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Design of Hybrid Fuzzy Neural Network for Function Approximation

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Figure

Figure 1. Architecture of subsethood based FNN model
Figure 3. Fuzzification of numeric input
Figure 6. (a) Mesh plot and counters of 900 training pat-terns; (b) mesh plot and counters of 400 testing
Figure 7. f(d) error surface for 5 rules; (e) error surface for 10 rules; (f) error surface for 15 rules; (g) f(x, y) surface for 20 rules; (h) f(x, y) surface for 30 rules; (i) f(x, y) surface for 50 rules; (j) error surface for 20 rules; (k) error surface for 30 rules; (l) error surface for 50 rules(x, y) surface plot and their corresponding testing error surface after 250 epochs for different rule counts w learning scheduace for 5 rul; (b) f(x, y) surfacce for 15 rules; ithle as LS = 0.01, (a) f(x, y) surfese for 10 rules; (c) f(x, y) surfa
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