neural-network-based model
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 ...
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A Neural Network Based Model for Loanword Identification in Uyghur
... CRFs-based model (CRFs), the string similarity based loanword identification model (SSIM) (Mi et ...identification model based on classification (CBIM) (Mi et ...RNN based ...
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4 Tier Neural Network based Model for Reliable
... Case-2: Error encounters during the transmission from source node to the destination Base Stationi Fault tolerance of the proposed model in case error occurs due to mistaken bits:Recover[r] ...
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Control of a Double -Effect Evaporator using Neural Network Model Predictive Controller
... The Neural network based model predictive controller are designed to control the concentration of the Double Effect ...the Neural networks Model predictive controller has a ...
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Hybrid Neural Network Controller Design for a Batch Reactor to Produce Methyl Methacrylate
... problem, neural networks studied and provided successfully to capture the dynamics of nonlinear and complex systems have been proposed and formulated ...[8-13]. Neural networks have several advantages of ...
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AN EFFICIENT SUPER PEER SELECTION ALGORITHM FOR PEER TO PEER (P2P) LIVE STREAMING NETWORK
... Neural Network based on Nonlinear Auto- Regressive model (NAR) has been utilized to get the dynamic response of the ...NARX model for numbers of neuron and number of delay used in NAR ...
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An Intrusion Detection Model based on a Convolutional Neural Network
... Traditional rule-based security solutions hardly detect advanced attacks such as zero-day attacks and advanced persistent threats (APT). Attackers acquire advanced skills and exploit unknown vulnerabilities to ...
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Research on Building Energy Consumption Prediction Method Based on LSTM Network
... Neural network model is a typical artificial intelligence ...BP neural network is the most classical neural network ...the model parameters are trained by error ...
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Design of Low Noise Amplifier of IRNSS using ANN
... determine neural network weights w such that the neural model output best matches the training ...trained neural network model can then be used during microwave design ...
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Detection of Network Intrusion Threat Based on the Probabilistic Neural Network Model
... users’ network security from the internal and external malicious attacks, briefly introduces the probabilistic neural network and principal component analysis method, and combines them for detection ...
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Deep Temporal Recurrent Replicated Softmax for Topical Trends over Time
... a neural temporal topic model which we name as RNN-RSM, based on prob- abilistic undirected graphical topic model RSM with time-feedback connections via determinis- tic RNN, to capture ...
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Comparative study of static and dynamic neural network models for nonlinear time series forecasting
... decades, neural network models have been focused upon by researchers due to their more real performance and on this basis different types of these models have been used in ...dynamic neural ...
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Factored Language Model based on Recurrent Neural Network
... Even though n is usually limited to three or four, the number of parameters in a back-off n-gram LM is still enormous. Assuming the vocabulary size is 64K , a 4-gram language model needs to estimate 64K 2 bigrams, ...
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The Gait Pattern Predicted Model Based on Neural Network
... Regression Neural Network (GRNN) was applied to predict the Fourier coefficient vectors for a specific subject in this ...Predicted Model (GPPM) was ...Predicted Model (GPPM) was ...waveforms ...
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A neural network-based framework for financial model calibration
... Heston model), and each candidate produces a number of market samples (here 35, ...Heston model each ...is based on the parallel DE combined with the ANN, where all parameter candidates in one ...
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Vehicle Model Recognition Based on Convolutional Neural Network
... vehicle model recognition method and explores the impacts of parameter setting, number of convolutional layers and moving average model on the recognition accuracy through ...the model is properly ...
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ASSOCIATION RULE MINING BASED VIDEO CLASSIFIER WITH LATE ACCEPTANCE HILL CLIMBING APPROACH
... a neural network ensemble model to perform the judgement of combustion diagnosis based on the spectral distribution of the light intensity pulse signal of the ...single neural ...
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Improving of Crystal Size Distribution Control Based on Neural Network Based Hybrid Model for Purified Terephthalic Acid Batch Crystallizer
... implemented model identification and an open-loop optimal temperature control strategy on a bench-scale potassium nitrate-water system ...artificial neural network. An obvious advantage of ...
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Research on Financial Early Warning of Listed Corporation Based on SOM Fusion BP Neural Network
... forecasting model of listed corporations based on the SOM network fusion BP network is ...The model firstly extracts the initial training samples re- lying on the SOM network and ...
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Prediction of Petroleum Price Using Back Propagation Artificial Neural Network Based on Chaotic Self-Adaptive Particle Swarm Algorithm
... is based on the idea of optimal exploitation of ...is based on Ulph [11] through the exploration of exhaustible resources, that is, starting from the petroleum market ...the model are poor through ...
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