Chapter 3 :
Optimal Estimate Training
For OET, the activation function f( ) should be a continuous invertible
function such as the hyperbolic tangent (tanh).
The number of processing elements in the input layer, n, is the same as the
number of processing
AI B
0 .2 + 0 .3
2
3
Note that if the algebraic product is used to define A I B
i.e. A I B ( x) = A ( x) B ( x), then A I B = 0.1 + 0.12
2
3
Smallest t-norm any t-norm largest t-norm
AUB
A = 0.6 + 0.5 + 0.3 B = 0.2 + 0.4 + 0.9 A U B = 0.6 + 0.5 + 0.4 +
0.6
NO
0.49
YES
YES
0.51
0.6
60%
40%
due to rule 1
due to rule 2
There are other ways of doing things:
e.g. simply set speed = 60%x1000+40%x500
= 800
Single value to represent the
resulting output speed
M
S
x x
o o
B
o
o
x
o
o
o
# #
o
#
x
x
x #
o
B (x)
1+ e
1
- (x - 15)
A (x)
B (x)
a number more than
two but not large
o o
o
o
o o
A (x)
o
o
o o
1 1 0.5
+ +
1 2 3
0.5 1 1 1 1 0.5
+ + + + +
3 4 5 6 7 8
0.5 1 1
+ +
8 9 10
These are not arithmetic operator
The Hopfield Network
Chapter 6:
Hopfield Neural Network
A recurrent neural network has feedback loops from its
outputs to the inputs. The presence of such loops has a
profound impact on the learning capability of the network.
How does the recurrent netw
Fundamental Questions of AI
ELEC 3503
Introduction to
Artificial Intelligence
Can machines think?
Is there mind without communication?
Is there language without living?
Is there intelligence without life?
thinking
September 2, 2013
Dr. Grantham Pang
gpang
Chapter 2 : Multilayer
Why do we need a hidden layer ?
Neural Networks
What is a multilayer neural network ?
A multilayer neural network is a feedforward neural network
with one or more hidden layers.
The network consists of an input layer of source neu
The most common basis function chosen is a Gaussian
function, in which case the activation level hj of hidden unit j
is calculated by
T
2
Chapter 5 :
Radial Basis Function Networks
h j = exp[ ( X C j ) ( X C j ) / 2 j ]
A radial basis function (RBF) netwo
Chapter 4:
Unsupervised Learning
Unsupervised Learning
Can a neural network learn without a teacher ?
The main property of ANN is an ability to learn from its
environment, and to improve its performance through
learning. BP and OET are supervised or activ
COMP 328: Machine Learning
Lecture 2: Naive Bayes Classiers
Nevin L. Zhang
Department of Computer Science and Engineering
The Hong Kong University of Science and Technology
Spring 2010
Nevin L. Zhang (HKUST)
COMP 328
Spring 2010
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Two dierent types o