Application of a Multi-Structure Neural Network (MSNN) to Sorting Pistachio Nuts

1997 ◽  
Vol 08 (01) ◽  
pp. 55-61 ◽  
Author(s):  
Ahmad Ghazanfari ◽  
Anthony Kusalik ◽  
Joseph Irudayaraj
Keyword(s):  
Neural Network ◽  
Single Neuron ◽  
Back Propagation ◽  
Classification Performance ◽  
Fourier Descriptor ◽  
Back Propagation Algorithm ◽  
Feed Forward Neural Network ◽  
Pistachio Nuts ◽  
The Individual

A multi-structure neural network (MSNN) classifier consisting of four discriminators followed by a maximum selector was designed and applied to classification of four grades of pistachio nuts. Each discriminator was a multi-layer feed-forward neural network with two hidden layers and a single-neuron output layer. Fourier descriptor of the nuts' boundaries and their area were used as the recognition features. The individual discriminators were trained using a biased technique and a back-propagation algorithm. The MSNN classifier gave an average classification performance of 95.0%. This was an increase of 14.8% over the performance of a multi-layer neural network (MLNN) with similar complexity for classifying the same set of patterns.

scholarly journals Modeling of Speech Recognition Using Artificial Neural Network

2019 ◽  
Vol 8 (9) ◽  
pp. 1225-1229
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Pattern Matching ◽  
Back Propagation ◽  
Experimental Result ◽  
Communication Link ◽  
Back Propagation Algorithm ◽  
Feed Forward Neural Network ◽  
The Voice

Automatic speech recognition has attained a lot of significance as it can act as easy communication link between machines and humans. This mode of communication is easy for man to use as it is effortless and easy. Many approaches for extraction of the features of the speech and classification of speech have been considered. This paper unveils the importance of neutral network and the way it can be used for recognition of speech. Mel Frequency Cepstrum Coefficients is made use of for extraction of the features from the voice. For pattern matching neural network has been used. MATLAB has been used to show how the speech is recognized. In this paper the speech recognition has been done firstly by multilayer feed forward neural network using Back propagation algorithm. Then the process of speech recognition is shown by using Radial basis function neural network. The paper then analyzes the performance of both the algorithms and experimental result shows that BPNN outperforms the RBFNN.

scholarly journals Neural networks used for speech recognition

2010 ◽  
Vol 20 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Wouter Gevaert ◽  
Georgi Tsenov ◽  
Valeri Mladenov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Speech Recognition ◽  
Radial Basis Functions ◽  
Back Propagation ◽  
Classification Performance ◽  
Basis Functions ◽  
Back Propagation Algorithm ◽  
Feed Forward Neural Network ◽  
Propagation Algorithm

In this paper is presented an investigation of the speech recognition classification performance. This investigation on the speech recognition classification performance is performed using two standard neural networks structures as the classifier. The utilized standard neural network types include Feed-forward Neural Network (NN) with back propagation algorithm and a Radial Basis Functions Neural Networks.

scholarly journals Deep neural networks using a single neuron: folded-in-time architecture using feedback-modulated delay loops

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Florian Stelzer ◽  
André Röhm ◽  
Raul Vicente ◽  
Ingo Fischer ◽  
Serhiy Yanchuk
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Neural Network ◽  
Single Neuron ◽  
Deep Neural Networks ◽  
Back Propagation ◽  
Local Network ◽  
Multiple Time ◽  
Learning Tools ◽  
Back Propagation Algorithm

AbstractDeep neural networks are among the most widely applied machine learning tools showing outstanding performance in a broad range of tasks. We present a method for folding a deep neural network of arbitrary size into a single neuron with multiple time-delayed feedback loops. This single-neuron deep neural network comprises only a single nonlinearity and appropriately adjusted modulations of the feedback signals. The network states emerge in time as a temporal unfolding of the neuron’s dynamics. By adjusting the feedback-modulation within the loops, we adapt the network’s connection weights. These connection weights are determined via a back-propagation algorithm, where both the delay-induced and local network connections must be taken into account. Our approach can fully represent standard Deep Neural Networks (DNN), encompasses sparse DNNs, and extends the DNN concept toward dynamical systems implementations. The new method, which we call Folded-in-time DNN (Fit-DNN), exhibits promising performance in a set of benchmark tasks.

