Fuzzy Hypersphere Neural Network Research Articles

Lung Cancer Classification using Fuzzy Hyperline Segment Neural Network (FHLSNN)

Pattern recognition has become more and more popular as it induces attractive attention coming from wider areas. This paper describes Fuzzy Hyperline Segment Neural Network (FHLSNN) with its learning algorithm. The FHLSNN is a supervised learning neural network classifier that utilizes fuzzy sets as pattern classes. Here each fuzzy set is a union of fuzzy set hyperline segments. The performance of Fuzzy Hyperline Segment Neural Network (FHLSNN) [1] is compared with other fuzzy neural networks like Fuzzy Hypersphere Neural Network (FHSNN) [2], proposed by Kulkarni et. al..

Pattern Classification Using Supervised Hypersphere Neural Network0

Pattern classification is of significant demand in the field of machine learning. Its applications range from a simple problem of speech recognition to a complex and important problem of medical diagnosis. Fuzzy based algorithms have been one of the most important methods which have contributed in solving the pattern classification problem. A customized Fuzzy based Supervised Hypersphere Neural Network (SHNN) is presented for the use of pattern classification. Here, a modernized pattern classification method has been presented by considering the fuzzy hypersphere neural network concept at the back end and using a modified version of the membership function aiming to solve the pattern classification problems and boost the performance of the algorithm. The proposed SHNN model creates supervised hypersphere using measurements obtained from intra-class distance techniques along with individual class pattern choice, over the fuzzy membership function. The previous modified fuzzy approaches presented an inherent drawback of ambiguous assignment of classes, losing the fuzzy nature during the assigning of classes in the testing phase and over fitting of the model during the training phase. The proposed approach solves this problem by adding a non linearity to the output of the membership function to maintain its fuzzy nature. Additionally, a new weighted Euclidean distance equation has been designed to enhance the performance of the algorithm. The performance of the proposed model of SHNN has been examined on four standard datasets namely - Pima, liver, glass and monks-3. The results obtained were superior to the previous proposed approaches. Thus, presenting a new state result of the art on the datasets.

A novel fuzzy convolutional neural network for recognition of handwritten Marathi numerals

Pattern classification is the approach of designing a method to map the inputs to the matching output classes. A novel fuzzy convolutional neural network (FCNN) is proposed in this paper for recognition of handwritten Marathi numerals. FCNN uses fuzzy set hypersphere as a pattern classifier to map inputs to classes represented by the combination of the fuzzy set hypersphere. Given labelled classes, the model designed proved efficient with 100% accuracy on the training set. The two major factors which improve the learning algorithm of FCNN are: 1) extract the dominant features from numeral image patterns using customised convolutional neural network (CCNN); 2) supervised clustering is used to create a new fuzzy hypersphere based on its distance measurement learning rules of fuzzy hypersphere neural network (FHSNN) and pattern classification done by the fuzzy membership function. Performance evaluation of model is done on Marathi numerals large datasets and its performance is found to be superior to traditional convolutional neural network (CNN) model. The obtained results demonstrate the fact that FCNN learning rules can be used as a useful representation for different classification pattern problems.

A novel fuzzy convolutional neural network for recognition of handwritten Marathi numerals

Pattern classification is the approach of designing a method to map the inputs to the matching output classes. A novel fuzzy convolutional neural network (FCNN) is proposed in this paper for recognition of handwritten Marathi numerals. FCNN uses fuzzy set hypersphere as a pattern classifier to map inputs to classes represented by the combination of the fuzzy set hypersphere. Given labelled classes, the model designed proved efficient with 100% accuracy on the training set. The two major factors which improve the learning algorithm of FCNN are: 1) extract the dominant features from numeral image patterns using customised convolutional neural network (CCNN); 2) supervised clustering is used to create a new fuzzy hypersphere based on its distance measurement learning rules of fuzzy hypersphere neural network (FHSNN) and pattern classification done by the fuzzy membership function. Performance evaluation of model is done on Marathi numerals large datasets and its performance is found to be superior to traditional convolutional neural network (CNN) model. The obtained results demonstrate the fact that FCNN learning rules can be used as a useful representation for different classification pattern problems.

