scholarly journals A Novel Classification and Identification Scheme of Emitter Signals Based on Ward’s Clustering and Probabilistic Neural Networks with Correlation Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Xiaofeng Liao ◽  
Bo Li ◽  
Bo Yang
Keyword(s):  
Neural Networks ◽  
Correlation Analysis ◽  
Probabilistic Neural Network ◽  
Rapid Development ◽  
Optimal Number ◽  
Identification Accuracy ◽  
Identification Algorithm ◽  
Probabilistic Neural Networks ◽  
Ward Clustering ◽  
Clustering Validity

The rapid development of modern communication technology makes the identification of emitter signals more complicated. Based on Ward’s clustering and probabilistic neural networks method with correlation analysis, an ensemble identification algorithm for mixed emitter signals is proposed in this paper. The algorithm mainly consists of two parts, one is the classification of signals and the other is the identification of signals. First, self-adaptive filtering and Fourier transform are used to obtain the frequency spectrum of the signals. Then, the Ward clustering method and some clustering validity indexes are used to determine the range of the optimal number of clusters. In order to narrow this scope and find the optimal number of classifications, a sufficient number of samples are selected in the vicinity of each class center to train probabilistic neural networks, which correspond to different number of classifications. Then, the classifier of the optimal probabilistic neural network is obtained by calculating the maximum value of classification validity index. Finally, the identification accuracy of the classifier is improved effectively by using the method of Bivariable correlation analysis. Simulation results also illustrate that the proposed algorithms can accurately identify the pulse emitter signals.

scholarly journals Computing Discrete Adriatic Indices of Probabilistic Neural Network

2020 ◽  
Vol 13 (5) ◽  
pp. 1149-1161
Author(s):  
T Deepika ◽  
V. Lokesha
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Topological Index ◽  
Probabilistic Neural Network ◽  
Topological Indices ◽  
Probabilistic Neural Networks ◽  
Mathematical Chemistry ◽  
International Academy

A Topological index is a numeric quantity which characterizes the whole structure of a graph. Adriatic indices are also part of topological indices, mainly it is classified into two namely extended variables and discrete adriatic indices, especially, discrete adriatic indices are analyzed on the testing sets provided by the International Academy of Mathematical Chemistry (IAMC) and it has been shown that they have good presaging substances in many compacts. This contrived attention to compute some discrete adriatic indices of probabilistic neural networks.

RADIAL BASIS PROBABILISTIC NEURAL NETWORKS: MODEL AND APPLICATION

1999 ◽  
Vol 13 (07) ◽  
pp. 1083-1101 ◽  
Author(s):  
DE-SHUANG HUANG
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Model ◽  
Probabilistic Neural Network ◽  
Radial Basis Function Networks ◽  
Probabilistic Neural Networks ◽  
Huge Amount ◽  
Training Time ◽  
One Dimensional ◽  
Radial Basis

This paper investigates the capabilities of radial basis function networks (RBFN) and kernel neural networks (KNN), i.e. a specific probabilistic neural networks (PNN), and studies their similarities and differences. In order to avoid the huge amount of hidden units of the KNNs (or PNNs) and reduce the training time for the RBFNs, this paper proposes a new feedforward neural network model referred to as radial basis probabilistic neural network (RBPNN). This new network model inherits the merits of the two old odels to a great extent, and avoids their defects in some ways. Finally, we apply this new RBPNN to the recognition of one-dimensional cross-images of radar targets (five kinds of aircrafts), and the experimental results are given and discussed.

Damage Identification Using Wavelet Packet Transform and Neural Network Ensembles

2018 ◽  
Vol 18 (12) ◽  
pp. 1850148 ◽  
Author(s):  
Xiang Zhang ◽  
Renwen Chen ◽  
Qinbang Zhou
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Damage Identification ◽  
Probabilistic Neural Network ◽  
Wavelet Packet ◽  
Rotor Blades ◽  
Identification Accuracy ◽  
Vibration Responses ◽  
Neural Network Ensembles ◽  
Network Ensembles

This study presents a damage identification method that combines wavelet packet transforms (WPTs) with neural network ensembles (NNEs). The WPT is used to extract damage features, which are taken as the input vectors in the NNEs used for damage identification. An experiment was performed on a helicopter rotor blades structure to verify the proposed method. First, the vibration responses collected by different sensors are decomposed using the WPT. Second, the relative band energy of each decomposed frequency band is calculated and fused as the damage feature vectors. Third, two types of the NNEs are designed. One is based on the backward propagation neural networks (BPNNs) for detecting the damage locations and severities and the other one is based on the probabilistic neural network (PNN) to detect the damage types. Finally, the trained NNEs are employed in damage identification. From the identification outcomes, it is concluded that damage information can be extracted effectively by the WPT and the identification accuracy of the NNEs is better than that of individual neural networks (INNs).

scholarly journals Development of a LeNet-5 Gas Identification CNN Structure for Electronic Noses

