Printed Japanese character recognition based on multiple modified LVQ neural network

Diabetes Mellitus is a chronic, non-infectious disease that affects people’s health, which has a rapid onset trend with the continuous improvement of China’s economy and material life. At present, there is no practical solution to this condition. Diabetic retinopathy (DR) is the most important manifestation of diabetic microangiopathy, which can be divided into proliferative lesion and non-proliferative lesion. The traditional artificial diagnosis method has strong subjectivity and low accuracy. Because disease diagnosis can be regarded as a two-classification pattern recognition problem, the application of neural network method can provide the possibility for the application of artificial intelligence (AI) in disease-assisted diagnosis and treatment. Furthermore, it has significant meanings to find which kind of neural network has a better efficiency. In this paper, learning vector quantization (LVQ) neural network and back propagation (BP) neural network were used to diagnose diabetes and diabetic retinopathy with MATLAB and their recognition rate were compared. The datasets of diabetes and diabetic retinopathy were available in the UCI database. The experiment results were analyzed to evaluate the efficiency of each neural network classifier. The results demonstrate that both LVQ neural network and BP neural network can classify the two datasets effectively. However, compared with the LVQ neural network, the average accuracy rate and sensitivity of the BP neural network is higher.

Transformer inrush currents are high magnitude, harmonic-rich currents generated when transformer cores are driven into saturation during energization. In this paper an efficient method for detection of inrush current in distribution transformer based on wavelet transform is presented. Using this method inrush current can be discriminate from other transients such as capacitor switching, load switching and single phase to ground fault. Wavelet transform is used for decomposition of signals and Learning Vector Quantizer(LVQ) neural network used for classification. Inrush current data and other transients are obtained by simulation using EMTP program. Results show that the proposed procedure is efficient in identifying inrush current from other events.

Standard back propagation (BP) neural network has disadvantages such as slow convergence speed, local minimum and difficulty in definition of network structure. In this paper, an learning vector quantization (LVQ) neural network classifier is established, then it is applied in pattern classification of two-dimensional vectors on a plane. To test its classification ability, the classification results of LVQ neural network and BP neural network are compared with each other. The simulation result shows that compared with classification method based on BP neural network, the one based on LVQ neural network has a shorter learning time. Besides, its requirements for learning samples and the number of competing layers are also lower. Therefore it is an effective classification method which is powerful in classification of two-dimensional vectors on a plane.

Tractor condition recognition has important research value in helping to understand the operating status of tractors and the trend of tillage depth changes in the field. Therefore, this article presents a method for recognizing tractor conditions, providing the basis for establishing the relationship between tractor conditions and the tillage depth of the attached agricultural machinery. This study designed a tractor condition recognition method based on neural networks. Using real-world vehicle data to establish a data set, K-means clustering analysis was used to label the data set based on four conditions: “accelerated start”, “constant speed”, “decelerated stop” and “turning”. The learning vector quantization (LVQ) neural network and the VGG-16 model of a CNN were selected for use recognizing the tractor conditions. The results showed that both the neural networks had good recognition effects. The average accuracy rates of the VGG-16 model of CNN and LVQ neural network were 90.25% and 79.7%, respectively, indicating that these models could be applied to tractor condition recognition and provide theoretical support for the correction of angle detection errors.

A learning vector quantization(LVQ) neural network classifier is for the first time applied for SAR data classification, of which the training termination rule is modified to make it have both the ability of classification and class signature analysis. A very high land cover classification accuracy is achieved. Especially under the condition that texture is considered, almost all roads can be classified correctly, which cannot be identified by BP MLP neural network and ML classifier.

The Raman spectra of tissue of 20 brain tumor patients was recorded using a confocal microlaser Raman spectroscope with 785 nm excitation in vitro. A total of 133 spectra were investigated. Spectra peaks from normal white matter tissue and tumor tissue were analyzed. Algorithms, such as principal component analysis, linear discriminant analysis, and the support vector machine, are commonly used to analyze spectral data. However, in this study, we employed the learning vector quantization (LVQ) neural network, which is typically used for pattern recognition. By applying the proposed method, a normal diagnosis accuracy of 85.7% and a glioma diagnosis accuracy of 89.5% were achieved. The LVQ neural network is a recent approach to excavating Raman spectra information. Moreover, it is fast and convenient, does not require the spectra peak counterpart, and achieves a relatively high accuracy. It can be used in brain tumor prognostics and in helping to optimize the cutting margins of gliomas.

It is an important part in aluminum electrolysis production to control the anode effect (AE). Since there are some shortcomings in traditional methods of anode effect prediction in aluminum electrolysis, this paper combined two methods, the slope of cell resistance and learning vector quantization (LVQ) neural network, to predict anode effect. First of all, the first prediction of anode effect will be conducted based on the slope of cell resistance. Afterwards, the inaccurate data are supposed to be re-predicted. The second prediction consists of two steps, one is to estimate the power spectrum from the signal of cell resistance by means of periodogram, the other is to re-predict the anode effect with the LVQ neural network, since the energy of frequency bands are served as the input feature variables of neural network, so as to raise the accuracy of prediction. It turned out that the success rate of ten-minute in advance prediction for anode effect can be above 85%, though just cell resistance signal is studied.

