  We propose an optimized parameter set for protein secondary structure
prediction using three layer feed forward back propagation neural network. The
methodology uses four parameters viz. encoding scheme, window size, number of
neurons in the hidden layer and type of learning algorithm. The input layer of
the network consists of neurons changing from 3 to 19, corresponding to
different window sizes. The hidden layer chooses a natural number from 1 to 20
as the number of neurons. The output layer consists of three neurons, each
corresponding to known secondary structural classes viz. alpha helix, beta
strands and coils respectively. It also uses eight different learning
algorithms and nine encoding schemes. Exhaustive experiments were performed
using non-homologues dataset. The experimental results were compared using
performance measures like Q3, sensitivity, specificity, Mathew correlation
coefficient and accuracy. The paper also discusses the process of obtaining a
stabilized cluster of 2530 records from a collection of 11340 records. The
graphs of these stabilized clusters of records with respect to accuracy are
concave, convergence is monotonic increasing and rate of convergence is
uniform. The paper gives BLOSUM62 as the encoding scheme, 19 as the window
size, 19 as the number of neurons in the hidden layer and One- Step Secant as
the learning algorithm with the highest accuracy of 78%. These parameter values
are proposed as the optimized parameter set for the three layer feed forward
back propagation neural network for the protein secondary structure predictionv