Sensors ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 217 ◽  
Author(s):  
Guangfen Wei ◽  
Gang Li ◽  
Jie Zhao ◽  
Aixiang He
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Electronic Nose ◽  
Probabilistic Neural Network ◽  
Recognition Rate ◽  
Test System ◽  
Identification Accuracy ◽  
System Response ◽  
Electronic Noses ◽  
Gas Identification

A new LeNet-5 gas identification convolutional neural network structure for electronic noses is proposed and developed in this paper. Inspired by the tremendous achievements made by convolutional neural networks in the field of computer vision, the LeNet-5 was adopted and improved for a 12-sensor array based electronic nose system. Response data of the electronic nose to different concentrations of CO, CH4 and their mixtures were acquired by an automated gas distribution and test system. By adjusting the parameters of the CNN structure, the gas LeNet-5 was improved to recognize the three categories of CO, CH4 and their mixtures omitting the concentration influences. The final gas identification accuracy rate reached 98.67% with the unused data as test set by the improved gas LeNet-5. Comparison with results of Multiple Layer Perceptron neural networks and Probabilistic Neural Network verifies the improvement of recognition rate while with the same level of time cost, which proved the effectiveness of the proposed approach.

A Parametric Investigation of the Diagnostic Ability of Probabilistic Neural Networks on Turbofan Engines

2001 ◽  
Author(s):  
C. Romessis ◽  
A. Stamatis ◽  
K. Mathioudakis
Keyword(s):  
Neural Networks ◽  
Diagnostic Performance ◽  
Probabilistic Neural Network ◽  
Operating Conditions ◽  
Probabilistic Neural Networks ◽  
Training Set ◽  
Diagnostic Ability ◽  
Parametric Investigation ◽  
Turbofan Engines ◽  
And Training

Fault identification through the use of Artificial Neural Networks has become very popular recently. Probabilistic Neural Networks (PNN) is one of the architectures, which have mostly been investigated for gas turbine diagnostics. In this paper, the influence of parameters related to the structure and training on the diagnostic performance of a probabilistic Neural Network (PNN), is investigated. In particular, the parametric investigation examines the effect of the training set on the diagnostic performance of a PNN. The effect of noise level was also examined and found to be important. Another parameter examined is the severity of a fault, which was found to affect seriously the performance of the diagnostic PNN. Other parameters also examined are the effect of the operating conditions as well as the considered output parameters of the network. Guidelines useful for setting up this type of network, are derived.

scholarly journals An Efficient and Robust Star Identification Algorithm Based on Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7686
Author(s):  
Bendong Wang ◽  
Hao Wang ◽  
Zhonghe Jin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Identification Accuracy ◽  
Identification Algorithm ◽  
Rotation Invariant ◽  
One Dimensional ◽  
Identification Rate ◽  
Star Identification ◽  
Space Star ◽  
Fully Connected

A lost-in-space star identification algorithm based on a one-dimensional Convolutional Neural Network (1D CNN) is proposed. The lost-in-space star identification aims to identify stars observed with corresponding catalog stars when there is no prior attitude information. With the help of neural networks, the robustness and the speed of the star identification are improved greatly. In this paper, a modified log-Polar mapping is used to constructed rotation-invariant star patterns. Then a 1D CNN is utilized to classify the star patterns associated with guide stars. In the 1D CNN model, a global average pooling layer is used to replace fully-connected layers to reduce the number of parameters and the risk of overfitting. Experiments show that the proposed algorithm is highly robust to position noise, magnitude noise, and false stars. The identification accuracy is 98.1% with 5 pixels position noise, 97.4% with 5 false stars, and 97.7% with 0.5 Mv magnitude noise, respectively, which is significantly higher than the identification rate of the pyramid, optimized grid and modified log-polar algorithms. Moreover, the proposed algorithm guarantees a reliable star identification under dynamic conditions. The identification accuracy is 82.1% with angular velocity of 10 degrees per second. Furthermore, its identification time is as short as 32.7 miliseconds and the memory required is about 1920 kilobytes. The algorithm proposed is suitable for current embedded systems.

scholarly journals On the Topological Properties of the Certain Neural Networks

2018 ◽  
Vol 8 (4) ◽  
pp. 257-268 ◽  
Author(s):  
Jia-Bao Liu ◽  
Jing Zhao ◽  
Shaohui Wang ◽  
M. Javaid ◽  
Jinde Cao
Keyword(s):  
Neural Networks ◽  
Topological Index ◽  
Probabilistic Neural Network ◽  
Topological Indices ◽  
Topological Descriptors ◽  
Topological Properties ◽  
Neural Computing ◽  
Probabilistic Neural Networks ◽  
Computer Based ◽  
First Time

Abstract A topological index is a numeric quantity associated with a network or a graph that characterizes its whole structural properties. In [Javaid and Cao, Neural Computing and Applications, DOI 10.1007/s00521-017-2972-1], the various degree-based topological indices for the probabilistic neural networks are studied. We extend this study by considering the calculations of the other topological indices, and derive the analytical closed formulas for these new topological indices of the probabilistic neural network. Moreover, a comparative study using computer-based graphs has been carried out first time to clarify the nature of the computed topological descriptors for the probabilistic neural networks. Our results extend some known conclusions.

