivesystems! mathematical semiotics! the dynamic nature of symbols! Godel theorems and intelligence! emotions and thinking! the mathematics of emotional intellect! consciousness! and more. Perlovsky's remarkable conclusion is that the work of ancient philosophe Zusammenfassung This work describes a new mathematical concept of modeling field theory and its applications to a variety of problems while offering a view of the relationships among mathematics, computational concepts in neural networks, semiotics, and concepts of mind in psychology and philosophy. The book is directed towards a diverse audience of students, teachers, researchers, and engineers working in the areas of neural networkss, artificial intelligence, cognitive science, fuzzy systems, pattern recognition and machine/computer vision, data mining, robotics, target tracking, sensor fusion, spectrum analysis, time series analysis, and financial market forecasting. Mathematically inclined philosophers, semioticians, and psychologists will also find many areas of interest. Modeling field neural networks utilize internal "world" models. The concept of internal models of the mind originated in artifical intelligence and cognitive psychology, but its roots date back to Plato and Aristotle. Intelligent systems based on rules utlize models in their final conceptual forms of rules. Like the Eide (Ideas) of Plato, rules lack adaptivity. In modeling field theory, the adaptive models are similar to the Forms of Aristotle and serve as the basis for learning. By combining the a priori knowledge with learning, the most perplexing problems in field of neural networks and intelligent systems are addresses: fast learning and robust generalization. The new mathematics describes a basic instinct for learning and the related affective signals in the learning process. An ability to perceive beauty is shown to be an essential property of adaptive system related to the instinct for learning. The combination of intuition with mathematics provides the foundation of a physical theory of mind. The book reviews most of the mathematical concepts and engineering approaches to the development of intelligent systems discussed since the 1940s. The origin of the Aristotelian mathema...
