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首页六十年代里程碑:第一代神经网络详解
"第一代神经网络,起源于20世纪60年代,是人工智能领域的一个里程碑。这一时期的神经网络结构相对简单,由三个基本层次构成:输入层、隐藏层和输出层。这些层次的设计模仿了人脑神经元的工作原理,每个层次都有特定的功能。 输入层接收原始数据,比如图像像素或声音波形,作为神经网络处理的起点。隐藏层则是关键部分,它包含多个节点,每个节点通过一系列权重连接到输入层,同时与下一层的节点相连。这些节点通过激活函数处理输入信号,模拟人类大脑神经元之间的复杂相互作用。 输出层根据隐藏层的处理结果生成最终的预测或决策,可能是数字输出(如分类标签)、连续值(如回归预测)或者更复杂的模式。第一代神经网络的训练通常采用反向传播算法,该算法通过调整连接权重来最小化预测误差,使网络逐步逼近最佳解决方案。 尽管第一代神经网络相比现代深度学习模型要浅得多,但它们为后续的研究奠定了基础。这些早期的尝试揭示了神经网络潜力,引发了对深度学习的探索,即后来的多层神经网络,它们能够处理更复杂的数据结构和任务,如图像识别、自然语言处理等。 第一代神经网络作为深度学习的历史教材,不仅展示了技术发展初期的创新思维,也提供了理解现代深度学习架构演变的重要视角。阅读关于第一代神经网络的书籍,可以帮助读者深入理解机器学习的核心原理,并欣赏到人工智能技术从简到繁的演进过程。"
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Page
Number
13.
THREE-LAYER
SYSTEMS
WITH
VARIABLE
S-A
CONNECTIONS
(cont'd)
13.3
Back-Propagating
Error
Correction
Procedures
292
13.4
Simulation
Experiments
298
14.
SUMMARY
OF
THREE-LAYER
SERIES-COUPLED
SYSTEMS:
CAPABILITIES
AND
DEFICIENCIES
303
PART
III
-
MULTI-LAYER
AND
CROSS-COUPLED
PERCEPTRONS
15.
MULTI-LAYER
PERCEPTRONS
WITH
FIXED
PRE-
TERMINAL
NETWORKS
313
15.
1
Multi-layer
Binomial
and
Poisson
Models
316
15.2
The
Concept
of
Similarity-Generalization
320
15.2.1
Similarity
Classes
321
15.
2.
2
Measurement
of
Similarity,
Objective
and
Subjective
324
15.
3
Four-Layer
Systems
with
Intrinsic
Similarity
Generalization
326
15.
3.
1
Perceptron
Organization
326
15.
3.
2
Analysis
329
15.
3.
3
Example.-
338
15.4
Laws
of
Similarity-Generalization
in
Perceptrons-
342
16.
FOUR-LAYER
PERCEPTRONS
WITH
ADAPTIVE
PRETERMINAL
NETWORKS
345
16.
1
Description
of
the
Model
346
16.2
General
Analysis
34£
16.2.
1
Development
of
the
Steady-State
Equation
348
16.
2.
2
A
Numerical
Example
360
16.
3
Organization
of
Dichotomies
364
16.4
Organization
of
Multiple
Classes
370
16.
5
Similarity
Generalization
375
16.6
Analysis
of
Value-Conserving
Models
385
16.
6.
1
Analysis
of
j
-systems
386
16.
6.
2
Analysis
of
T
-systems
388
16.
7
Functionally
Equivalent
Models
389
-xv-
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Page
Number
17.
OPEN-LOOP
CROSS-COUPLED
SYSTEMS
391
17.
1
Similarity-Generalizing
Systems:
An
Analog
of
the
Four-Layer
System
391
17.2
Comparison
of
Four-Layer
and
Open-Loop
Cross-Coupled
Models
393
17.
3
Reduction
of
Size
Requirements
for
Universal
Perceptrons
401
18.
Q-FUNCTIONS
FOR
CROSS-COUPLED
PERCEPTRONS
403
18.1
Stimulus
Sequences:
Notation
404
18.2
Q-
Functions
and
Stability
407
18.3
:\
Functions
413
19.
ADAPTIVE
PROCESSES
IN
CLOSED-LOOP
CROSS-
COUPLED
PERCEPTRONS
421
19.
1
Postulated
Organization
and
Dynamics
422
19.2
The
Phase
Space
of
the
A-units
423
19.
3
The
Assumption
of
Finite
Sequences
425
19.4
General
Analysis:
The
Time-Dependent
Equation
427
19.
