(1) Statement: Classify input vowel patterns into
two category of vowel, VOWEL-A and VOWEL-B
(2) Initialize: Smoothing parameter
(Determined from observation of successful learning)
(3) Output of each pattern unit,
( is the weight vector)
(4) Find neuron activation function by performing non-linear operation of the form
(5) Sum all the for category VOWEL-A and do the same for category VOWEL-B
(6) Take binary decision for the two summation outputs with variable weight given by-
and Priori probability of occurrence of pattern from VOWEL-A and VOWEL-B respectively
   Loss associated with wrong decision
and No of patterns in
VOWEL-A and VOWEL-B respectively which is 10 for both categories
Algorithm 2: PNN learning algorithm.