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Neural Network (NN)

Adaptive Control of a DC Motor Using Neural Network Sliding Mode Control

Adaptive Control of a DC Motor Using Neural Network Sliding Mode Control

... adaptive neural network sliding mode controller proposed to control a DC motor, so we compare proposed controller with PID, classic SMC and ALNN, The simulation results proved that proposed controller is a ...

5

Intrusion Detection using Artificial Neural Network and Swarm Intelligence Algorithm

Intrusion Detection using Artificial Neural Network and Swarm Intelligence Algorithm

... Artificial Neural Networks (ANNs) have been displayed that can bring an enormous agreement of support in medical domains of oncology, critical care, cardiovascular medicine, bioinformatics including survival study ...

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Neural Network Based Model Predictive Control of Batch Extractive Distillation Process for Improving Purity of Acetone

Neural Network Based Model Predictive Control of Batch Extractive Distillation Process for Improving Purity of Acetone

... of neural network model-based controllers: the neural network direct inverse model control (NNDIC) and neural network based model predictive control (NNMPC) in order to achieve ...

13

Image Description Using Deep Neural Network

Image Description Using Deep Neural Network

... Recurrent neural networks (RNN) are quite popular for text generation, and so many researchers use them in this task, albeit in different settings Karpathy and Fei-Fei[3], Vinyals et al [4] are influenced by ...

6

Research of Teacher’s Performance Evaluation Model Based on AHP and Improved PSO BP Neural Network

Research of Teacher’s Performance Evaluation Model Based on AHP and Improved PSO BP Neural Network

... BP neural network in the teachers' performance comprehensive evaluation, such as non-convergence and large prediction error, the paper proposed an evaluation index system based on analytic hierarchy process ...

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Automatic diagnosis of imbalanced ophthalmic images using a cost-sensitive deep convolutional neural network

Automatic diagnosis of imbalanced ophthalmic images using a cost-sensitive deep convolutional neural network

... convolutional neural network (CNN) models have demon- strated extraordinary performance in medical image recognition tasks ...residual network [30]) and a cost-sensitive data-balancing method to ...

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Comparative Study of Various Neural Network Models for Software Quality Estimation

Comparative Study of Various Neural Network Models for Software Quality Estimation

... of neural network in software quality estimation based on object oriented ...three neural network models namely Ward Neural Network (WNN), General Regression Neural ...

11

Fruit Image Classification using Convolutional Neural Network

Fruit Image Classification using Convolutional Neural Network

... In order to create our convolutional neural network we used Tensor Flow. This is an open source framework for machine learning created by Google for numerical computation using data flow graphs. Nodes in ...

13

Prediction of Tourist Quantity Based on RBF Neural Network

Prediction of Tourist Quantity Based on RBF Neural Network

... RBF neural network [1], in which the principle and algorithm of RBF neural network is ...RBF neural network model shows based on RBF neural network tourist quantity ...

6

Comparative Study of Static and Dynamic Artificial Neural Network Models in Forecasting of Tehran Stock Exchange

Comparative Study of Static and Dynamic Artificial Neural Network Models in Forecasting of Tehran Stock Exchange

... of network architecture, in this part before different types of feed-forward neural network models, some points related to the network architecture will be ...

17

Short Term Load Forecasting With Feed Forward Neural Network Algorithm

Short Term Load Forecasting With Feed Forward Neural Network Algorithm

... Load forecasting plays an important role in power system planning and operation. Basic operating functions such as unit commitment, economic dispatch, fuel scheduling and unit maintenance, can be performed efficiently ...

24

Speech Enhancement Using Neural Network

Speech Enhancement Using Neural Network

... The main objective of this system is to enhance the speech signal to obtain a clean signal with higher quality. The signal-processing problem of noise reduction and speech enhancement has received considerable attention ...

5

Research on neural network chaotic encryption algorithm in wireless network security communication

Research on neural network chaotic encryption algorithm in wireless network security communication

... discrete neural net- work, the chaotic neural network has more nonlinear dynamic characteristics and complexity, and its main features are reflected in chaotic ...the neural network, ...

10

Neural Network Toolbox for MATLAB - Free Computer, Programming, Mathematics, Technical Books, Lecture Notes and Tutorials

Neural Network Toolbox for MATLAB - Free Computer, Programming, Mathematics, Technical Books, Lecture Notes and Tutorials

... The Neural Network Toolbox implements a number of these ...a network on a representative set of input/ target pairs and get good results without training the network on all possible ...

840

Neural Network Design - Free Computer, Programming, Mathematics, Technical Books, Lecture Notes and Tutorials

Neural Network Design - Free Computer, Programming, Mathematics, Technical Books, Lecture Notes and Tutorials

... of neural networks become ap- parent only for large-scale problems, which are computationally intensive and not feasible for hand ...MATLAB, neural network al- gorithms can be quickly implemented, ...

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The Application of BP Neural Network in Leukocyte Classification Recognition

The Application of BP Neural Network in Leukocyte Classification Recognition

... Artificial neural network is the most widely used and most mature technology in artificial intelligence information fusion; classification recognition is one of the main ...BP neural network ...

7

Modelling and predicting of MODIS leaf area index time series based on a hybrid SARIMA and BP neural network method

Modelling and predicting of MODIS leaf area index time series based on a hybrid SARIMA and BP neural network method

... BP neural network and a hybrid method of SARIMA-BP neural network were used for modeling and predicting MODIS LAI time ...SARIMA-BP neural network combined both SARIMA and BP ...

7

Comparative study of static and dynamic neural network models for nonlinear time series forecasting

Comparative study of static and dynamic neural network models for nonlinear time series forecasting

... as neural network models, in modeling and forecasting the market indexes can yield impressive results (Aladag et ...as neural network models, is a response to the lack of consensus on ...

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Differential evolution for neural networks learning enhancement

Differential evolution for neural networks learning enhancement

... The first step, weights are encoded into chromosome format and the second step is to define a fitness function for evaluating the chromosome’s performance. This function must estimate the performance of a given ...

113

Tree Copula Theory Based Fusion And Compressive Sensing For Activity Detection using Multi Modal Data

Tree Copula Theory Based Fusion And Compressive Sensing For Activity Detection using Multi Modal Data

... as Neural Network (NN), Deep Belief Neural Network (DBN), and the Recurrent Neural network (RNN) are compared with the proposed TC-GO based DCNN classifier in order to convey the ...

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