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HYBRID SYSTEMS
Hybrid systems are the systems which use
more than one technology to solve a particular
problems.
Some of the hybrid systems are,
i) Neuro-Fuzzy hybrids
ii) Neuro-Genetic hybridsiii) Fuzzy-Genetic hybrids
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Contd.,
Neural Networks:
Are highly simplified models of the human
nervous system which mimic our ability toadopt to circumstances
and learn from past
experience
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NEURO-FUZZY HYBRIDS
NN+FUZZY =NEURO FUZZY HYBRIDS
Deals with uncertainty
Merits of NN:
Can model complex nonlinear relationships and areappropriately
suited for classification phenomenon in to
predetermined classes.
Demerits:
Precision of outputs is limited
Training time required is large.
Training data has to be chosen carefully.
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Contd.,
Merits of Fuzzy logic:
fuzzy logic systems address the imprecision
of inputs and outputs directly by definingthem using fuzzy
sets
Greater flexibility
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Neuro-Fuzzy hybrids
Can be used to accomplish the specification of
mathematical relationships among numerous
variables in a complex dynamic process,
performing mapping with some degree ofimprecision.
To control non-linear problems.
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Techniques
One is to endow NNs with fuzzy
capabilities,thereby increasing the networks
expressiveness and flexibility to adapt to
uncertain environments.
Second is to apply neuronal learning
capabilities to fuzzy systems to make the fuzzy
systems more adaptive to changingenvironments.
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NEURO-GENETIC HYBRIDS
NN+GENETIC=NEURO-GENETIC
Genetic algorithms encode the parameters ofNNs as a string of
the properties of the
network,that is chromosomes. A large population of chromosomes
representing
the many possible parameter sets for the given
NN is generated.
Combined GA-NN have the ability to locate theneighbourhood of an
optimum solution quicker
than others.
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Drawbacks of GANN
The large amount of memory required to
handle and manipulate chromosomes for a
given network.
The size of the networks become large.
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