基于人工免疫网络模型的航空发动机传感器故障诊断

提出了一种用于传感器故障诊断的免疫网络,对其结构和特点进行了分析,给出了相应的诊断算法。对传感器典型故障进行了故障诊断仿真,分析了免疫网络能检测出的最小故障偏差水平以及在不同噪声水平下的故障诊断效果。仿真结果表明,所研究的方法能有效检测到故障传感器,并具有良好的灵敏性及抗噪声干扰能力。

An optimization algorithm for locomotive secondary spring load adjustment based on artificial immune

In order to control the locomotive wheel(axle) load distribution, a shimming process to adjust the locomotive secondary spring loads was heretofore developed. An immune dominance clonal selection multi-objective algorithm based on the artificial immune system was presented to further improve the performance of the optimization algorithm for locomotive secondary spring load adjustment, especially to solve the lack of control on the output shim quantity. The algorithm was designed into a two-level optimization structure according to the preferences of the problem, and the priori knowledge of the problem was used as the immune dominance. Experiments on various types of locomotives show that owing to the novel algorithm, the shim quantity is cut down by 30% 60% and the calculation time is about 90% less while the secondary spring load distribution is controlled on the same level as before. The application of this optimization algorithm can significantly improve the availability and efficiency of the secondary spring adjustment process.

A Fuzzy-based Adaptive Genetic Algorithm and Its Case Study in Chemical Engineering

Considering that the performance of a genetic algorithm （GA） is affected by many factors and their rela-tionships are complex and hard to be described,a novel fuzzy-based adaptive genetic algorithm （FAGA） combined a new artificial immune system with fuzzy system theory is proposed due to the fact fuzzy theory can describe high complex problems.In FAGA,immune theory is used to improve the performance of selection operation.And,crossover probability and mutation probability are adjusted dynamically by fuzzy inferences,which are developed according to the heuristic fuzzy relationship between algorithm performances and control parameters.The experi-ments show that FAGA can efficiently overcome shortcomings of GA,i.e.,premature and slow,and obtain better results than two typical fuzzy GAs.Finally,FAGA was used for the parameters estimation of reaction kinetics model and the satisfactory result was obtained.

Smartphone Malware Detection Model Based on Artificial Immune System

In order to solve the problem that me traditional signature-based detection technology cannot effectively detect unknown malware, we propose in this study a smartphone malware detection model （SP-MDM） based on artificial immune system, in which static malware analysis and dynamic malware analysis techniques are combined, and antigens are generated by encoding the characteristics extracted from the malware. Based on negative selection algorithm, the mature detectors are generated. By introducing clonal selection algorithm, the detectors with higher affinity are selected to undergo a proliferation and somatic hyper-mutation process, so that more excellent detector offspring can be generated. Experimental result shows that the detection model has a higher detection rate for unknown smartphone malware, and better detection performance can be achieved by increasing the clone generation.

Dendritic Cells Algorithm and Its Application to Nmap Portscan Detection

Dendritic Cells Algorithm （DCA） is a new development in Artificial Immune System （AIS）. It has various parameters, and as yet has not been ex- tensively tested. The general applicability of the al- gorithm to a variety of problems is d. The aim of this work is to demonstrate the feas^ility and ro- bustness of the algorithm, and the sensitivity to the change of various parameters in a series of experi- ments for Nmap portscan detection by using DCA. Experiment results show that the algorithm per- forms well on the task of detecting a ping based Nmap portscan. Sensitivity analysis is also per- formed. True positive rate is higher for the detec- tion of anomaly processes and false positive rate is lower for the detection of normal orocesses.

An immune-swarm intelligence based algorithm for deterministic coverage problems of wireless sensor networks

A novel immune-swarm intelligence (ISI) based algorithm for solving the deterministic coverage problems of wireless sensor networks was presented.It makes full use of information sharing and retains diversity from the principle of particle swarm optimization (PSO) and artificial immune system (AIS).The algorithm was analyzed in detail and proper swarm size,evolving generations,gene-exchange individual order,and gene-exchange proportion in molecule were obtained for better algorithm performances.According to the test results,the appropriate parameters are about 50 swarm individuals,over 3 000 evolving generations,20%-25% gene-exchange proportion in molecule with gene-exchange taking place between better fitness affinity individuals.The algorithm is practical and effective in maximizing the coverage probability with given number of sensors and minimizing sensor numbers with required coverage probability in sensor placement.It can reach a better result quickly,especially with the proper calculation parameters.