scholarly journals OPTIMAL CONSTRUCTION OF THE PATTERN MATRIX FOR PROBABILISTIC NEURAL NETWORKS IN TECHNICAL DIAGNOSTICS BASED ON EXPERT ESTIMATIONS

2021 ◽  
Author(s):  
Vadim Romanuke
Keyword(s):  
Neural Network ◽  
Error Rate ◽  
Probabilistic Neural Network ◽  
Optimal Number ◽  
Technical Diagnostics ◽  
Probabilistic Neural Networks ◽  
Number Of Clusters ◽  
Operation Speed ◽  
Pattern Matrix ◽  
Optimal Number Of Clusters

In the field of technical diagnostics, many tasks are solved by using automated classification. For this, such classifiers like probabilistic neural networks fit best owing to their simplicity. To obtain a probabilistic neural network pattern matrix for technical diagnostics, expert estimations or measurements are commonly involved. The pattern matrix can be deduced straightforwardly by just averaging over those estimations. However, averages are not always the best way to process expert estimations. The goal is to suggest a method of optimally deducing the pattern matrix for technical diagnostics based on expert estimations. The main criterion of the optimality is maximization of the performance, in which the subcriterion of maximization of the operation speed is included. First of all, the maximal width of the pattern matrix is determined. The width does not exceed the number of experts. Then, for every state of an object, the expert estimations are clustered. The clustering can be done by using the k-means method or similar. The centroids of these clusters successively form the pattern matrix. The optimal number of clusters determines the probabilistic neural network optimality by its performance maximization. In general, most results of the error rate percentage of probabilistic neural networks appear to be near-exponentially decreasing as the number of clustered expert estimations is increased. Therefore, if the optimal number of clusters defines a too “wide” pattern matrix whose operation speed is intolerably slow, the performance maximization implies a tradeoff between the error rate percentage minimum and maximally tolerable slowness in the probabilistic neural network operation speed. The optimal number of clusters is found at an asymptotically minimal error rate percentage, or at an acceptable error rate percentage which corresponds to maximally tolerable slowness in operation speed. The optimality is practically referred to the simultaneous acceptability of error rate and operation speed.

scholarly journals Transformer-based artificial neural networks for the conversion between chemical notations

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lev Krasnov ◽  
Ivan Khokhlov ◽  
Maxim V. Fedorov ◽  
Sergey Sosnin
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Rapid Development ◽  
Performance Level ◽  
Rule Based ◽  
Development Costs ◽  
Overall Performance ◽  
Artificial Neural

AbstractWe developed a Transformer-based artificial neural approach to translate between SMILES and IUPAC chemical notations: Struct2IUPAC and IUPAC2Struct. The overall performance level of our model is comparable to the rule-based solutions. We proved that the accuracy and speed of computations as well as the robustness of the model allow to use it in production. Our showcase demonstrates that a neural-based solution can facilitate rapid development keeping the required level of accuracy. We believe that our findings will inspire other developers to reduce development costs by replacing complex rule-based solutions with neural-based ones.

Pretrained Convolutional Neural Networks Perform Well in a Challenging Test Case: Identification of Plant Bugs (Hemiptera: Miridae) Using a Small Number of Training Images

2021 ◽  
Vol 5 (2) ◽  
Author(s):  
Alexander Knyshov ◽  
Samantha Hoang ◽  
Christiane Weirauch
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Training Image ◽  
Species Level ◽  
Identification Accuracy ◽  
Support Vector ◽  
Test Case ◽  
Learning Approaches ◽  
Lower Accuracy ◽  
Neural Network Classifiers

Abstract Automated insect identification systems have been explored for more than two decades but have only recently started to take advantage of powerful and versatile convolutional neural networks (CNNs). While typical CNN applications still require large training image datasets with hundreds of images per taxon, pretrained CNNs recently have been shown to be highly accurate, while being trained on much smaller datasets. We here evaluate the performance of CNN-based machine learning approaches in identifying three curated species-level dorsal habitus datasets for Miridae, the plant bugs. Miridae are of economic importance, but species-level identifications are challenging and typically rely on information other than dorsal habitus (e.g., host plants, locality, genitalic structures). Each dataset contained 2–6 species and 126–246 images in total, with a mean of only 32 images per species for the most difficult dataset. We find that closely related species of plant bugs can be identified with 80–90% accuracy based on their dorsal habitus alone. The pretrained CNN performed 10–20% better than a taxon expert who had access to the same dorsal habitus images. We find that feature extraction protocols (selection and combination of blocks of CNN layers) impact identification accuracy much more than the classifying mechanism (support vector machine and deep neural network classifiers). While our network has much lower accuracy on photographs of live insects (62%), overall results confirm that a pretrained CNN can be straightforwardly adapted to collection-based images for a new taxonomic group and successfully extract relevant features to classify insect species.

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