Neural Networks - Algorithms, Applications,and ... - Csbdu.in

2.5 Simulating the Adaline S3Adaline while it is running. Based on these observations, there is no needto store or accumulate errors across all patterns within the training algorithm.Thus, we can design the training algorithm merely to adapt the weights for asingle pattern. However, this design decision places on the application programthe responsibility for determining when the Adaline has trained sufficiently.This approach is usually acceptable because of the advantages it offers overthe implementation of a self-contained training loop. Specifically, it means thatwe can use the same training function to adapt the Adaline initially or whileit is on-line. The generality of the algorithm is a particularly useful feature,in that the application program merely needs to detect a condition requiringadaptation. It can then sample the input that caused the error and generate thecorrect response "on the fly," provided we have some way of knowing thatthe error is increasing and can generate the correct desired values to accommodateretraining. These values, in turn, can then be input to the Adalinetraining algorithm, thus allowing adaptation at run time. Finally, it also reducesthe housekeeping chores that must be performed by the simulator, sincewe will not need to maintain a list of expected outputs for all training patterns.We must now define algorithms to compute the squared error term (£ 2 (t)),the approximation of the gradient of the error surface, and to update the connectionweights to the Adaline. We can again simplify matters by combiningthe computation of the error and the update of the connection weightsinto one function, as there is no need to compute the former withoutperforming the latter. We now present the algorithms to accomplish thesefunctions:function compute_error (A : Adaline; TARGET : float)return floatvar tempi : float; {scratch memory}temp2 : float; {scratch memory}err : float;{error term for unit}begintempi = sum_inputs (A.input.outs, A.output.weights);temp2 = compute_output (tempi, A.output~.activation) ;err = absolute (TARGET - temp2); {fast error}return (err);{return error}end function;function update_weights (A : Adaline; ERR : float)return voidvar grad : float; {the gradient of the error}ins : "float[]; {pointer to inputs array}wts : "float[]; {pointer to weights array}i : integer; {iteration counter}