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As viruses become more complex, existing antivirus methods are inefficient to detect various forms of viruses, especially new variants and unknown viruses. Inspired by immune system, a hierarchical artificial immune system (AIS) model, which is based on matching in three layers, is proposed to detect a variety of forms of viruses. In the bottom layer, a non-stochastic but guided candidate virus gene...
A virus detection system (VDS) based on artificial immune system (AIS) is proposed in this paper. VDS at first generates the detector set from virus files in the dataset, negative selection and clonal selection are applied to the detector set to eliminate autoimmunity detectors and increase the diversity of the detector set in the non-self space respectively. Two novel hybrid distances called hamming-max...
Inspired by natural immune systems, Artificial Immune System (AIS) is an emerging kind of computational intelligence paradigm. The traditional immune algorithm and Ai-net for clustering still have the problems of training time-consumption and accuracy. In this paper, AIS Algorithm is improved with Quantum Mechanics theory and the Schro??dinger equation to add the idea of the energy level into the...
The aim of this paper is to design an adaptive artificial immune algorithm for solving multi-modal optimization problems effectively and speedily. Based on analyzing the characteristics and disadvantages of CLONALG, an improved immune-based algorithm is proposed, which combines memory cells producing, network suppression and valley searching method. Testing benchmark functions show that it can fast...
This paper presents a parallel artificial immune model termed as tower master-slave model (TMSM) for solving optimising problems. Based on TMSM, the parallel immune memory clonal selection algorithm (PIMCSA) is also proposed. TMSM is a two level coarse-grained parallel artificial immune model with distributed immune response and distributed immune memory. In PIMCSA, vaccines are extracted and migrated...
For the problem of Underdetermined Blind Source Separation (UBSS) under nonstrictly sparse condition, we propose a new algorithm to estimate mixing matrix. Firstly, a clustering prototype of orthogonal complement space is introduced; Secondly, a fuzzy EVD clustering method which combines fuzzy clustering and Eigenvalue Decomposition (EVD) is presented. Based on these two methods, the algorithm proposed...
Neural interpretation is of increasing interest in artificial neural networks and it is potential to reveal the intrinsic mechanism of multivariable systems. This study aims at investigating the efficiency of Bayesian neural networks in neural interpretation. The measures to ensure the stability of the network model are first elaborated and then, two types of Bayesian networks with linear and partly-linear...
Prediction of C3 concentration, the most important parameter in determining the product' s grade and quality control of liquid gas produced in FCCU, was studied. A neural estimator model based on improved dynamic principal component analysis (DPCA) and multiple neural networks (MNN) was proposed to infer the C3 concentration from real process variables. DPCA was carried out to select the most relevant...
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