AN APPLICATION OF SMALL-WORLD CELLULAR NEURAL NETWORKS ON ODOR CLASSIFICATION

2012 ◽  
Vol 22 (01) ◽  
pp. 1250013 ◽  
Author(s):  
TUBA AYHAN ◽  
MÜŞTAK E. YALÇIN
Keyword(s):  
Neural Network ◽  
Biological Networks ◽  
Antennal Lobe ◽  
Small World ◽  
Classification Problem ◽  
Cellular Neural Network ◽  
Classification Performance ◽  
Artificial System ◽  
Odor Classification ◽  
Better Than

Many biological networks are constructed with both regular and random connections between neurons. Bio-inspired systems should prevent this mixed topology of biological networks while the artificial system is still realizable. In this work, a bio-inspired network which has many analog realizations, Cellular Neural Network (CNN) is investigated under existing random connections in addition to its regular connections: Small-World Cellular Neural Network (SWCNN). Antennal Lobe, an organ in the olfaction system of insects, is modeled with SWCNN by extending the network with the use of two types of processors on the same network. The model combined with a classifier, SVM and overall system is tested with a five-class odor classification problem. While all neurons are connected to each other with direct or indirect connections in CNNs, the idea of short-cuts does not provide an improvement in classification performance but the results show that the fault tolerance ability of SWCNN is better than the classical CNN.

Endek Classification Based On GLCM Using Artificial Neural Networks with Adam Optimization

2020 ◽  
Vol 9 (2) ◽  
pp. 285
Author(s):  
Putu Wahyu Tirta Guna ◽  
Luh Arida Ayu Ayu Rahning Putri
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Back Propagation ◽  
Classification Problem ◽  
Model Accuracy ◽  
Computing Power ◽  
The Neural Network ◽  
Extraction Algorithm ◽  
Better Than

Not many people know that endek cloth itself has 4 known variances. .Nowadays. Computing and classification algorithm can be implemented to solve classification problem with respect to the features data as input. We can use this computing power to digitalize these endek pattern. The features extraction algorithm used in this research is GLCM. Where these data will act as input for the neural network model later. There is a lot of optimizer algorithm to use in back propagation phase. In this research we  prefer to use adam which is one of the newest and most popular optimizer algorithm. To compare its performace we also use SGD which is older and popular optimizer algorithm. Later we find that adam algorithm generate 33% accuracy which is better than what SGD algorithm give, it is 23% accuracy. Longer epoch also give affect for overall model accuracy.

CELLULAR NEURAL NETWORKS FOR EDGE DETECTION

2007 ◽  
Vol 17 (04) ◽  
pp. 1323-1328
Author(s):  
GIUSEPPE GRASSI ◽  
PIETRO VECCHIO ◽  
EUGENIO DI SCIASCIO ◽  
LUIGI A. GRIECO
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Edge Detection ◽  
Cellular Neural Network ◽  
Cellular Neural Networks ◽  
Detection Technique ◽  
Hardware Implementations ◽  
Rigorous Model ◽  
Better Than

This Letter presents an effective edge detection technique based on the cellular neural network paradigm. The approach exploits a rigorous model of the image contours and takes into account some electrical restrictions of existing hardware implementations. The method yields accurate results, better than the ones achievable by other cellular neural network-based techniques.

Power Quality Events Classification Using MWT and MLP

2011 ◽  
Vol 403-408 ◽  
pp. 4266-4271 ◽  
Author(s):  
Manish Kumar Saini ◽  
Rajiv Kapoor
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Power System ◽  
Power Quality ◽  
Scaling Function ◽  
Classification Performance ◽  
Correct Classification ◽  
Multi Layer Perceptron ◽  
Feature Extractor ◽  
Better Than

The work presented uses multiwavelet because of its inherent property to resolve the signal better than all single wavelets. Multiwavelets are based on more than one scaling function. The proposed methodology utilizes an enhanced resolving capability of multiwavelet to recognize power system disturbances. The disturbance classification schema is performed with multiwavelet neural network (MWNN). It performs a feature extraction and a classification algorithm composed of a multiwavelet feature extractor based on norm entropy and a classifier based on a multi-layer perceptron. The performance of this classifier is evaluated by using total 1000 PQ disturbance signals which are generated the based model. The classification performance of different PQ disturbance using proposed algorithm is tested. The rate of average correct classification is about 99.65% for the different PQ disturbance signals and noisy disturbances.

