SETTING UP A PROBABILISTIC NEURAL NETWORK FOR CLASSIFICATION OF HIGHWAY VEHICLES

2005 ◽  
Vol 05 (04) ◽  
pp. 411-423 ◽  
Author(s):  
MAJURA F. SELEKWA ◽  
VALERIAN KWIGIZILE ◽  
RENATUS N. MUSSA
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Tree ◽  
Probabilistic Neural Network ◽  
Classification Problem ◽  
Misclassification Rate ◽  
Tree Methods ◽  
Decision Tree Methods

Many neural network methods used for efficient classification of populations work only when the population is globally separable. In situ classification of highway vehicles is one of the problems with globally nonseparable populations. This paper presents a systematic procedure for setting up a probabilistic neural network that can classify the globally nonseparable population of highway vehicles. The method is based on a simple concept that any set of classifiable data can be broken down to subclasses of locally separable data. Hence, if these locally separable data can be identified, then the classification problem can be carried out in two hierarchical steps; step one classifies the data according to the local subclasses, and step two classifies the local subclasses into the global classes. The proposed approach was tested on the problem of classifying highway vehicles according to the US Federal Highway Administration standard, which is normally handled by decision tree methods that use vehicle axle information and a set of IF-THEN rules. By using a sample of 3326 vehicles, the proposed method showed improved classification results with an overall misclassification rate of only 2.9% compared to 9.7% of the decision tree methods. A similar setup can be used with different neural networks such as recurrent neural networks, but they were not tested in this study especially since the focus was for in situ applications where a high learning rate is desired.

Application of Probabilistic Neural Network in Pattern Classification

2013 ◽  
Vol 441 ◽  
pp. 738-741 ◽  
Author(s):  
Shuo Ding ◽  
Xiao Heng Chang ◽  
Qing Hui Wu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Classification ◽  
Probabilistic Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Networks ◽  
Training Samples ◽  
The One ◽  
Speed Accuracy

The network model of probabilistic neural network and its method of pattern classification and discrimination are first introduced in this paper. Then probabilistic neural network and three usually used back propagation neural networks are established through MATLAB7.0. The pattern classification of dots on a two-dimensional plane is taken as an example. Probabilistic neural network and improved back propagation neural networks are used to classify these dots respectively. Their classification results are compared with each other. The simulation results show that compared with back propagation neural networks, probabilistic neural network has simpler learning rules, faster training speed and it needs fewer training samples; the pattern classification method based on probabilistic neural network is very effective, and it is superior to the one based on back propagation neural networks in classifying speed, accuracy as well as generalization ability.

Classification of Capacity Loss Degree of Vanadium Redox Flow Battery Based on Probabilistic Neural Network

2013 ◽  
Vol 448-453 ◽  
pp. 2872-2878
Author(s):  
Hong Fei Cao ◽  
Xin Jian Zhu ◽  
Hai Feng Shen ◽  
Meng Shao
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Average Power ◽  
Classification Problem ◽  
Vanadium Redox Flow Battery ◽  
Redox Flow Battery ◽  
Flow Battery ◽  
Capacity Loss ◽  
Vanadium Redox

During long-time charge-discharge cycling, the capacity of the vanadium redox flow battery (VRFB) will reduce gradually. To recognize the capacity loss condition in the time of the operation process, a method for classification of the capacity loss degree based on Probabilistic Neural Network (PNN) is presented. The network inputs are the value of the voltage per second and the average power of the cell stack in any two minutes of the circulation. The network will give out three classes in form of three numbers to classify the capacity loss degree into different levels. The network is trained and validated by experimental data and the results show that the network is suitable for the classification problem of VRFB capacity loss and the method is useful to determine whether the capacity is sufficient and when to restore the cell capacity in real time.

Classification of Alloys with an Artificial Neural Network and Multivariate Calibration of Glow-Discharge Emission Spectra

1991 ◽  
Vol 45 (10) ◽  
pp. 1706-1716 ◽  
Author(s):  
Mark Glick ◽  
Gary M. Hieftje
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Glow Discharge ◽  
Multivariate Calibration ◽  
Emission Spectra ◽  
K Nearest Neighbor ◽  
The Neural Network ◽  
Calibration Sets ◽  
Artificial Neural

Artificial neural networks were constructed for the classification of metal alloys based on their elemental constituents. Glow discharge-atomic emission spectra obtained with a photodiode array spectrometer were used in multivariate calibrations for 7 elements in 37 Ni-based alloys (different types) and 15 Fe-based alloys. Subsets of the two major classes formed calibration sets for stepwise multiple linear regression. The remaining samples were used to validate the calibration models. Reference data from the calibration sets were then pooled into a single set to train neural networks with different architectures and different training parameters. After the neural networks learned to discriminate correctly among alloy classes in the training set, their ability to classify samples in the testing set was measured. In general, the neural network approach performed slightly better than the K-nearest neighbor method, but it suffered from a hidden classification mechanism and nonunique solutions. The neural network methodology is discussed and compared with conventional sample-classification techniques, and multivariate calibration of glow discharge spectra is compared with conventional univariate calibration.

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