Artificial Immune Network Model Research Articles

Improved fault diagnosis algorithm based on artificial immune network model and neighbourhood rough set theory

With the aim to identify new fault diagnosis and advanced robotic systems, this paper first proposes a fault diagnosis algorithm based on an artificial immune network model that can adjust the pruning threshold. Secondly, the algorithm is improved based on neighbourhood rough set theory, in which the relationships among the pruning threshold, misdiagnosis rate, and missed diagnosis rate in the shape space are discussed. In addition, an improved algorithm for adjusting the adaptively pruning threshold based solely on an observation index is described. The simulation experiments show that the algorithm can identify the new fault modes while keeping the misdiagnosis and missed diagnosis rates low.

INDUCING FUZZY REGRESSION TREE FORESTS USING ARTIFICIAL IMMUNE SYSTEMS

Fuzzy decision forests aim to improve the predictive power of single fuzzy decision trees by allowing multiple views of the same domain to be modelled. Such forests have been successfully created for classification problems where the outcome field is discrete; however predicting a continuous output value is more challenging in combining the output from multiple fuzzy decision trees. This paper presents a new approach to creating fuzzy regression tree forests based upon the induction of multiple fuzzy regression decision trees from one training sample, where each tree will represent a different view of the data domain. The singular fuzzy regression trees are induced using a proven algorithm known as Elgasir which fuzzifies crisp CHAID decision trees using trapezoidal membership functions for fuzzification and applies Takagi-Sugeno inference to obtain the final predicted values. A modified version of Artificial Immune System Network model (opt-aiNet) is then used for the simultaneous optimization of the membership functions across all trees within the forest. A strength of the proposed method is that data does not require fuzzification before forest induction this reducing pre-processing time and the need for subjective human experts. Five problem sets from the UCI repository and KEEL repository are used to evaluate the approach. The experimental results have shown that fuzzy regression tree forests reduce the error rate compared with single fuzzy regression trees.

Unsupervised remote sensing image classification using an artificial immune network

In this article, the artificial immune network (aiNet) model, a computational intelligent approach based on artificial immune networks (AINs), is applied to remote sensing image processing to improve its intelligence. aiNet has been utilized for clustering, optimization, and data analysis. Nevertheless, due to the inherent complexity of the aiNet algorithm and the large volume of data in remote sensing imagery, the application of aiNet to remote sensing image classification has been rather limited. This article presents an unsupervised artificial immune network for remote sensing image classification (RSUAIN) based on aiNet. The proposed method can adaptively obtain some user-defined parameters, such as clone rate and mutation rate, and evolve the memorial immune network by immune operators and biological properties, such as clone, mutation and memory operators, using the remote sensing image for the task of remote sensing image clustering. Three experiments with different types of images were performed to evaluate the performance of the proposed algorithm and to compare it with other traditional unsupervised classification algorithms, for example, k-means, ISODATA (Iterative Self-organizing Data Anaysis Techniques Algorithm) and fuzzy k-means. RSUAIN was observed to outperform the traditional algorithms in the three experiments and hence potentially provides an effective option for unsupervised remote sensing image classification.

Dynamics of an artificial immune network1

Artificial immune systems (AIS) are adaptive systems developed by taking inspiration from theoretical and experimental immunology and targeted at solving complex problems. Theoretical models are basically aimed at aiding and providing a more qualitative and quantitative description of the biological system they model, sometimes allowing the prediction and design of experiments, and the recovery of information from experimental set-ups. This paper describes the dynamics of one artificial immune network model based on artificial immune systems, named aiNet, and demonstrates through empirical results that even these highly simplified models may present several qualitative behaviours typical of the biological systems and theoretical models; thus being worthy of attention by the more theoretical community. In particular, clonal selection and expansion, affinity maturation, primary, secondary and cross-reactive responses are studied, and demonstrated to be observable in aiNet. Immune network features, such as self-organization, dynamics, connectivity and metadynamics are also discussed.

Blind Search for Optimal Wiener Equalizers Using an Artificial Immune Network Model

This work proposes a framework to determine the optimal Wiener equalizer by using an artificial immune network model together with the constant modulus (CM) cost function. This study was primarily motivated by recent theoretical results concerning the CM criterion and its relation to the Wiener approach. The proposed immune-based technique was tested under different channel models and filter orders, and benchmarked against a procedure using a genetic algorithm with niching. The results demonstrated that the proposed strategy has a clear superiority when compared with the more traditional technique. The proposed algorithm presents interesting features from the perspective of multimodal search, being capable of determining the optimal Wiener equalizer in most runs for all tested channels.

IMMUNE AND NEURAL NETWORK MODELS: THEORETICAL AND EMPIRICAL COMPARISONS

This paper brings a detailed mathematical description of an artificial immune network model, named aiNet. The model is implemented in association with graph concepts and hierarchical clustering techniques, and is proposed to perform machine learning, data compression and cluster analysis. Pictorial representations for the aiNet basic units and typical architectures are introduced. The proposed immune network was primarily compared on a theoretical basis with well-known artificial neural networks. Then, the aiNet was applied to a non-linearly separable benchmark and a real-world problem, and the results were compared with that of the self-organizing feature map and with others already presented in the literature.