... Exercises 22
2 OptimalControl 23
2.1 OptimalControl Problems with a Fixed Final State 24
2.1.1TheOptimalControlProblemofTypeA 24
2.1.2Pontryagin’sMinimumPrinciple 25
2.1.3Proof 25
2.1.4 Time -Optimal, ... nicely reveals that the solution of an optimalcontrol prob-
lem always is “as bad” as the considered formulation of the optimal control
problem. This optimalcontrol problem lacks any sustainability ... Time-Invariant Case with Infinite Horizon 83
3.3 ApproximativelyOptimalControl 86
3.3.1 Notation 87
3.3.2Lukes’Method 88
3.3.3 Controller with a Progressive Characteristic 92
3.3.4LQQSpeedControl 96
3.4...
... Statistics and Probability
for EngineeringApplications
With Microsoft
®
Excel
by
W.J. DeCoursey
College of Engineering,
University of Saskatchewan
Saskatoon
Amster ...
variance of population
xv
Chapter 2
Example 2.2
Three nuts with metric threads have been accidentally mixed with twelve nuts with
U.S. threads. To a person taking nuts from a bucket, all ... population, the sample mean would be a good approximation of the population
mean, with no systematic error but with a random error which tends to become
smaller as the sample size increases....
... for the Solution of
Optimal Control Problems,"
IEEE
Transactions on Automatic Control,
Vol. AC-17, NO. 5, pp. 591-597, 1972.
14
OPTIMIZATION AND CONTROL
WITH
APPLICATIONS
Take now ... Variations Algorithms
for OptimalControl Problems with Terminal Inequality Constraints,"
J.
xxiv
OPTIMIZATION AND CONTROLWITHAPPLICATIONS
Optimization Theory and Applications, Vol. 16, No. ...
OPTIMIZATION
AND
CONTROL WITHAPPLICATIONS
262.
A.
Brockwell,
E.
Polak, R. Evans, and
D.
Ralph, "Dual-Sampling-Rate
Moving Horizon Control of a Class of Linear Systems with Input Satura-...
... network applications in
industrial and control engineering.
This second volume begins with a part of artifi cial neural network applications in tex-
tile industries which are concerned with the ...
Contents
VII
Control and Robotic Engineering 357
Artificial Neural Network –
Possible Approach to Nonlinear System Control 359
Jan Mareš, Petr Doležel and Pavel Hrnčiřík
Direct Neural Network Control ...
spinning ends-
down and neps
Artificial Neural Networks - Industrial and ControlEngineeringApplications
4
2. Applications to fibres and yarns
2.1 Fibre classification
Kang and Kim (2002)...
... of the safe
knitted fabric without any knitting faults, tightened fibers with uniform configuration, big
faults with less area, non-uniform and extended faults with spread configuration, and ... Industrial and ControlEngineeringApplications
52
strength irregularity, breaking elongation and breaking elongation irregularity as input
layer and warp breakage rates as output layer in controlled ... system. They
made a comparison with two different network architectures, one with two sequential
networks working in tandem fed with a common input and another with a single network
that gave...
... Networks - Industrial and ControlEngineeringApplications
120
, where x
i
is the input of node j of the input layer, W
ij
is the connection weight associated
with node i of the input layer ... the
corresponding spectral outputs within the full width half-maximum (FWHM) linewidth.
Artificial Neural Networks - Industrial and ControlEngineeringApplications
124
Δε/2 = Δσ/2E+(Δσ/2K')
1/n' ... matrix effect. This, in fact, means that materials with the same elemental
Artificial Neural Networks - Industrial and ControlEngineeringApplications
102
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7...
... of Carbon Material. Key Engineering Materials, Vol.385-387, (July
2008), pp. 385-387, ISSN 1662-9795
Artificial Neural Networks - Industrial and ControlEngineeringApplications
154
1.1 ...
Artificial Neural Networks - Industrial and ControlEngineeringApplications
162
The depicting effect of mentioned factors and their interactions with one another, two
parameters were altered ... 1420°C and the holding time of 80min, while
Artificial Neural Networks - Industrial and ControlEngineeringApplications
158
2.5 Experimental database
Since an ANN model is empirical, its performance...
... concentration.
Artificial Neural Networks - Industrial and ControlEngineeringApplications
174
4.2 Multi-variable relationships of GCV with ultimate and proximate analysis
parameters
The best-correlated ... Industrial and ControlEngineeringApplications
192
Once the artificial neural network is trained, which means that all of the weights and bias
are set, it can be tested. First with the patterns ... detection of the
transformer’s iron core
Artificial Neural Networks - Industrial and ControlEngineeringApplications
198
Sabate, J. A.; Vlatkovic, V.; Ridley, R. B.; Lee, F. C. & Cho,...
... noses, infrared spectroscopy upgraded with
machine learning methods (ANN, genetic algorithms).
Artificial Neural Networks - Industrial and ControlEngineeringApplications
222
by A. niger in ... and controlled simultaneously,
and it is quite difficult to derive classical structured models, on account of practical
Artificial Neural Networks - Industrial and ControlEngineeringApplications ... Networks - Industrial and ControlEngineeringApplications
226
objects from different classes or having different properties). Clusters and empty spaces can
be inspected without prior knowledge...
... infrared; IR - infrared.
Table 4. Other applications of ANN in meat science and technology
Artificial Neural Networks - Industrial and ControlEngineeringApplications
260
smoothen the response ... neural network based fuzzy
logic control systems [J], IEEE Trans. Fuzzy Syst, 1994, 2(1): 46–63
Artificial Neural Networks - Industrial and ControlEngineeringApplications
266
()
1
1
1
()
()
()
s
s
s
Ah ... Artificial Neural Networks - Industrial and ControlEngineeringApplications
264
01x
f
ff
=
+ (20)
Fig. 2. Impulse response of the Hamming window with 20 Hz cut frequency
3. Complex ADALINE...
... the ANN output
in this thesis is compared well with the result of MNE Method.
Artificial Neural Networks - Industrial and ControlEngineeringApplications
276
approach has a suitable response ... of the error as measured on the current pattern
with respect to each weight:
Artificial Neural Networks - Industrial and ControlEngineeringApplications
302
2 4 6 8 10 12 14 16 18 20 22 ... reactive power transfer between generators and loads with almost similar
accuracy.
Artificial Neural Networks - Industrial and ControlEngineeringApplications
286
where
'
Y is the modified...
... overall measured output.
Fig. 22. Layer approach with correlating divisions
Artificial Neural Networks - Industrial and ControlEngineeringApplications
326
divided into quarters accordingly ... Networks - Industrial and ControlEngineeringApplications
318
general decreasing trend is recognized whose characteristic seems to result from the
increase of SOI timing. With more advanced SOI ... estimation in correlation to
measured data
Part 5
Mechanical Engineering
Artificial Neural Networks - Industrial and ControlEngineeringApplications
336
Fig. 1a. Structure of SFF model
...