Class-Specific Fuzzy Hypersphere Neural Network

A new Fuzzy Hypersphere Neural Network (FHNN) classifier is proposed in the framework of Radial Basis Function Neural Network (RBFNN) for which two class specific fuzzy clustering algorithms are suggested for the creation of fuzzy set hyperspheres (FSHs) in the hidden layer and the traditional least mean square (LMS) algorithm used to determine the weights between hidden to output layer is avoided by assigning the appropriate binary weights during training. The FHNN classifier utilizes FSH as pattern cluster and classes are represented by union of FSHs. The proposed fuzzy clustering algorithms are developed on the basis of inter-class and intra-class fuzzy membership metrics with maximum coverage of data points to create the FSHs in the hidden layer of FHNN. The weights between hidden to output layer of FHNN are adapted simultaneously during the creation of FSHs in the hidden layer. Unlike RBFNN and other similar models, the learning is: fast, independent of tuning parameters, formulates precise centroids and widths or radii of the FSHs, leading to 100% training accuracy on any training set. The learning rule 1 proposed for the FHNN is insusceptible to the order of data presentation and always results in identical clusters showing good data visualization, while the learning rule 2 eradicates the overlap between inter-class FSHs. The performance of FHNN is compared with the RBFNNs using twelve benchmark data sets. The empirical findings demonstrate that the FHNN is highly efficient classifier.

Modified Fuzzy Hypersphere Neural Network for Pattern Classification using Supervised Clustering

Pattern Classification involves formulating a method that maps the input feature variables to output space of binomial class. In this paper a modified fuzzy hypersphere neural network (MFHSNN) is proposed for pattern classification. MFHSNN utilizes fuzzy set hypersphere as a pattern cluster and classes are represented by a combination of fuzzy set hypersphere. The major factors which improve the learning algorithm of MFHSNN are:a new hypersphere created based on using supervised clustering and patterns of each class decided by its modified fuzzy membership function. If the new input pattern is outside of generated hypersphere then the radius is expanded to include the pattern based on expansion criteria. The presented model tested on three benchmark pattern data-sets and its performance is found to be superior than existing models. The obtained results show that the proposed membership function is able to improve MFHSNN for representation of pattern classification.

Pruned Fuzzy Hypersphere Neural Network (PFHSNN) for Lung Cancer Classification

Pruned Fuzzy Hypersphere Neural Network (PFHSNN) for Lung Cancer Classification

Iris Recognition Using Modified Fuzzy Hypersphere Neural Network with different Distance Measures

In this paper we describe Iris recognition using Modified Fuzzy Hypersphere Neural Network (MFHSNN) with its learning algorithm, which is an extension of Fuzzy Hypersphere Neural Network (FHSNN) proposed by Kulkarni et al. We have evaluated performance of MFHSNN classifier using different distance measures. It is observed that Bhattacharyya distance is superior in terms of training and recall time as compared to Euclidean and Manhattan distance measures. The feasibility of the MFHSNN has been successfully appraised on CASIA database with 756 images and found superior in terms of generalization and training time with equivalent recall time.

Liquid-Propellant Rocket Engine Health-Monitoring Techniques

Health monitoring of liquid-propellant rocket engines (LRE) is one of the key technologies for improving the safety of existing engines and developing reliable next-generation engines. Extensive research has been done on the health monitoring of the Space Shuttle Main Engine and next-generation reusable LRE. A brief overview of these research projects is presented. Research advances on the health monitoring of the Long March Main Engine YF-20B are described in detail. The failure mode simulation and analysis of the YF-20B engine are introduced. A component module-based diagnosis method is developed, and a fuzzy hypersphere neural network is demonstrated for the fault detection and isolation of the engine. A real-time verie cation system for the health-monitoring algorithms and system was constructed and applied in the research. I. Introduction B ECAUSE of increasingly stringent requirements for the safety, reliability, and operational capabilities of space vehicles and their launch systems in the past 30 years along with the feasibility provided by the progress of modern science and technology, the health-monitoring techniques for liquid-propellant rocket engines (LRE) have undergone signie cant developments. In earlier stages of development, health-monitoring technology was applied in the ground test process for large-scale LRE. In the 1970s, expendable LREs, such as Atlas and Titan, were monitored by redlines, which are limits or thresholds on some important operating parameters, 1 and automatic test data analysis systems were progressively developed for these engines in early 1990s. 2 The partially reusable rocket engine Space Shuttle Main Engine (SSME), developed in the 1970s, is monitored by a condition-monitoring system that is a simple function of redlines. 3 In the 1980s the SAFD (System for Anomaly and Failure Detection ) was developed for SSME ground tests to improve the monitoring performance of the redline system. 1