trained neural network model
Design of Low Noise Amplifier of IRNSS using ANN
... determine neural network weights w such that the neural model output best matches the training ...A trained neural network model can then be used during microwave ...
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A fast adaptive neural network system for intelligent control
... One solution is to model the system with two neural networks in parallel whereby one network is trained a priori with a wide range of historical dynamics while the [r] ...
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Auditory representations of speech sounds in a neural model: The role of peripheral processing
... computational model can mimic important aspects of the speech categorization behav- ior of human and animal ...artificial neural network ‘back-end’, modeling more central ...suitably trained ...
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Transfer learning using VGG-16 with Deep Convolutional Neural Network for Classifying Images
... The model built by using convolutional neural network in Ⅰ is trained on small dataset of images; therefore, it didn’t predict well and ended up overfitting after 3 epochs (See ...the ...
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KNOWLEDGE-BASED NEURAL NETWORK FOR LINE FLOW CONTINGENCY SELECTION AND RANKING
... Artificial Neural Network based method for MW security assessment corresponding to line outage events have been reported by various authors in the ...of Neural Networks is to extract rules that can ...
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Neural network model predictive control of a ultra high temperature milk treatment plant : a thesis presented in partial fulfilment of the requirements for the degree of Master of Technology in Engineering and Automation at Massey University
... 8.2.5 'hemodtes.m' % Trained heat exchanger neural network sub-model test function % % This function gives the heat exchanger network sub-model's output % prediction of another plant run[r] ...
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Title: DIABETIC RETINOPATHY DETECTION USING DEEP NEURAL NETWORK
... described neural network model based Desktop application works well for identification of Diabetic ...deep neural network architecture that is trained on thousands of images on ...
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A Neural Network Model Deployed in the Cloud for Prediction of Network Traffic
... (BP) neural network which is then used by the source node to adjust the sent-out rate ...(FIR) neural network and controlled congestion by throttling the input arrival ...on ...
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A Syllable based Technique for Word Embeddings of Korean Words
... learning model for Korean using a convolutional neural network, in which word representation is composed of trained syllable ...Our model successfully produces morphologi- cally ...
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NTHU at the CoNLL 2014 Shared Task
... ngram model and recurrent neural network (RNN) model, are used in correcting spelling, noun number, word form, and determiner ...We trained the ngram language model on English ...
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Fault Diagnosis of Electronic Circuits Based on Matlab
... the model of the inverter in Matlab / Simulink environment and simulate the fault of the inverter, and then collect the corresponding fault ...BP neural network was trained by using the ...
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Plant Disease Identification using Deep Neural Networks
... Automatic identification of disease in plants from their leaves is one of the most challenging task to researchers. Diseases among plants degrade their performance and results into a huge reduction of agricultural ...
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Effective Prediction of Thermal Conductivity of Concrete Using Neural Network Method
... The neural network, a prediction method for the estimation of the TCC, was constructed and trained using 124 experi- mental data obtained by previous studies (Kim et ...developed neural ...
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Local System Voting Feature for Machine Translation System Combination
... confusion network system combination approach with an additional model trained by a neural ...voting model by a neural network which is based on the words themselves and ...
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Artificial Neural Network Model for Compressive Strength of Lateritic Blocks
... best trained architecture, 3-71-1 with mix ratios inputted was used to predict the compressive strength of lateritic blocks as shown in Table ...Artificial Neural Network model for compressive ...
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Multimodal Decision level Group Sentiment Prediction of Students in Classrooms
... Convolutional Neural Network (CNN) model trained on the FER2013 facial images database to generate the feature vector for classification of video-based ...Recurrent Neural ...
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Detection and Classification of Leukaemia using Artificial Neural Network
... An automated system to detect leukaemia cells and classify it into their respective type is designed and created successfully . Images are segmented using k-means segmentation algorithm. Various features are extracted ...
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Computational Analysis of Sag and Swell in Electrical Power Supply Network
... (FFBP) neural network is used to approximation the values of sags and swells of a power supply ...recurrent neural network (RNN) is also used to approximation the values of sag and swell of ...
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Using of Artificial Neural Networks to Recognize the Noisy Accidents Patterns of Nuclear Research Reactors
... on neural networks for recognizing the nuclear research reactor accidents (patterns) is ...A neural network is designed and trained, initially without noise, to recognize the nuclear research ...
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EEG Signal Classification into Seizure and Non-Seizure Class using Discrete Wavelet Transform and Artificial Neural Network
... ABSTRACT : In this paper, a wavelet-based neural network (WNN) classifier for recognizing EEG signals is implemented. Main part of paper presents basic principles of signal decomposition in connection with ...
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