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dynamic neural inverse model

Hybrid Neural Network Controller Design for a Batch Reactor to Produce Methyl Methacrylate

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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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

... to model the complex systems (Wanous et al, ...the inverse transformation of obtained fuzzy numbers to the numerical data in the real-world (Tsipouras et al, ...

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Implementation of neural network based inverse model control strategies on an exothermic reactor

Implementation of neural network based inverse model control strategies on an exothermic reactor

... and dynamic behaviour, such as ignition/extinction behaviour and parametric sensitivity create challenges that are tough for traditional linear controllers to ...based inverse-model control ...

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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

... a dynamic optimization strategy is applied to determine an acetone distillate composition profile maximizing the weight of the distillate product ...of neural network model-based controllers: the ...

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NEURO MODELING AND CONTROL STRATEGIES FOR A pH PROCESS

NEURO MODELING AND CONTROL STRATEGIES FOR A pH PROCESS

... the dynamic behavior of such systems using conventional modeling techniques thereby designing controller ...a neural network based internal model control is designed for a pH process and its ...

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Nonlinear System Identification Using Hammerstein-Wiener Neural Network and subspace algorithms

Nonlinear System Identification Using Hammerstein-Wiener Neural Network and subspace algorithms

... extracted rows/columns space matrices include information about model and have high calculation performance. Also, these algorithms work based on linear algebra, mathematics, SVD and QR methods. So, they are ...

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The TALP UPC Neural Machine Translation System for German/Finnish English Using the Inverse Direction Model in Rescoring

The TALP UPC Neural Machine Translation System for German/Finnish English Using the Inverse Direction Model in Rescoring

... Our system uses the architecture from (Lee et al., 2016) where a character-level neural MT model maps the source character sequence to the target character sequence. The main difference in the encoder ...

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Biomechanical Investigation of Empirical Optimal Trajectories Introduced for Snatch Weightlifting

Biomechanical Investigation of Empirical Optimal Trajectories Introduced for Snatch Weightlifting

... We calculate all costs from each point in i th step to each point in ( i + 1 ) th step. We have many numbers as the cost related to each group of two adjacent points. We save them in a table and start the main algorithm, ...

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Combining spatial and parametric working memory in a dynamic neural field model

Combining spatial and parametric working memory in a dynamic neural field model

... to the value of continuous sensory or motor variables can be broadly classified in two distinct but not mutually exclusive coding schemes. Summation coding re- flects the idea that parameter values are represented by a ...

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Identification and control of electro mechanical systems using state dependent parameter estimation

Identification and control of electro mechanical systems using state dependent parameter estimation

... d ɺ ɺ are to be trusted. In this case, the SDP iterative algorithm converges in 5 iterations and the results plotted in Fig.7, together with the parameter estimates given in Table 4, demonstrate that the iterative SDP ...

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Span Based Constituency Parsing with a Structure Label System and Provably Optimal Dynamic Oracles

Span Based Constituency Parsing with a Structure Label System and Provably Optimal Dynamic Oracles

... of dynamic oracles to train a neural network model, where we collect all action examples for a given sentence before updat- ing, is “training with exploration” as proposed by Goldberg and Nivre ...

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Mechanisms of Intermittent State Transitions in a Coupled Heterogeneous Oscillator Model of Epilepsy

Mechanisms of Intermittent State Transitions in a Coupled Heterogeneous Oscillator Model of Epilepsy

... similar model approximation approach was taken recently by [32], who sought to uncover the dynamic principles underlying multi-stability and scale invariant fluc- tuations in the human alpha ...inspired ...

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Digital Inverse Filter Model

Digital Inverse Filter Model

... So, in this paper for the linear model, the model of the device distorting the digital inverse filter, optimal in the sense of Wiener. The influence of the choice of the number of coefficients and ...

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Behavioral Modeling of RF Power Amplifiers with Memory Effects Using Orthonormal Hermite Polynomial Basis Neural Network

Behavioral Modeling of RF Power Amplifiers with Memory Effects Using Orthonormal Hermite Polynomial Basis Neural Network

... artificial neural networks (ANNs) have emerged as an efficient and powerful computational tool and have been widely used in in pattern recognition, signal processing, system identification, and ...The ...

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Geoscientific Model Development

Geoscientific Model Development

... We validated both neural networks with completely indepen- dent data using a selection of actual satellite ephemerides. These ephemerides and solar wind conditions where not part of the training data set. The ...

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Sla Aware Load Balancing Algorithm Using Join-Idle Queue for Virtual Machines in Cloud Computing

Sla Aware Load Balancing Algorithm Using Join-Idle Queue for Virtual Machines in Cloud Computing

... Mohammad Alhamad,Tharam Dhillon and Elizabeth Chang [3] presents the design in cloud computing. We discussed the strategies of agreement between cloud provider and cloud consumer. This paper proposed the method for the ...

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Dynamic analysis of synchronous machine using neural network based characterization clustering and pattern recognition

Dynamic analysis of synchronous machine using neural network based characterization clustering and pattern recognition

... Dynamic analysis of synchronous machine using neural network Dynamic analysis of synchronous machine using neural network based characterization clustering and pattern recognition.. ba[r] ...

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INTRODUCTION TO ARTIFICIAL NEURAL NETWORKS

INTRODUCTION TO ARTIFICIAL NEURAL NETWORKS

... direct inverse modeling learns the inverse system ...Since inverse model of the plant is in off line trained model, it tacks ...

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A dynamic neural network model for predicting risk of Zika in real time

A dynamic neural network model for predicting risk of Zika in real time

... the model is measured using two metrics: (1) prediction accuracy (ACC) and (2) receiver operating characteristic (ROC) ...quantifying model performance using these two metrics, we evaluate the robustness of ...

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How Does Our Visual System Achieve Shift and Size Invariance?

How Does Our Visual System Achieve Shift and Size Invariance?

... network model called JIM (John and Irv’s model) for recognizing 3D-objects made of simple geometric ...this model is incomplete in that the where-information is not extracted from the input image but ...

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