5
Steady
State
Solutions
435
19.6
Analysis
of
Finite-Sequence
Environments
441
19.7
Analysis
of
Continuous
Periodic
Environments
446
19.8
Analysis
of
Continuous
Aperiodic
Environments
451
19.9
Cross-Coupled
Perceptrons
with
Value-Conservation
453
19.9.1
Analysis
of
<r-systems
453
19.
9.
2
Analysis
of
/"
-systems
456
19.
10
Similarity
Generalization
Experiments
457
19.
11
Comparison
of
Cross-Coupled
and
Multi-Layer
Systems
462
20.
PERCEPTRONS
WITH
CROSS-COUPLED
S
AND
R-SYSTEMS
465
20.1
Cross-coupled
S-units
465
20.2
Cross-coupled
R-units
466
-XVI
-
![](https://csdnimg.cn/release/download_crawler_static/11016954/bg12.jpg)
Page
Number
PART
IV
-
B
A
CK-COUPLED
PERCEPTRQNS
AND
PROBLEMS
FOR
FUTURE
STUDY
21.
BACK-COUPLED
PERCEPTRONS
AND
SELECTIVE
ATTENTION
471
21.
1
Three-Layer
Systems
with
Fixed
R-A
Connections
472
21.1.1
Single
Modality
Input
Systems
472
21.1.2
Dual
Modality
Input
Systems
481
21.2
Three-Layer
Systems
with
Variable
R-A
Connections
485
21.
2.
1
Fixed
Threshold
Systems
485
21.
2.
2
Servo-Controlled
Threshold
Systems
489
21.
3
Linguistic
Concept
Association
in
a
Four-Layer
Perceptron
493
22.
PROGRAM-LEARNING
PERCEPTRONS
499
22.
1
Learning
Fixed
Response
Sequences
500
22.
2
Conditional
Response
Sequences
502
22.
3
Programs
Requiring
Data
Storage
504
22.4
Attention-Scanning
and
Perception
of
Complex
Objects
506
22.
5
Recognition
of
Abstract
Relations
508
23.
SENSORY
ANALYZING
MECHANISMS
511
23.
1
Visual
Analyzing
Mechanisms
512
23.
1.
1
Local
Property
Detectors
512
23.
1.2
Heirarchical
Retinal
Field
Organizations
522
23.
1.
3
Sequential
Observation
Programs
530
23.
1.4
Sampling
of
Sensory
Parameters
532
23.
1.
5
"Mixed
Strategies"
and
the
Design
of
General
Purpose
Systems
535
23.
2
Audio-Analyzing
Mechanisms
537
23.
2,
1
Fourier
Analysis
and
Parameter
Sampling
537
23.
2.
2
A
Phoneme-Analyzing
Perceptron
538
23.2.
3
Melodic
Bias
in
a
Cross-Coupled
Aadio-Perceptron
546
24.
PERCEPTION
OF
FIGURAL
UNITY
549
-xvii-
![](https://csdnimg.cn/release/download_crawler_static/11016954/bg13.jpg)
Page
Number
25.
VARIABLE-STRUCTURE
PERCEPTRONS
557
25.
1
Structural
Modification
of
S-A
Networks
557
25,2
Systems
with
Make-Break
Mechanisms
for
Synaptic
Junctions
560
26.
BIOLOGICAL
APPLICATIONS
OF
PERCEPTRON
THEORY
563
26,
1
Biological
Methods
for
the
Achievement
of
Complex
Structures
563
26,
2
Basic
Types
of
Memory
Processes
565
26.
3
Physical
Requirements
for
Biological
Memory
Mechanisms
567
26.4
Mechanisms
of
Motivation
571
27.
CONCLUSIONS
AND
FUTURE
DIRECTIONS
573
27.
1
Psychological
Properties
in
Neurodynamic
Systems
575
27,
2
Strategy
and
Methodology
for
Future
Study
577
27.
3
Construction
of
Physical
Models
and
Engineering
Applications
581
27.
4
Concluding
Remarks
584
APPENDICES
APPENDIX
A
-
NOTATION
AND
STANDARD
SYMBOLS
1.
Notational
Conventions
2.
Standard
Symbols
APPENDIX
B
-
LIST
OF
THEOREMS
AND
COROLLARIES
APPENDIX
C
-
BASIC
EQUATIONS
APPENDIX
D
-
STANDARD
DIAGNOSTIC
EXPERIMENTS
589
589
590
595
603
607
REFERENCES
DISTRIBUTION
LIST
609
617
XV1U
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PART
I
DEVELOPMENT
OF
BASIC
CONCEPTS
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