Hierarchical Neural Network Classifier Research Articles

Multi-objective particle swarm optimisation for mental task classification using hybrid features and hierarchical neural network classifier

Multi-objective particle swarm optimisation for mental task classification using hybrid features and hierarchical neural network classifier

Multi-objective particle swarm optimisation for mental task classification using hybrid features and hierarchical neural network classifier

Recognition of mental tasks using electroencephalograph (EEG) signals is of prime importance in man machine interface and assistive technologies. Considerably low recognition rate of mental tasks is still an issue. This work combines power spectral density (PSD) features and lazy wavelet transform (LWT) coefficients to present a new approach to feature extraction from EEG signals. A simple but novel neural network classifier called hierarchical neural network is proposed for the task recognition. A novel methodology based on multi objective particle swarm optimisation (MOPSO) to select discriminative features and the number of hidden layer nodes is proposed to improve the classification accuracy. The extracted features are presented to the hierarchical classifier to discriminate left-hand movement, right-hand movement and word generation task. The results are verified on standard brain computer interface (BCI) database and our own B-alert experimental system database. The benchmarking indicates that the proposed work outperforms the state of the art.

Development of the algorithm of adaptive construction of hierarchical neural network classifiers

This paper presents the development of the algorithm for adaptive construction of hierarchical neural network classifiers based on automatic modification of the desired response of a perceptron with a small number of neurons in a single hidden layer. Improved versions of the algorithm are tested on standard benchmark problems Vowels and MNIST. A discussion of the results, strengths and weaknesses of the algorithm, directions of further work on its testing and improvement, is provided.

Analysis of the Earth's magnetosphere states using the algorithm of adaptive construction of hierarchical neural network classifiers

This paper presents analysis of the results of clusterization of the array of increases in the flux of relativistic electrons in the outer radiation belt of the Earth by two clustering algorithms. One of them is the algorithm for adaptive construction of hierarchical neural network classifiers developed by the authors, applied in clustering mode; the other one is the well-known k-means clusterization algorithm. The obtained clusters are analysed from the point of view of their possible matching to characteristic types of events, the partitions obtained by both methods are compared with each other.

A new implementation of the algorithm of adaptive construction of hierarchical neural network classifiers

The article presents development of the algorithm of adaptive construction of hierarchical neural network classifiers based on automatic modification of the desired output of perceptrons with a small number of neurons in the single hidden layer. The conducted testing of the new program implementation of this approach demonstrated that the considered algorithm was more computationally efficient and provided higher quality of solution of multiple classification problems in comparison with standard multi-layer perceptron.

DSP-based hierarchical neural network modulation signal classification

This paper discusses a real-time digital signal processor (DSP)-based hierarchical neural network classifier capable of classifying both analog and digital modulation signals. A high-performance DSP processor, namely the TMS320C6701, is utilized to implement different kinds of classifiers including a hierarchical neural network classifier. A total of 31 statistical signal features are extracted and used to classify 11 modulation signals plus white noise. The modulation signals include CW, AM, FM, SSB, FSK2, FSK4, PSK2, PSK4, OOK, QAM16, and QAM32. A classification hierarchy is introduced and the genetic algorithm is employed to obtain the most effective set of features at each level of the hierarchy. The classification results and the number of operations on the DSP processor indicate the effectiveness of the introduced hierarchical neural network classifier in terms of both classification rate and processing time.

A Self-Organizing Hierarchical Classifier for Multi-Lingual Large-Set Oriental Character Recognition

In this paper, we propose a practical scheme for multi-lingual, multi-font and multi-size large-set Oriental character recognition using a self-organizing hierarchical neural network classifier. In order to absorb the variation of the character shapes in multi-font and multi-size characters, a modified nonlinear shape normalization method based on dot density was introduced, and also to represent the different topological structures of multi-lingual characters effectively, a hierarchical feature extraction method was adopted. For coarse classification, a tree classifier and SOFM/LVQ based classifier which is composed of an adaptive SOFM coarse-classifier and an LVQ4 language-classifier were considered. For fine classification, a classifier based on LVQ4 learning algorithm has been developed. The experimental results revealed that the proposed scheme has the highest recognition rate of 98.27% for testing data with 7,320 kinds of multi-lingual classes and the time performance of more than 40 characters per second on 486DX-2 66MHz PC.