The design of pattern classifiers such as multiprototype classifiers and neural network classifiers such as learning vector quantization and radial basis function neural networks requires reducing the size of the training data sets. In addition, memory storage, computation complexity and time, and data redundancy demand many pattern classifiers to use a smaller subset of a training data set. In this paper, we present a data reduction algorithm which automatically selects the subset of training data that faithfully represents the training data set for pattern classification. The applicability of this algorithm is demonstrated through k-nearest neighbor and learning vector quantization neural networks classifiers using both speech and synthetic data sets.

— Neural networks are frequently used as a classifier for tasks in many classifications. However there are disadvantages in terms of amount of training data required, and length of training time. This paper, develop an intelligent diagnosis system for zinc oxide (ZnO) surge arrester fault classification. First the features were extracted from 600 ZnO surge arrester thermal images and leakage currents. Then these features were presented to several neural network architectures to investigate the most suitable network model for classifying the ZnO surge arrester fault condition effectively. Three classification models were used namely feed forward back propagation (FFBP), radial basis function (RBF) and learning vector quantization (LVQ) algorithm. The performance of the networks was compared based on resulted of misclassify and correct rate. The method was evaluated using 24 testing datasets. Comparison results showed that LVQ was the best training algorithm for the ZnO surge arrester fault classification compared to the others system. Also the LVQ is faster than FFBP and RBF.

At present, study on the non-asbestos gasket materials is the hotspot research in static sealing field. The non-asbestos sealing gaskets research and development has made great strides into the practical phase. Formula is an important factor of material, which determines performance of material. In order to obtain well performance, it is needed to optimization formula to get optimal formula that not only improve performance of non-asbestos gasket, but also reduce development time accordingly reduce cost of non-asbestos gasket. Classification of raw materials can be transformed into a mathematical clustering problem. It means that according to some algorithm, there will be some sort of input values of similar links together. Many neural networks were widely used in the classification of different materials. A method of classification by using neural network to the known 15 kinds of the non-asbestos gaskets of formula data was proposed in this paper. By using the PNN (probabilistic neural network), LVQ(Learning Vector Quantization) neural network and SOM (Self-Organizing Feature Map) neural network respectively to classify the non-asbestos gaskets to find a suitable method in the classification of non-asbestos gaskets formula. The results indicated that PNN neural network and LVQ neural network method based on the data that provided in the paper both can effectively classify, while SOM neural network can not classify them ideally, thus it provides a new theoretical basis for the classification of the non-asbestos gaskets.

This paper proposes a framework for developing a broad variety of soft clustering and learning vector quantization (LVQ) algorithms based on gradient descent minimization of a reformulation function. According to the proposed axiomatic approach to learning vector quantization, the development of specific algorithms reduces to the selection of a generator function. A linear generator function leads to the fuzzy c-means (FCM) and fuzzy LVQ (FLVQ) algorithms while an exponential generator function leads to entropy constrained fuzzy clustering (ECFC) and entropy constrained LVQ (ECLVQ) algorithms. The reformulation of clustering and LVQ algorithms is also extended to supervised learning models through an axiomatic approach proposed for reformulating radial basis function (RBF) neural networks. This approach results in a broad variety of admissible RBF models, while the form of the radial basis functions is determined by a generator function. This paper shows that gradient descent learning makes reformulated RBF neural networks an attractive alternative to conventional feed-forward neural networks.

The learning vector quantization(LVQ) is a model of neural networks, and it is used for complex pattern classifications in which typical feedforward networks don’t give a good performance. Fault tolerance is an important feature in the neural networks, when they are used for critical application. Many methods for enhancing the fault tolerance of neural networks have been proposed, but most of them are for feedforward networks. There is scarcely any methods for fault tolerance of LVQ neural networks. In this paper, I proposed a dependability measure for the LVQ neural networks, and then I presented two idea, the border emphasis and the encouragement of coupling, to improve the learning algorithm for increasing dependability. The experiment result shows that the proposed algorithm trains networks so that they can achieve high dependability.

Nowadays, air pollution is one of the major threats to the ecosystem and human existence. Addressing air pollution is the biggest environmental challenge in smart city environments. Therefore, monitoring of air quality has become an important task in urban and industrial areas. The primary focus to monitor air quality is a human-centred approach, as data collected from different monitoring stations are evaluated based on human experience and unreliable. Therefore, the emergence of machine learning technologies such as decision trees, neural networks (NN), and support vector machine (SVM) justifies the application of statistical methods to environmental modelling, especially in the forecasting of air quality. In addition, the existing techniques are insufficient to find the impact of every air pollutants for minimising air pollutions. An Artificial Neural Network (ANN) with Learning Vector Quantisation (LVQ) is implemented for prediction in this research study. The results showed that the ANN-LVQ achieved 97.58% of accuracy with a minimal error rate.

This paper proposes a novel fingerprint classification method. It uses an SPCNN (Simplified Pulse Coupled Neural Network) to estimate directional image of fingerprint, and quantizes them to obtain fingerprint vector. Then, a fully trained LVQ (Learning Vector Quantization) neural network is used as classifier for the fingerprint vector to determine the corresponding fingerprint classification. Experiments show this proposed method is robust and has high classification accuracy.

Static Cytometry and Neural Networks in the Discrimination of Lower Urinary System Lesions