类脑智能与视觉感知 华中科技大学出版社 pdf snb 115盘 kindle 在线 下载 pmlz mobi

《类脑智能与视觉感知（英文版）》主要介绍关于大脑启发的智力和视觉感知（BIVP）研究的新成果。

《类脑智能与视觉感知（英文版）》著者们收集BIVP的新的研究假设，包括介绍脑科学和视觉脑假说。此外，本文还介绍了BIVP的理论和算法，包括信息素积累与迭代、神经认知计算机制、核心模块的集成与调度、大脑感知、运动与控制。

《类脑智能与视觉感知（英文版）》不仅包含基本概念、理论和规律，以前分散在许多有影响的期刊和会议上，而且包含作者的项目中的一系列新研究进展，包括中国科学院的“西部之光”计划（XBBS-2014-16），国家自然科学院中国科学基金会（41571299）、中国“千人计划”（Y47161）、国家基础研究计划（2013CB035502）和国家高技术研究发展计划（2013AA122302）。

《类脑智能与视觉感知（英文版）》即可以供大学研究人员、研发工程师、本科生和研究生学习以及对机器人、脑认知或计算机视觉感兴趣的读者参考。

1 Introduction of Brain Cognition

1．1 Background

1．2 Theory and Mechanisms

1．2．1 Brain Mechanisms to Determine Attention Value of Information in the Video

1．2．2 Swarm Intelligence to Implement the Above Biological Mechanisms

1．2．3 Models Framework for Social Computing in Object Detection

1．2．4 Swarm Optimization and Classification of the Target Impulse Responses

1．2．5 Performance of Integration Models on a Series of Challenging Real Data

1．3 From Detection to Tracking

1．3．1 Brain Mechanisms for Select Important Objects to Track

1．3．2 Mechanisms for Motion Tracking by Brain-Inspired Robots

1．3．3 Sketch of Algorithms to Implement Biological Mechanisms in the Model

1．3．4 Model Framework of the Brain-Inspired Compressive Tracking and Future Applications

1．4 Objectives and Contributions

1．5 Outline of the Book

References

2 The Vision-Brain Hypothesis

2．1 Background

2．2 Attention Mechanisms

2．2．1 Attention Mechanisms in Manned Driving

2．2．2 Attention Mechanisms in Unmanned Driving

2．2．3 Implications to the Accuracy of Cognition

2．2．4 Implications to the Speed of Response

2．2．5 Future Treatment of Regulated Attention

2．3 Locally Compressive Cognition

2．3．1 Construction of a Compressive Attention

2．3．2 Locating Centroid of a Region of Interest

2．3．3 Parameters and Classifiers of the Cognitive System

2．3．4 Treating Noise Data in the Cognition Process

2．4 An Example of the Vision-Brain

2．4．1 Illustration of the Cognitive System

2．4．2 Definition of a Vision-Brain

2．4．3 Implementation of the Vision-Brain

References

3 Pheromone Accumulation and Iteration

3．1 Background

3．2 Improving the Classical Ant Colony Optimization

3．2．1 Model of Ants' Moving Environment

3．2．2 Ant Colony System： A Classical Model

3．2．3 The Pheromone Modification Strategy

3．2．4 Adaptive Adjustment of Involved Sub-paths

3．3 Experiment Tests of the SPB-ACO

3．3．1 Test of SPB Rule

3．3．2 Test of Comparing the SPB-ACO with ACS

3．4 ACO Algorithm with Pheromone Marks

3．4．1 The Discussed Background Problem

3．4．2 The Basic Model of PM-ACO

3．4．3 The Improvement of PM-ACO

3．5 Two Coefficients of Ant Colony's Evolutionary Phases

3．5．1 Colony Diversity Coefficient

3．5．2 Elitist Individual Persistence Coefficient

3．6 Experimental Tests of PM-ACO

3．6．1 Tests in Problems Which Have Different Nodes

3．6．2 Relationship Between CDC and EIPC

3．6．3 Tests About the Best-Ranked Nodes

3．7 Further Applications of the Vision-Brain Hypothesis

3．7．1 Scene Understanding and Partition

3．7．2 Efficiency of the Vision-Brain in Face Recognition

References

4 Neural Cognitive Computing Mechanisms

4．1 Background

4．2 The Full State Constrained Wheeled Mobile Robotic System

4．2．1 System Deion

4．2．2 Useful Technical Lemmas and Assumptions

4．2．3 NN Approximation

4．3 The Controller Design and Theoretical Analyses

4．3．1 Controller Design

4．3．2 Theoretic Analyses of the System Stability

4．4 Validation of the Nonlinear WMR System

4．4．1 Modeling Deion of the Nonlinear WMR System

4．4．2 Evaluating Performance of the Nonlinear WMR System

4．5 System Improvement by Reinforced Learning

4．5．1 Scheme to Enhance the Wheeled Mobile Robotic System

4．5．2 Strategic Utility Function and Critic NN Design

4．6 Stability Analysis of the Enhanced WMR System

4．6．1 Action NN Design Under the Adaptive Law

4．6．2 Boundedness Approach and the Tracking Errors Convergence

4．6．3 Simulation and Discussion of the WMR System

References

5 Integration and Scheduling of Core Modules

5．1 Background

5．2 Theoretical Analyses

5．2．1 Preliminary Formulation

5．2．2 Three-Layer Architecture

5．3 Simulation and Discussion

……

王文峰，中国科学院博士。 2007.8-2010.7，石河子大学数学系 2010.7-，中国科学院新疆生态与地理研究所对地观测与系统模拟实验室 2017.9-，哈工大靠前机器人创新研究院（合肥）人工智能研发与推广中心副主任2016.7-，兼任中国自动化学会认知计算与系统专委会和中国人工智能学会认知系统与信息处理专委会委员，国家自然科学基金评审专家，多个SCI期刊审稿专家

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书籍介绍

In the 19th century, Spanish anatomists founded the theory of neurons. With the development of brain science, the biological characteristics of neurons and related electrical properties have been discovered. The advent of mathematical methods to simulate the actual human neural network in 1943 can be recognized as one of the notable landmarks. 63 years since then, deep neural networks were proposed and developed to simulate the structure of the human cerebral cortex. The emergence of deep learning has a great influence on the traditional artificial intelligence and enhanced the importance of brain-inspired intelligence in the whole field of artificial intelligence. This is a great dream into reality!