scholarly journals Research on grounding grid corrosion classification method based on convolutional neural network

2018 ◽  
Vol 160 ◽  
pp. 01008
Author(s):  
Jingyi Du ◽  
Liqian Yan ◽  
Haixia Wang ◽  
Qiong Huang
Keyword(s):  
Neural Network ◽  
Classification Performance ◽  
Detection Methods ◽  
Svm Classifier ◽  
Image Method ◽  
Grid Simulation ◽  
Grounding Grid ◽  
Grounding Grids ◽  
Improved Model ◽  
Better Than

Aiming at the problem that the traditional detection methods can not accurately classify the corrosion degree of grounding grids. The corrosion image is taken as the research object, the convolution neural network is used as the algorithm firstly to classify the corrosion degree. Firstly, the corrosion simulation experiment was carried out, and the sample library was established by using the corrosion image collected in different stages. Then, according to the LeNet-5 model, the traditional CNN and improved CNN models were designed for corrosion classification of grounding grid. Simulation experiments were carried out in the preprocessed samples. Finally, the experimental results of Soft-max and SVM classifier are compared and analyzed. The results show: the classification results of the two models were better than those of the original samples, and the classification performance of SVM is better than that of Soft-max. The improved model can improve classification accuracy. This study fills the blank of detecting the corrosion degree of grounding grid by image method, and it is significant to quickly grasp the corrosion degree to avoid faults or accidents.

scholarly journals Dimensionality Reduction Algorithms on High Dimensional Datasets

2014 ◽  
Vol 2 (2) ◽  
Author(s):  
Iwan Syarif
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Dimensionality Reduction ◽  
Particle Swarm ◽  
Classification Problem ◽  
Classification Performance ◽  
High Dimensional ◽  
Selection Algorithms ◽  
High Dimensional Datasets ◽  
Better Than

Classification problem especially for high dimensional datasets have attracted many researchers in order to find efficient approaches to address them. However, the classification problem has become very complicatedespecially when the number of possible different combinations of variables is so high. In this research, we evaluate the performance of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) as feature selection algorithms when applied to high dimensional datasets.Our experiments show that in terms of dimensionality reduction, PSO is much better than GA. PSO has successfully reduced the number of attributes of 8 datasets to 13.47% on average while GA is only 31.36% on average. In terms of classification performance, GA is slightly better than PSO. GA‐ reduced datasets have better performance than their original ones on 5 of 8 datasets while PSO is only 3 of 8 datasets.Keywords: feature selection, dimensionality reduction, Genetic Algorithm (GA), Particle Swarm Optmization (PSO).

scholarly journals Semantic Graph Neural Network: A Conversion from Spam Email Classification to Graph Classification

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Weisen Pan ◽  
Jian Li ◽  
Lisa Gao ◽  
Liexiang Yue ◽  
Yan Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Classification Problem ◽  
Graph Classification ◽  
Method Performance ◽  
The Public ◽  
Semantic Graph ◽  
Public Dataset ◽  
Public Datasets ◽  
Better Than ◽  
Email Classification

In this study, we propose a method named Semantic Graph Neural Network (SGNN) to address the challenging task of email classification. This method converts the email classification problem into a graph classification problem by projecting email into a graph and applying the SGNN model for classification. The email features are generated from the semantic graph; hence, there is no need of embedding the words into a numerical vector representation. The method performance is tested on the different public datasets. Experiments in the public dataset show that the presented method achieves high accuracy in the email classification test against a few public datasets. The performance is better than the state-of-the-art deep learning-based method in terms of spam classification.

Optimizing Time-multiplexing Raster Cellular Neural Network simulator using genetic algorithms with RK8(6)

2011 ◽  
Vol 3 (6) ◽  
pp. 87-90
Author(s):  
O. H. Abdelwahed O. H. Abdelwahed ◽  
◽  
M. El-Sayed Wahed ◽  
O. Mohamed Eldaken
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Cellular Neural Network ◽  
Network Simulator
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