Feature selection for chemical process fault diagnosis by artificial immune systems

With the Industry 4.0 era coming, modern chemical plants will be gradually transformed into smart factories, which sets higher requirements for fault detection and diagnosis(FDD) to enhance operation safety intelligence. In a typical chemical process, there are hundreds of process variables. Feature selection is a key to the efficiency and effectiveness of FDD. Even though artificial immune system has advantages in adaptation and independency on a large number of fault samples, antibody library construction used to be based on experience. It is not only time consuming, but also lack of scientific foundation in fault feature selection, which may deteriorate the FDD performance of the AIS. In this paper, a fault antibody feature selection optimization(FAFSO) algorithm is proposed based on genetic algorithm to optimize the fault antibody features and the antibody libraries' thresholds simultaneously. The performance of the proposed FAFSO algorithms is illustrated through the Tennessee Eastman benchmark problem.

Immune modelling and programming of a mobile robot demo

An artificial immune system was modelled with self/non-self selection to overcome abnormity in a mobile robot demo. The immune modelling includes the innate immune modelling and the adaptive immune modelling. The self/non-self selection includes detection and recognition, and the self/non-self detection is based on the normal model of the demo. After the detection, the non-self recognition is based on learning unknown non-self for the adaptive immunization. The learning was designed on the neural network or on the learning mechanism from examples. The last step is elimination of all the non-self and failover of the demo. The immunization of the mobile robot demo is programmed with Java to test effectiveness of the approach. Some worms infected the mobile robot demo, and caused the abnormity. The results of the immunization simulations show that the immune program can detect 100% worms, recognize all known Worms and most unknown worms, and eliminate the worms. Moreover, the damaged files of the mobile robot demo can all be repaired through the normal model and immunization. Therefore, the immune modelling of the mobile robot demo is effective and programmable in some anti-worms and abnormity detection applications.

Research on a randomized real-valued negative selection algorithm

A real-valued negative selection algorithm with good mathematical foundation is presented to solve some of the drawbacks of previous approach. Specifically, it can produce a good estimate of the optimal number of detectors needed to cover the non-self space, and the maximization of the non-self coverage is done through an optimization algorithm with proven convergence properties. Experiments are performed to validate the assumptions made while designing the algorithm and to evaluate its performance.

A Control Algorithm Derived from Immune Principle

Artificial immune systems (AIS) are biologically inspired problem solvers having been successfully applied in many fields. A controller was designed according to the interactive mechanism between immune molecules. Multiform antigens were constructed and corresponding antibodies designed. The concept of “antibiotic” is presented, whose features and injection time as well as construction method discussed. Based on biological immune mechanism, some fuzzy rules are summarized and used in constructing the controller. The result shows that this controller is simple in structure and can be easily computed, so it is suitable for real time control. The control variable can change adaptively according to the error and its change tendency. Therefore the controller is very flexible and can be directly used in controlling some nonlinear plants. To test the validity of the algorithm, two simulation examples are given, one is linear, and the other is nonlinear. The Simulation results indicate that the control performance of this algorithm is better than that of the conventional PID.

A fuzzy logic resource allocation and memory cell pruning based artificial immune recognition system

In order to improve the resource allocation mechanism of artificial immune recognition system(AIRS) and decrease the memory cells,a fuzzy logic resource allocation and memory cell pruning based AIRS(FPAIRS) is proposed.In FPAIRS,the fuzzy logic is determined by a parameter,thus,the optimal fuzzy logics for different problems can be located through changing the parameter value.At the same time,the memory cells of low fitness scores are pruned to improve the classifier.This classifier was compared with other classifiers on six UCI datasets classification performance.The results show that the accuracies reached by FPAIRS are higher than or comparable to the accuracies of other classifiers,and the memory cells decrease when compared with the memory cells of AIRS.The results show that the algorithm is a high-performance classifier.