西安电子科技大学：《神经网络与模糊系统 Neural Networks and Fuzzy Systems》课程PPT课件讲稿（2006）Chapter 04-1 Synaptic Dynamics：Unsupervised Learning Part Ⅰ

Synaptic Dynamics: Unsupervised Learning Part I Xiao Bing 三D

Synaptic Dynamics: Unsupervised Learning Part Ⅰ Xiao Bing

Input Output 处理 单元 Input Output 处理 单元

处理 单元 处理 单元 Input Input Output Output

outline 。Learning Supervised Learning and Unsupervised Learning Supervised Learning and Unsupervised Learning in neural network Four Unsupervised Learning Laws

outline • Learning • Supervised Learning and Unsupervised Learning • Supervised Learning and Unsupervised Learning in neural network • Four Unsupervised Learning Laws

outline ·Learning Supervised Learning and Unsupervised Learning Supervised Learning and Unsupervised Learning in neural network Four Unsupervised Learning Laws

outline • Learning • Supervised Learning and Unsupervised Learning • Supervised Learning and Unsupervised Learning in neural network • Four Unsupervised Learning Laws

Learning Encoding A system learns a pattern if the system encodes the pattern in its structure. Change 。 A system learns or adapts or“self-organizes” when sample data changes system parameters. ·Quantization A system learns only a small proportion of all patterns in the sampled pattern environment,so quantization is necessary

Learning • Encoding A system learns a pattern if the system encodes the pattern in its structure. • Change A system learns or adapts or “self -organizes” when sample data changes system parameters. • Quantization A system learns only a small proportion of all patterns in the sampled pattern environment, so quantization is necessary

Learning II 。Encoding: A system learns a pattern if the system encodes the pattern in its structure. 。 Change: A system learns or adapts or "self -organizes"when sample data changes system paramefers. Quantization A system learns only a small proportion of all patterns in the sampled environment

Learning • Encoding: A system learns a pattern if the system encodes the pattern in its structure. • Change: A system learns or adapts or “self -organizes” when sample data changes system parameters. • Quantization A system learns only a small proportion of all patterns in the sampled pattern environment

Encoding A system has Learned a stimulus- response)pair y五 ·If(xy,) is a sample from the functjor"-R A system has learnedf if the system responses withy for x y=f(x) ,and

Encoding • A system has Learned a stimulus￾response pair ( , ) i i x y S xi yi • If is a sample from the function A system has learned if the system responses with for all ,and . n p ( , ) x y i i f : R → R f y x y = f (x)

Encoding Close to X Close to y,y=f(x A system has partially learned or approximated the function

Encoding x ′ y ′ x S Close to Close to , y • A system has partially learned or approximated the function . f y = f (x)

Learning Encoding: A system learns a pattern if the system encodes the pattern in its structure. ·Change: A system learns or adapts or "self organizes"when sample data changes system parameters. Quantization A system learns only a small proportion of all patterns in the sampled pattern environment

Learning • Encoding: A system learns a pattern if the system encodes the pattern in its structure. • Change: A system learns or adapts or “self - organizes” when sample data changes system parameters. • Quantization A system learns only a small proportion of all patterns in the sampled pattern environment

Change 。·We have learned calculus if our calculus- exam-behavior has changed from failing to passing. A system learns when pattern stimulation change a memory medium and leaves it changed for some comparatively long stretch of time

Change • We have learned calculus if our calculus￾exam-behavior has changed from failing to passing. • A system learns when pattern stimulation change a memory medium and leaves it changed for some comparatively long stretch of time