Artificial immune algorithm based on RBF neural network and adaptive search

Abstract

The invention provides an artificial immune algorithm based on an RBF neural network and adaptive search. The artificial immune algorithm comprises the following steps: S1, performing antigen recognition, and constructing the RBF neural network; S2, constructing an antibody-antigen nonlinear mapping curved surface; S3, randomly generating a certain number of initial antibody groups; S4, calculating an antibody-antigen structural body, and preferably selecting N antibodies to serve as antibodies to be evaluated; S5, evaluating the antibodies; S6, sorting the antibody groups, extracting the previous nA antibody groups to serve as memory cells to form a population A, and extracting subsequent nB antibody groups to serve as populations B to be inoculated; S7, judging a termination condition, outputting a result if the termination condition is satisfied, or otherwise, executing S8; and S8, performing selection, crossover and mutation operations on the antibody groups excluding the population A in the S6 to form a population C, after vaccination is performed on the populations B to be inoculated, forming an antibody population D via the populations B together with the populations A and C, and skipping to S4. The invention aims at providing the artificial immune algorithm based on the RBF neural network and adaptive search, which is high in local search capability, high in convergencespeed, high in algorithm efficiency and high in precision.

Description

technical field [0001] The invention relates to an artificial immune algorithm based on RBF neural network and self-adaptive search. Background technique [0002] With the diversification of support tasks and the increasingly high requirements for efficient use of energy, it is of great significance to effectively improve energy utilization efficiency and reduce system energy consumption to improve the continuous and efficient work of rescue vehicles in the field environment. In recent years, hydraulic energy-saving technologies such as hydraulic hybrid technology and energy recovery and reuse technology have played an active role in reducing energy consumption and improving efficiency, because they not only effectively reduce the throttling loss of the system, but also reduce the braking effect of the system. Potentially recoverable energy such as energy and gravitational potential energy can be recovered and reused. However, since these new systems are still a complex and...

AI Technical Summary

Problems solved by technology

Although the immune algorithm has excellent global search ability, it has a significant gap in convergence speed compared with population evolution algorithm, and its local search ability is relatively weak

When the artificial immune algorithm is used to solve the global optimization problem, the expression form of the solution will also have a great impact on the running speed of the algorithm. If the expression form is a function relationship or a simple simulation model, it can be directly applied to the artificial immune algorithm. In the algorithm, because the antibody can quickly construct the antibody-antibody solution structure through this expression form, if the expression form of the solution is a complex simulation model, a large number of antibody-antibody solution structures are required to obtain a group of antibody antibody-antibody solution structures time, which will greatly slow down the convergence speed of the algorithm

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