细胞式神经网络的通用性与新兴计算UNIVERSALITY AND EMERGENT COMPUTATION IN CELLULAR NEURAL NETWORKS

作者： Radu Dogaru著

出 版 社： Pengiun Group (USA)

出版时间： 2003-12-1字数：版次： 1页数： 246印刷时间： 2003/01/01开本：印次： 1纸张： 胶版纸I S B N ： 9789812381026包装： 精装内容简介

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.

目录

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 theory

2. 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 remarks

3. 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 remarks

4. 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 Automata

6 Unconventional Applications:Biometric Authentication

References

Index