A Feed forward Neural Network MPPT Control Strategy Applied to a Modified Cuk Converter

2016 ◽  
Vol 6 (4) ◽  
pp. 1421
Author(s):  
Mohamed Tahar Makhloufi ◽  
Yassine Abdessemed ◽  
Mohamed Salah Khireddine
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Control Strategy ◽  
Back Propagation ◽  
Network Control ◽  
Back Propagation Algorithm ◽  
Feed Forward Neural Network ◽  
Cuk Converter ◽  
Neural Network Controller ◽  
Artificial Neural

<p class="References">This paper presents an intelligent control strategy that uses a feedforward artificial neural network in order to improve the performance of the MPPT (Maximum Power Point Tracker) MPPT photovoltaic (PV) power system based on a modified Cuk converter. The proposed neural network control (NNC) strategy is designed to produce regulated variable DC output voltage. The mathematical model of Cuk converter and artificial neural network algorithm is derived. Cuk converter has some advantages compared to other type of converters. However the nonlinearity characteristic of the Cuk converter due to the switching technique is difficult to be handled by conventional controller. To overcome this problem, a neural network controller with online learning back propagation algorithm is developed. The NNC designed tracked the converter voltage output and improve the dynamic performance regardless load disturbances and supply variations. The proposed controller effectiveness during dynamic transient response is then analyze and verified using MATLAB-Simulink. Simulation results confirm the excellent performance of the proposed NNC.</p>

LEAF DISEASE CLASSIFICATION USING ARTIFICIAL NEURAL NETWORK

2015 ◽  
Vol 77 (17) ◽  
Author(s):  
Syafiqah Ishak ◽  
Mohd Hafiz Fazalul Rahiman ◽  
Siti Nurul Aqmariah Mohd Kanafiah ◽  
Hashim Saad
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Image Processing ◽  
Back Propagation ◽  
Disease Classification ◽  
Back Propagation Algorithm ◽  
Feed Forward Neural Network ◽  
Leaf Quality ◽  
Leaf Disease ◽  
Color Transformation

Nowadays, herb plants are importance to medical field and can give benefit to human. In this research, Phyllanthus Elegans Wall (Asin-Asin Gajah) is used to analyse and to classify whether it is healthy or unhealthy leaf. This plant was chosen because its function can cure breast cancer. Therefore, there is a need for alternative cure for patient of breast cancer rather than use the technology such as Chemotherapy, surgery or use of medicine from hospital. The purpose of this research to identify the quality of leaf and using technology in agriculture field. The process to analysis the leaf quality start from image acquisition, image processing, and classification. For image processing method, the most important for this part is the segmentation using HSV to input RGB image for the color transformation structure. The analysis of leaf disease image is applied based on colour and shape. Finally, the classification method use feed-forward Neural Network, which uses Back-propagation algorithm. The result shows comparison between Multi-layer Perceptron (MLP) and Radial Basis Function (RBF) and comparison between MLP and RBF shown in percentage of accuracy. MLP and RBF is algorithm for Neural Network. Conclusively, classifier of Neural Network shows better performance and more accuracy.

scholarly journals A Feed forward Neural Network MPPT Control Strategy Applied to a Modified Cuk Converter

2016 ◽  
Vol 6 (4) ◽  
pp. 1421
Author(s):  
Mohamed Tahar Makhloufi ◽  
Yassine Abdessemed ◽  
Mohamed Salah Khireddine
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Control Strategy ◽  
Back Propagation ◽  
Network Control ◽  
Back Propagation Algorithm ◽  
Feed Forward Neural Network ◽  
Cuk Converter ◽  
Neural Network Controller ◽  
Artificial Neural

<p class="References">This paper presents an intelligent control strategy that uses a feedforward artificial neural network in order to improve the performance of the MPPT (Maximum Power Point Tracker) MPPT photovoltaic (PV) power system based on a modified Cuk converter. The proposed neural network control (NNC) strategy is designed to produce regulated variable DC output voltage. The mathematical model of Cuk converter and artificial neural network algorithm is derived. Cuk converter has some advantages compared to other type of converters. However the nonlinearity characteristic of the Cuk converter due to the switching technique is difficult to be handled by conventional controller. To overcome this problem, a neural network controller with online learning back propagation algorithm is developed. The NNC designed tracked the converter voltage output and improve the dynamic performance regardless load disturbances and supply variations. The proposed controller effectiveness during dynamic transient response is then analyze and verified using MATLAB-Simulink. Simulation results confirm the excellent performance of the proposed NNC.</p>

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