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It proposes a target classification and recognition method based on immune genetic neural network algorithm. Immune genetic algorithm is adopted to optimize the initial weights and thresholds of BP (Back Propagation) network. Respectively use traditional BP neural network algorithm and immune genetic neural network algorithm to train network, until the convergence error precision of neural network reaches a pre-set requirement. Simulation results show that with the same hidden layer nodes and error precision request, it proposes immune genetic neural network algorithm for image recognition, its effect is better than that of traditional BP algorithm, improves the convergence speed of BP neural network, and reduces the training time. The recognition rate improves to some extent. Immune Genetic Algorithm Genetic algorithm is a stochastic search algorithm, groups search and information exchanges between individuals are achieved through crossover and mutation operators [1]. It ensures the entire population evolutionary direction under the "survival of the fittest" selection mechanism. But crossover and mutation operators are carrying out the random iterative search under certain probability conditions. In the evolution of each individual, but also it may produce degradation. Immunization is a phenomenon of organisms autonomously against viruses to maintain their physical activity stable and equilibrium. When the immune system produces "initial immune response" with a specific antigen, part of the antibodies become memory cells, and remember the antigen [2]. When they meet again, it will produce "twice immune response” and clear the antigen quickly. Using the remembered antigen recognition function in the genetic algorithm, it can speed up the search. Immune genetic algorithm combines immune algorithm with genetic algorithm, make the objective function of practical problem correspond to an antigen, problem solution correspond to the antibody; use antigen and antibody affinity to represent the approximation degree of feasible solution and optimal solution [3]. For a particular problem, the algorithm randomly generates a set of initial antibody, and then calculates the fitness of each antibody, achieve the environmental adaptation by promotion and restrain between antibodies. BP Neural Network BP neural network is also known as back propagation neural network. It is divided into the input layer, intermediate layer (hidden layer) and output layer [4]. There is no connection between each layer of neurons; the input layer and output layer are 628 Advances in Computer Science Research (ACRS), volume 54 International Conference on Computer Networks and Communication Technology (CNCT2016)

Using Immune Genetic Algorithm to Optimize BP Neural Network and Its Application