PrefaceDL was developed in our research group over the past 15, or so, years. The book disseminates this breakthrough mathematical-engineering idea, which results in 100 times improvement and better in classical algorithmic areas that have been intensively studied for decades. Initial developments in DL were described in "Neural Networks and Intellect," by L. Perlovsky, Oxford University Press, 2001 (which is now in the 3rd printing). The current book describes new breakthrough results developed during the last eight years. First we present the basic technique of DL, explain the fundamental mathematical reason why classical techniques in many areas fail for real-world problems, and how DL overcomes this difficulty. We discuss the algorithmic failure of many techniques to reach informationtheoretic performance bounds, relate it to computational complexity, and ultimately to the Gödel theory (it turns out that all past algorithms, neural networks, fuzzy systems, used logic at some step and were subject to Gödelian limitations).Then we describe a number of applications where significant breakthrough improvements were achieved over popular state-of-the-art techniques (detection, clustering, supervised and unsupervised learning, tracking, sensor fusion, prediction, and particularly financial prediction). We follow with novel engineering areas, where revolutionary results were obtained. The theory is extended toward mathematical modeling of the mind, including higher cognitive functions, beyond anything that has been published in engineering books (no competition): mechanisms of the mind-brain (recent neuroimaging experiments proved that brain is actually using DL computations), applications to learning natural language, to language-understanding search engines for the Internet, to modeling interactions between language and cognition, language and emotions, evolution of languages, evolution of cultures, the role of music in evolution of the mind and cultures.The mind is the best mechanism for solving complex engineering problems. Therefore, it is just natural that developing engineering algorithms and modeling the mind goes hand in hand. Solving complex engineering problems helps understand working of the mind, and cognitively-inspired algorithms work better than classical engineering methods. This approach to engineering is called computational intelligence.The book is based on about 200 papers published over the last several years describing DL and its applications. Many of them were important events attracting attention and receiving awards. Every book chapter is written anew, all are unified by a common theme -mathematical technique of dynamic logic and by consistent notations. The book is written for students as well as seasoned professionals, it VI Preface contains details about applications, algorithms, notations, flowcharts, details that are missing in the papers. DL is easy to use as a textbook or manual. Engineering improvements achieved make it stand out over other texts.The book contains two parallel tracks...
