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CANFIS
Coactive Neuro Fuzzy Inference
systems
G.Anuradha
Introduction
• Highlights the extensions of anfis
• Multiple output anfis with nonlinear fuzzy
rules
• Generalized anfis is called as CANFIS
• In CANFIS both NN and FIS play an active
role in a effort to reach a specific goal
Framework
• Towards multiple inputs/outputs systems
• Architectural comparisons
Towards multiple inputs/outputs
systems
• Canfis has extended the notion of single-
output system of ANFIS to produce
multiple outputs.
• One way to accomplish is to place as
many ANFIS models side by side as the
number of required outputs.
Canfis
• In CANFIS the antecedents are the same,
but the consequents are different
according the number of outputs required.
• Fuzzy rules are constructed with shared
membership values to express
correlations between outputs.
Multiple ANFIS
• In MANFIS no modifiable parameters are
shared by the juxtaposed ANFIS models.
• Each anfis has an independent set of
fuzzy rules, which makes it difficult to
realize possible correlations between
outputs.
• Also the adjustable parameters increases
with the increase in the number of outputs
Canfis

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Canfis

  • 1. CANFIS Coactive Neuro Fuzzy Inference systems G.Anuradha
  • 2. Introduction • Highlights the extensions of anfis • Multiple output anfis with nonlinear fuzzy rules • Generalized anfis is called as CANFIS • In CANFIS both NN and FIS play an active role in a effort to reach a specific goal
  • 3. Framework • Towards multiple inputs/outputs systems • Architectural comparisons
  • 4. Towards multiple inputs/outputs systems • Canfis has extended the notion of single- output system of ANFIS to produce multiple outputs. • One way to accomplish is to place as many ANFIS models side by side as the number of required outputs.
  • 6. • In CANFIS the antecedents are the same, but the consequents are different according the number of outputs required. • Fuzzy rules are constructed with shared membership values to express correlations between outputs.
  • 8. • In MANFIS no modifiable parameters are shared by the juxtaposed ANFIS models. • Each anfis has an independent set of fuzzy rules, which makes it difficult to realize possible correlations between outputs. • Also the adjustable parameters increases with the increase in the number of outputs