An Artificial Immune Inspired Hybrid Classification Algorithm and its Application to Fault Diagnosis

2011 ◽  
Vol 411 ◽  
pp. 626-629 ◽  
Author(s):  
Gang Li ◽  
Ming Yang ◽  
Jian Zhuang
Keyword(s):  
Information Entropy ◽  
Clonal Selection ◽  
Classification Algorithm ◽  
Classification Model ◽  
Superior Performance ◽  
Memory Cells ◽  
Artificial Immune ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Hybrid Classification

To efficiently mining the classification model, an artificial immune inspired hybrid classification algorithm was put forward by means of combining antibody clonal selection, fuzzy C means clustering (FCM) and information entropy principle. In this algorithm, fuzzy C means clustering algorithm was employed to generate initial antibody population for making use of the prior knowledge of the training data. From the viewpoint of information entropy, for evolving memory cells the information entropy of antibodies population was employed to provide stop criteria of training. Finally classification was performed in a nearest neighbor approach. Experimental results on the fault detection of DAMADICS demonstrate the effectiveness of the algorithm. Compared with CLONALG artificial immune classifiers, the hybrid classifier has a superior performance in terms of recognition rate, computation time, number of memory cells and condense rate.

A Combinatorial Classifier for Error-Data in Joining Processes with Diverse-Granular Computing

2012 ◽  
Vol 548 ◽  
pp. 740-743
Author(s):  
Yi Lan Chen ◽  
Huan Bao Wang
Keyword(s):  
Fuzzy Clustering ◽  
Granular Computing ◽  
Classification Performance ◽  
Classification Model ◽  
Fuzzy Partition ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Hybrid Classification ◽  
Scale Granularity ◽  
Better Than

In this paper, we present a novel hybrid classification model with fuzzy clustering and design a newly combinatorial classifier for error-data in joining processes with diverse-granular computing, which is an ensemble of a naïve Bayes classifier with fuzzy c-means clustering. And we apply it to improve classification performance of traditional hard classifiers in more complex real-world situations. The fuzzy c-means clustering is applied to a fuzzy partition based on a given propositional function to augment the combinatorial classifier. This strategy would work better than a conventional hard classifier without fuzzy clustering. Proper scale granularity of objects contributes to higher classification performance of the combinatorial classifier. Our experimental results show the newly combinatorial classifier has improved the accuracy and stability of classification.

Research on Transformer Fault Diagnosis Method Based on Artificial Immune Network and Fuzzy C-Means Clustering Algorithm

2014 ◽  
Vol 574 ◽  
pp. 468-473 ◽  
Author(s):  
Fu Zhong Wang ◽  
Shu Min Shao ◽  
Peng Fei Dong
Keyword(s):  
Fault Diagnosis ◽  
Clustering Algorithm ◽  
Power Transformer ◽  
Immune Network ◽  
Artificial Immune ◽  
Fuzzy C Means ◽  
Artificial Immune Network ◽  
Fuzzy C Means Clustering ◽  
Diagnosis Method ◽  
Transformer Fault

The transformer is one of the indispensable equipment in transformer substation, it is of great significance for fault diagnosis. In order to accurately judge the transformer fault types, an algorithm is proposed based on artificial immune network combined with fuzzy c-means clustering to study on transformer fault samples. Focus on the introduction of data processing of transformer faults based on artificial immune network, the identification of transformer faults based on fuzzy c-means clustering, and the simulation process. The experimental results show that the proposed algorithm can classify power transformer fault types effectively, and the algorithm has a good application prospect in the transformer fault diagnosis.

scholarly journals 3D model retrieval using MeshSIFT descriptor and fuzzy C-means clustering

2020 ◽  
Vol 19 (3) ◽  
pp. 1452
Author(s):  
Najlaa Abd Hamza ◽  
Shatha Habeeb Jafer ◽  
Raghad Mohammed Hadi
Keyword(s):  
Three Dimensional ◽  
3D Models ◽  
Superior Performance ◽  
Huge Number ◽  
Fuzzy C Means ◽  
Model Retrieval ◽  
Fuzzy C Means Clustering ◽  
The Subject ◽  
Dimensional Models ◽  
Three Dimensional Models

A huge number of three-dimensional models exists on the internet, due to the fact that there are now more three-dimensional modelling and digitizing tools available for ever-increasing applications. The procedures for retrieval of three-dimensional models have thus become even more essential. The subject of this paper is a shape retrieval of 3D models that are signified as triangle meshes. We propose a new method which first computes the descriptor of 3D models through extracting its features, and then divides a model into clusters depending on a descriptor which is invariant to scale and orientation. A Fuzzy C-means clustering method is utilized for dividing the model into clusters. The superior performance and benefits of our method are shown in the results.

Artificial Immune Optimization Algorithm

2015 ◽  
pp. 104-123
Author(s):  
Waseem Ahmad
Keyword(s):  
Immune System ◽  
Clonal Selection ◽  
Affinity Maturation ◽  
Local Optimum ◽  
Memory Cells ◽  
Artificial Immune ◽  
Immune Optimization ◽  
Specific Memory ◽  
Immune Optimization Algorithm ◽  
System Components

Artificial immune system (AIS) is a paradigm inspired by processes and metaphors of natural immune system (NIS). There is a rapidly growing interest in AIS approaches to machine learning and especially in the domain of optimization. Of particular interest is the way human body responds to diseases and pathogens as well as adapts to remain immune for long periods after a disease has been combated. In this chapter, we are presenting a novel multilayered natural immune system (NIS) inspired algorithms in the domain of optimization. The proposed algorithm uses natural immune system components such as B-cells, Memory cells and Antibodies; and processes such as negative clonal selection and affinity maturation to find multiple local optimum points. Another benefit this algorithm presents is the presence of immunological memory that is in the form of specific memory cells which keep track of previously explored solutions. The algorithm is evaluated on two well-known numeric functions to demonstrate the applicability.

scholarly journals Segmentation of Brain Tissues from MRI using Bilateral Filter Based Fuzzy C-Means Clustering

2019 ◽  
Vol 8 (9S) ◽  
pp. 249-255
Keyword(s):  
Spatial Information ◽  
Bilateral Filter ◽  
Superior Performance ◽  
Segmentation Method ◽  
Mr Images ◽  
Fuzzy C Means ◽  
Intensity Information ◽  
Fuzzy C Means Clustering ◽  
The Impact ◽  
Local Spatial Information

This paper represents a segmentation method that incorporates both local spatial information and intensity information in an efficient fuzzy way. The newly introduced segmentation method BWFCM is an abbreviation of Bilateral weighted fuzzy C-Means. BWFCM uses the advantage of the bilateral filter in its objective function as a bilateral kernel that replaced the spatial neighborhood term with Gaussian weighted Euclidean distance mean of the intensity value of neighbor pixels. BWFCM preserves the damping extent of adjacent pixels while removing the noise because of its averaging behavior. The BWFCM segmentation method is perceived to be very focused on several state-of-the-art methods on a range of images.Experiment analysis on simulated and real MR images show that the proposed method BWFCM provides superior performance over the conventional FCM method and several FCM based methods. The proposed method BWFCM has weakened the impact of Rician noise and other artifact and gives more accurate and efficient segmentation results.

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