細(xì)胞式神經(jīng)網(wǎng)絡(luò)的通用性與新興計(jì)算UNIVERSALITY AND EMERGENT COMPUTATION IN CELLULAR NEURAL NETWORKS

內(nèi)容概要

Cellular computing is a natural information processing paradigm, capable of modeling various biological, physical and social phenomena, as well as other kinds of complex adaptive systems. The programming of a cellular computer is in many respects similar to the genetic evolution in biology, the result being a proper cell design and a task-specific gene.     How should one "program" the cell of a cellular computer such that a dynamic behavior with computational relevance will emerge? What are the "rules" for designing a computationally universal and efficient cell?     The answers to those questions can be found in this book. It introduces the relatively new paradigm of the cellular neural network from an original perspective and provides the reader with the guidelines for understanding how such cellular computers can be "programmed" and designed optimally. The book contains numerous practical examples and software simulators, allowing readers to experiment with the various phases of designing cellular computers by themselves.

書(shū)籍目錄

1. Introduction  1.1. Emergent computation as a universal phenomena  1.2. Emergence  1.3. Cellular computing systems  1.4. Universality  1.5. Designing for emergence, the essence of this book  1.6. Detecting the potential for emergence: the local activity theory2. Cellular Paradigms: Theory and Simulation  2.1. Cellular systems  2.2. Major cellular systems paradigms    The Cellular Neural Network (CNN) model    The Generalized Cellular Automata    Reaction-Diffusion Cellular Nonlinear Networks  2.3. Matlab simulation of generalized cellular automata    Uncoupled GCAs    Coupled GCAs    Simulation of standard cellular neural networks  2.4. Simulation of Reaction-Diffusion Cellular Neural Networks  2.5. Concluding remarks3. Universal Cells  3.1. Universality and cellular computation, basic ideas      Boolean universal cells      The simplicial cell - universality expanded to continuous states  3.2. Binary cells    3.2.1. What would be an "ideal" binary CNN cell?      Universality      Compactness      Robustness      Capability of evolution    3.2.2. Orientation s and Projection Tapes      Local binary computation      Projections      Orientations      Projection tapes      Default orientations      Valid and non-valid projection tapes      Transitions and robust transitions      Finding the optimal orientation      Optimal orientations for totalistic and semi-totalistic      Boolean functions    3.2.3. Universal cells with canonical discriminants    3.2.4. Compact universal cells with multi nested discriminants      Bifurcation tree for multi-nested discriminant function      Uniform multi-nested cell s and their bifurcation trees      The uniform multi-nested discriminant as an analog-to-digital      converter      Uniform orientations and projection tapes      Boolean realizations: an analytic approach      Finding the genes for arbitrary Boolean functions      Other random search methods  3.3. Continuous state cells    3.3.1. Overview    3.3.2. Some theoretical issues on simplicial neural cells      Relationships with fuzzy logic      Training and testing samples      Quantization of gene's coefficients    3.3.3. Circuit implementation issues      Considerations regarding the implementation of the local      Boolean logic      Software implementations    3.3.4. A general procedure for training the simplicial cell    3.3.5. Functional capabilities and applications      Square scratch removal      Median Filters      Edge detection      Pattern classification    3.3.6. Nonlinear expansion of the input space    3.3.7. Comparison with multi-layer perceptrons  3.4. Concluding remarks4. Emergence in Continuous-Time Systems:  Reaction-Diffusion Cellular Neural Networks  4.1. The theory of local activity as a tool for locating emergent behaviors  4.2. Narrowing the search, "Edge of chaos" domains  4.3. The methodology of finding "edge of chaos" domains    4.3.1. Four steps precluding the local activity testing    4.3.2. The concept of local activity    4.3.3. Testing for stable and unstable local activity      Local activity test for one diffusion coefficient      Local activity test for two diffusion coefficients    4.3.4. Unrestricted versus restricted local activity, the edge of chaos      Unrestricted local activity and passivity      The Edge of Chaos    4.3.5. Bifurcation diagrams      One-diffusion coefficient case      The two-diffusion case    4.3.6. Emergent behaviors near and within the "edge of chaos"      Mapping the Edge of Chaos        Static and dynamic patterns on the Edge of Chaos        Homogeneous static patterns      Turing-like patterns        Spiral wave patterns        Information computation patterns        Periodic dynamic patterns……5 Emergence in Discrete-Time Systems:Generalized Cellular Automata6  Unconventional Applications:Biometric AuthenticationReferencesIndex

圖書(shū)封面

評(píng)論、評(píng)分、閱讀與下載

---

    細(xì)胞式神經(jīng)網(wǎng)絡(luò)的通用性與新興計(jì)算UNIVERSALITY AND EMERGENT COMPUTATION IN CELLULAR NEURAL NETWORKS PDF格式下載

---