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Triticale (Triticosecale) a hybrid of wheat (Triticum) and rye
(Secale) is an important cereal crop and has more production as
compared to wheat. It has gained importance in fulfilling the
increased food demands of the world. For a breeding programme
genetic variability and the genetic relationship among genotypes is
important. To study this, 70 lines of triticale introduced from
CIMMYT were planted in complete block design. Data was recorded for
plant height, spike length, number of spikelets per spike, number
of grains per spike, number of tillers per meter, grain yields per
meter and 1000 grain weight. The data showing significant
differences was subjected correlation and cluster analysis.
Correlation analysis gave the results that plant height had
negative and non-significant correlation with grain yield per
meter. It was found that spike length had positive and significant
correlation with grain yield per meter. Number of spikelets per
spike and grain yield per meter was reported to have positive and
significant correlation whereas number of grains per spike had
positive and highly significant correlation.
Starting inrush current and pulsations in the inducedtorque affect
the performance of an induction motor.Artificial neural networks
(ANNs) and adaptive neurofuzzy inference system (ANFIS) can enhance
theperformance of the motor by making a control systemwhich would
provide smooth starting to inductionmotor. Dynamic model of
induction machine indifferent frames of reference was implemented
usingMatlab Simulink. Feed forward back propagation basedand radial
basis neural networks were trained, withdata obtained using
simulations, to estimatedifferent parameters required by ANFIS to
adjustfiring angle of back-to-back connected pairs ofthyristors in
AC voltage controller. Inrush currentand pulsations in torque were
reduced significantly.Radial basis and feed forward neural networks
werecompared for off-line and on-line training, trainingtime,
memory required for implementations, number ofneurons,
computational procedures and algorithms, reliability of the system
and most important cost ofimplementation. Artificial neural
networks andAdaptive neuro fuzzy inference system were
developedusing tool boxes in Matlab Simulink
Numerical simulations of sheet metal forming process based on
finite element method (FEM) is widely applied for its powerful
capability in forming process prediction. Since there are
parameters which could affect the result of forming process, it
becomes important to approach a set of parameters to improve the
formability. In the present work, first, a comprehensive literature
review was made for different optimization methodologies in sheet
metal forming process. Then we proposed an optimization methodology
using Response Surface methodology (RSM) and Genetic Algorithm (GA)
for the optimization of sheet metal forming process, and the theory
of RSM and GA are illustrated. The presented method was first
applied to an example from literature, the results verified the
feasibility of the proposed methodology. Then the methodology was
applied to variable binder force optimization of the NUMISHEET'93
2D draw bending problem. The work indicated that the proposed
optimization methodology is efficient and universal, which means it
can also be applied in other applications of aerospace industry.
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