A Target Damage Effectiveness Assessment Mathematical Calculation Method with Uncertain Information Based on an Adaptive Fuzzy Neural Network
Algorithm 1
Pseudocode of Takagi-Sugeno fuzzy neural network of the damage effectiveness of the target.
Start of Algorithm
Inputs: The distance deviation of the warhead fragment group, the density of warhead fragments covering the cabin (), the warhead fragment fire density hitting cabin (), the ratio between the coverage area of warhead fragments and target ()
Output: The damage effectiveness of the target ()
Initialization:
(1) Initialize fuzzy rule base, initialize fuzzy controller parameters, initialize clustering parameters, and initialize the maximum number of fuzzy sets
(2) Cluster the input data set by a subtractive clustering algorithm to find all clustering centers by equations (24) and (25), generate the initial fuzzy rule base and fuzzy controller parameters
Steps:
(1) Convert input variables into fuzzy sets by equation (18)
(2) Calculate the membership of input variables in each fuzzy set by equation (19)
(3) Use Takagi-Sugeno fuzzy reasoning method to obtain the incentive strength of fuzzy rules by equation (20)
(4) Calculate the reasoning results according to the fuzzy rule base and the input after fuzzification by equation (21)
(5) Use the weighted average defuzzification method to convert the fuzzy output into specific output values by (22)
